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["SIPOC"]

{"sheet":"SIPOC","index":1,"n_rows":6,"n_cols":7,"rows_kept":6,"cut":false,"hidden":true,"merged":[[5,0,21,0],[0,0,1,0],[0,3,0,4],[1,3,1,4],[0,6,0,7],[1,6,1,7]]}
["SIPOC","SUPPLIERS","INPUTS","PROCESS",null,"OUTPUTS","CUSTOMERS"]
[null,"Providers of the required resources","Resources required by the process","Top level description of the activity",null,"Deliverables from the process","Anyone who receives a deliverable from the process"]
[null,null,null,"Requirements",null,null,"Requirements"]
[]
[]
["The SIPOC helps you to identify all of the relevant elements of the project - especially the process outputs and the customers of the outputs so the voice of the customer can be captured.  \n\nSIPOC is the starting point of the project it and helps to  quickly capture the 'as is' state of the organisation and processes in question"]

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["Execution Check List & Quadrant Gate Review Guide"]
[null,null,"4Q Project Name:"]
[]
[null,null,"Pre 4Q",null,null,"Q1 \nMeasure",null,null,"Q2 \nAnalyze",null,null,"Q3 \nImprove",null,null,"Q4 \nSustain"]
[]
["1) Checklist can be used during each 4Q project as a reminder of actions needed.  Print out large and have on the wall for progress reference.",null,"q","Develop draft problem statement",null,"q","Form the project team",null,"q","Analyze variation",null,"q","Team creative brainstorm solutions",null,"q","Select control techniques (SPC)"]
[]
[]
[null,null,null,null,null,null,null,null,"q","Analyze waste"]
[]
[null,null,"q","Take immediate containment action",null,"q","SIPOC process map",null,null,null,null,"q","Select optimum solutions",null,"q","Standardize via documentation"]
[null,null,null,null,null,null,null,null,"q","Team brainstorm to identify root causes"]
[]
[null,null,null,null,null,"q","Capture VOC & translate to CTQ"]
[]
[null,null,"q","Identify initial project scope",null,null,null,null,null,null,null,"q","Conduct pilot study (or Risk Analysis)",null,"q","Develop control metrics (KPI's)"]
["2) The Key Question guide can be used when moving between quadrants to ensure robust execution",null,null,null,null,null,null,null,"q","Document root causes on Cause & Effect diagram"]
[]
[null,null,null,null,null,"q","Stakeholder map"]
[]
[null,null,"q","Create business case",null,null,null,null,null,null,null,"q","Verify & validate improvements",null,"q","Disengage old process"]
[null,null,null,null,null,"q","Communication & project plan"]
[]
[null,null,"q","Determine SMART objectives",null,null,null,null,"q","Select top 3-5 root causes"]
[]
["3) Print out the 2 page guide as double sided, short edge (top) binding & fold in thirds.",null,null,null,null,null,null,null,null,null,null,"q","Develop 'to-be' process map",null,"q","Monitor progress"]
[null,null,null,null,null,"q","Develop 'as-is' process map"]
[]
[null,null,"q","Create project charter",null,null,null,null,"q","Validate selected root causes as actual root causes",null,null,null,null,"q","Validate improvements in process performance"]
[]
[null,null,null,null,null,null,null,null,null,null,null,"q","Review stakeholder map & comms plan"]
[null,null,null,null,null,"q","Validate measurement system"]
[]
[null,null,"q","Create and enter project white sheet into SMT"]
[]
[null,null,null,null,null,null,null,null,"q","Project sponsor approval to proceed",null,"q","Implementation business case (cost & benefits & solution plan",null,"q","Share lessons learnt (organization memory, newsletters etc)"]
[null,null,null,null,null,"q","Develop data collection plan"]
[]
[]
[]
[null,null,"q","Project sponsor approval to proceed"]
[null,null,null,null,null,"q","Collect data"]
[null,null,null,null,null,null,null,null,null,null,null,null,null,null,"q","Thank the team"]
[]
[null,null,null,null,null,"q","Calculate baseline process performance",null,null,null,null,"q","Project sponsor, process owner and budget holder approval to proceed"]
[null,null,null,null,null,null,null,null,null,null,null,null,null,null,"q","Celebrate success"]
[]
[]
[null,null,null,null,null,null,null,null,null,null,null,null,null,null,"q","Plan future activities"]
[null,null,null,null,null,"q","Revisit problem statement"]
[]
[null,null,null,null,null,null,null,null,null,null,null,null,null,null,"q","Close project"]
[]
[null,null,null,null,null,null,null,null,null,null,null,"q","Implement actions on solution plan"]
[null,null,null,null,null,"q","Project sponsor approval to proceed"]

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["Key Questions for \n4Q Quadrant Gate Reviews",null,"ABB OPEX 4Q",null,null,"ABB OPEX 4Q",null,null,"ABB OPEX 4Q"]
[]
[null,null,"Pre-4Q                     Quadrant Review",null,null,"Q1 Measure                      Continued",null,null,"Q2 Analyze                       Continued"]
[null,null,null,null,null,null,null,null,3,"Has the team conducted root cause analysis to identify potential causes (Output: fishbone / 5 Whys)?"]
[null,null,"Key Questions",null,null,4,"Has a high level project plan with scheduled reviews been prepared?"]
[]
[]
[null,null,1,"Are your top level organizational / functional issues identified?"]
["2) The Key Question guide can be used when moving between quadrants to ensure robust execution"]
[null,null,null,null,null,5,"Has the 'as-is' process map been developed by the team? Check who was involved in this process."]
[null,null,null,null,null,null,null,null,4,"What improvement targets can we set?  What is the capability in the existing process for 'quick win' improvements?"]
[]
[null,null,2,"Is the project linked to the strategic objectives of the organization?"]
[]
[]
[]
[null,null,null,null,null,6,"What did the team learn from 'walking the process'?"]
["3) Print out the 2 page guide as double sided, short edge (top) binding & fold in thirds.",null,3,"Are potential opportunities and improvement projects identified?",null,null,null,null,5,"Have the root cause(s) been verified?"]
[]
[]
[null,null,null,null,null,null,null,null,6,"Does the problem statement need revisiting based on latest knowledge?"]
[null,null,null,null,null,7,"Are the areas where you need to collect data identified?"]
[null,null,4,"Has the problem statement been developed?"]
[]
[]
[null,null,null,null,null,null,null,null,7,"Are you satisfied with the level of cooperation and support you are getting from other parts of your organization?"]
[null,null,null,null,null,8,"What type of data is available? How has it been collected?"]
[null,null,5,"How does this current problem impact on customers (internal & external), their critical requirements (CTQs) and the organization?"]
[]
[]
[]
[null,null,null,null,null,9,"Has the baseline process capability been established?"]
[null,null,null,null,null,null,null,null,8,"What other actions or activities do you need to accelerate your progress?"]
[]
[]
[]
[null,null,6,"Has the project been scoped by identifying the process boundaries?",null,10,"What are your next steps?"]
[null,null,null,null,null,null,null,null,9,"Has a quadrant review taken place?"]
[]
[null,null,null,null,null,11,"Are you satisfied with the level of cooperation and support you are getting?"]
[null,null,null,null,null,null,null,null,"Q3 Improve               Quadrant Review"]
[null,null,7,"What are the SMART objectives and improvement goals of the project?"]
[]
[]
[null,null,null,null,null,12,"What other actions or activities do you need to accelerate your progress?",null,"Key Questions"]
[]
[null,null,8,"Are the results of these steps summarized in a project charter?"]
[null,null,null,null,null,null,null,null,1,"Has a team brainstorming session been conducted to identify possible improvement actions?"]
[]
[null,null,null,null,null,13,"Has a quadrant review taken place?"]
[]
[null,null,9,"Has a quadrant review taken place?"]
[null,null,null,null,null,"Q2 Analze                   Quadrant Review"]
[]
[null,null,"Q1 Measure         Quadrant Review",null,null,null,null,null,2,"Have decision making tools (e.g. complexity vs. value matrix) been utilized to prioritize the solutions?"]
[]
[null,null,null,null,null,"Key Questions"]
[]
[null,null,1,"Has a team been formulated with the key actors associated with the problem?"]
[null,null,null,null,null,1,"Have the team analyzed the waste in the process by using appropriate Lean techniques?"]
[]
[null,null,null,null,null,null,null,null,3,"Has the future state process map been developed?"]
[]
[null,null,2,"Have the key stakeholders in the project been identified?"]
[]
[]
[null,null,null,null,null,2,"Have the team analyzed the variation in the process using appropriate statistical techniques?",null,4,"Has a pilot study been conducted?"]
[]
[null,null,3,"Has a stakeholder communications plan been prepared?"]
[null,null,null,null,null,null,null,null,5,"If a pilot study is not appropriate (low volume process), has a risk assessment been performed (FMEA)?"]
[]
[]
[]
[]
[]
[null,null,"ABB OPEX 4Q",null,null,"ABB OPEX 4Q"]
[]
[null,null,"Q3 Improve                             Continued",null,null,"Q4 Sustain                                Continued"]
[]
[]
[]
[null,null,6,"Is an implementation business case available (cost / benefit analysis)?",null,7,"If the process owner was not leading the change, has he / she taken ownership of the new process?"]
[]
[]
[]
[]
[null,null,7,"Is an implmentation timing plan available & been agreed with all stakeholders?"]
[]
[null,null,null,null,null,8,"Is the new process in control?"]
[]
[]
[null,null,8,"Has the implementation plan been communicated to the appropriate people?",null,9,"Have lessons learned been captured to create a 'corporate memory'?"]
[]
[]
[]
[]
[null,null,null,null,null,10,"Have lessons learned been communicated to other functions & ABB Business Units?"]
[]
[null,null,9,"Is there an ongoing review process with all stakeholders to monitor implementation?"]
[]
[]
[]
[]
[null,null,null,null,null,11,"What new tools have you learned?"]
[]
[null,null,10,"Are you satisfied with the level of cooperation and support you are getting from other parts of your organization?"]
[null,null,null,null,null,12,"Are you satisfied with the level of cooperation and support you received?  What should we do differently to support the next project?"]
[]
[]
[]
[]
[]
[null,null,11,"What other actions or activities do you need to accelerate your progress?"]
[]
[]
[null,null,null,null,null,13,"Do you have any ideas for follow up projects?"]
[]
[null,null,12,"Has a milestone review taken place?"]
[]
[]
[null,null,"Q4 Sustain    Quadrant Review",null,null,14,"Have you thanked the team and celebrated the success?"]
[]
[]
[]
[null,null,"Key Questions"]
[]
[]
[null,null,1,"Have control techniques been applied (SPC, Visual management etc)?",null,"Further Information"]
[]
[]
[]
[null,null,null,null,null,"References"]
[null,null,2,"How will the process be monitored and controlled (KPIs / Charts etc)?"]
[]
[null,null,null,null,null,"ABB OPEX program:"]
[]
[]
[null,null,3,"Has baseline vs. improved performance been evaluated?",null,"Intranet Access: http://inside.abb.com/gf-go"]
[]
[]
[null,null,null,null,null,"Program Management Office:"]
[]
[null,null,4,"Have the old processes been disengaged and removed?"]
[null,null,null,null,null,"Pekka Raudaskoski"]
[]
[]
[null,null,null,null,null,"Alex Danci"]
[null,null,5,"Have all process changes been documented (SOPs, flowcharts)?"]

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["Project Charter"]
[]
[null,null,"4Q Project Charter"]
[]
[]
[null,null,"Project","(Name of the Project)"]
[null,null,"Div","(Name of the Division)"]
[null,null,"BU","(Name of the Business Unit)"]
[null,null,"Project Site","(Location Address)"]
[]
[null,null,"Problem description","Briefly describe the problem(s) the team is about to solve"]
[]
[null,null,"Problem goal","Briefly state what results the project is going to deliver"]
[null,null,"Major Assumption (optional)","Specify if there are any major assumptions.\neg. Production volume is 100 pcs/day"]
[]
[null,null,"Requirement (optional)","Specify if there are any requirements.\neg.All data should be retrieved from SAP system only"]
[]
[null,null,"Budget\n(optional)","Please specify the budget estimate for the project"]
[]
[null,null,"Schedule","Start date:",null,null,null,"End date:"]
[]
[null,null,"Project Team"]
[null,null,"Role","Name",null,null,null,"Title / Department"]
[null,null,"Project Sponsor"]
[null,null,"Project Leader"]
[null,null,"Team Members"]

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["Jump to:"]
["Key Word","Description"]
["#","Number"]
["%Contribution","See Contribution."]
["%Failure","A measure of un-reliability. See also Failure."]
["%GRR","Measurement system study variation (sMS) divided by total variation (sTOTAL). \nAssesses data relative to total sample data spread irrespective of spec limits.\nRange: <10% = good, 10-20% = Acceptable, 20-30% = Marginal. \nAlso known as: P/TV, %R&R, %Study Tolerance, %SV, Study Variation, Measruement Capability Index."]
["%R&R","Measurement system study variation (sMS) divided by total variation (sTOTAL). \nAssesses data relative to total sample data spread irrespective of spec limits.\nRange: <10% = good, 10-20% = Acceptable, 20-30% = Marginal. \nAlso known as: P/TV, %R&R, %Study Tolerance, %SV, Study Variation, Measurement Capability Index."]
["%Study Tolerance","Measurement system study variation (sMS) divided by total variation (sTOTAL). \nAssesses data relative to total sample data spread irrespective of spec limits.\nRange: <10% = good, 10-20% = Acceptable, 20-30% = Marginal. \nAlso known as: P/TV, %R&R, %Study Tolerance, %SV, Study Variation, Measruement Capability Index."]
["%SV","Measurement system study variation (sMS) divided by total variation (sTOTAL). \nAssesses data relative to total sample data spread irrespective of spec limits.\nRange: <10% = good, 10-20% = Acceptable, 20-30% = Marginal. \nAlso known as: P/TV, %R&R, %Study Tolerance, %SV, Study Variation, Measruement Capability Index."]
["%Tolerance","Measurement System variation (5.15*sMS) divided by Tolerance. \nAlso known as: %Tolerance, P/T, SV/Toler, Tolerance Ratio."]
["%Variation","Type 1 Gauge R & R capability. Data range divided by tolerance.\n%Variation = 6*Sgauge / TotalTolerance. For acceptance %Variation must be <=15%.\n%Variation in a Type 1 study is equivalent to %Tolerance in a Type 2 study.\nsee also Study Variation, Total Variation, Random Variation, Assignable Variation."]
["*","1. Multiplied by\n2. An outlier on a box plot."]
["**","To the power of. E.g. 3**2 means 3 squared. Alternative nomenclature: ^ (Caret), or superscript e.g. x2"]
["/","Divide by"]
["^","1. The Caret accent on a letter\n2. The circumflex accent on a letter e.g. ô\n3. The Mode. E.g. x with a caret represents the Mode of the x values\n4. To the power of. E.g. 3^2 means 3 squared. Alternative nomenclature: **, or superscript e.g. x**2 or x2"]
["`","1. Bar or Overscore character\n2. The Mean. E.g. X-Bar, R-Bar. The mean of those values.\nSee X-Bar, T-Bar, L-Bar."]
["`L","See L-Bar."]
["`T","See T-Bar."]
["`X","See X-Bar."]
["|","Pipe. Symbol used in Minitab to indicate all permutaions of factors"]
["~","1. The Tilde accent on a letter\n2. The Median. E.g. x with a tilde represents the Median of the x values"]
["1 Sample Sign Test","Non-parametric test equivalent to 1 Sample t-Test. Used in lieu of Wilcoxon test if distribution is non-symmetrical."]
["1 Sample t-test ","A t-Test comparing one sample with a fixed value. If data is non-normal use 1 Sample Sign Test or 1 Sample Wilcoxon Test."]
["1 sample Wilcoxon Test","Non-parametric test equivalent to 1 Sample t-Test. Used when data is symmetrical. For non-symmetrical data use the 1 Sample Sign Test."]
["14 Management Principles","See Demings 14 Management Principles."]
["1P","Person/people - See 5M's & a P."]
["2 Sample t-test ","A t-Test comparing two samples of data. If data is non-normal use Mann-Whitney Test."]
["2 Variances","A Hypothesis Test for assessing homogeneity of the variances of 2 samples."]
["2K Factorials","A DOE where each factor has only two levels. This enables rapid early assessment of a large number of factors with relatively few runs. May be used to conribute to further more complex study. See also: Full Factorials, Fractional Factorials, Standard Order. \nNomenclature: 2 = # Levels, K = # Factors.\nStep 1:  State the Practical Problem     Step 2:  State the factors and levels of interest.\nStep 3:  Select the appropriate sample size based on the effect you are trying to detect.  Stat>Power&SampleSize>2Level\nStep 4:  Create the Minitab experimental data sheet with the factors in their respective columns. Randomize the experimental runs in the data sheet.  Conduct the experiment. Stat > DOE > Factorial> Create Factorial Design\nStep 5:  Construct  the ANOVA table for the full model: Stat>DOE>Factorial>Analyze-Graphs>EffectsPlots(normal/Pareto)\nStep 6:  Review  the ANOVA table and eliminate effects with p-values above .05.  Remove these one at a time and then run the reduced  model and plot the residual graphs for further analysis Stat>DOE>Factorial>Analyze-Graphs>EffectsPlots-Graphs>ResidualsPlots\nStep 7:  Analyze the residual plots to ensure we have a model that fits \nStep 8:  Investigate significant interactions (p-value < .05).  Assess the significance of the highest order interactions first.\nFor 3-way interactions unstack the data and analyze. Stat > DOE > Factorial> Factorial Plots > Interaction Plot\nOnce the highest order interactions are interpreted, analyze the next set of lower order interactions.\nStep 9:  Investigate the main effect of significant factors (p-value < .05) whose settings have not been determined by interaction analysis. \nStat > DOE > Factorial> Factorial Plots > Main Effects Plot & Cube Plots\nStep 10:  State the mathematical model obtained.  If possible calculate the epsilon squared  and determine the practical significance.\nStep 11:  Translate the mathematical model into process terms and formulate conclusions and recommendations.\nStep 12:  Replicate optimum conditions.  Plan the next experiment or institutionalize the change.\n"]
["2R's","Random & Representative sampling"]
["3.0SL","The Upper 3 Sigma Limit. A former MiniTab nomenclature for Upper Control Limit."]
["-3.0SL","The Lower 3 Sigma Limit. A former MiniTab nomenclature for Lower Control Limit."]
["4M's an E & a P","Another rendering of 5M's and a P."]
["5.15*Sigma","Equivalent to range covering 99% of variance. See Gage R & R"]
["5M's and a P","Machine (Equipment), Method (Procedures), Measurement system, Materials, Mother Nature (Environment), People. \nSame as Fish Bone or Cause & Effect diagram. See also MEPEM."]
["5S","Sorting, Storage, Shining, Standardise, Sustain. “5S Standards are the foundation that supports all the Phases of Lean Manufacturing.”  The system can only be as strong as the foundation it is built on. The foundation of a production system is a CLEAN and SAFE work environment.  It’s strength is contingent upon the employees/company committed to maintaining it. Derived from TPS Japanese:\nJapanese  Literal Translation    English Equivalent\nSeiri         Clearing Up             Sorting\nSeiton      Organizing              Storage\nSeiso       Cleaning                 Shining\nSeketsu   Standardizing          Standardize\nShitsuke  Training & Discipline Sustaining\nSee Sorting, Storage, Shining, Standardise, Sustain.\nSee also Lean Manufacturing=5S>Visual Factory>Standardized Work>Kaizen>KanBan."]
["5Y's","Ask the question \"Why?\" five times to peel the onion of causes down to root causes to address. See also Stair Stepping."]
["6 Pack","See Capability Six Pack"]
["6-Sigma","1. A problem solving methodology - To ensure sources of variation in manufacturing and business processes are objectively identified, quantified and controlled or eliminated.\n2. Process Sigma Level 6 - I.E. 6 Std Deviations between mean & nearest spec limit. Equivalent to 3.4 DPMO = \"Pretty Darn Good\" Mike Cifaratta"]
["7 Pack","The full set of 7 QS-9000 standards and 2 supplements: QS-9000, QS-9000 QSA, APQP, PPAP, MSA, FMEA, SPC and TE Supplement with its' QSA-TE. Also known as the 9 Pack!"]
["7 Sins of Muda","Elimination of Waste. Muda = Japanese for Waste. See waste for the 7 areas of waste."]
["7-Step Method","Process Mapping methodology:\n1) Define the scope of the process the team needs to map.  Start & Stop\n2) Document all tasks or operations needed in the production of a “good” product or service \n3) Document each task or operation above as value add or non value add. \n4) List both internal and external Y's at each process step.\n5) List both internal and external X's at each process step.\n6) Classify all X's as one or more of : Controllable (C), Standard Operating Procedures (SOP), Noise (N)\n7) Clearly identify all data collection points."]
["80:20","See Pareto Diagram"]
["9 Pack","The full set of 7 QS-9000 standards and 2 supplements: QS-9000, QS-9000 QSA, APQP, PPAP, MSA, FMEA, SPC and TE Supplement with its' QSA-TE. Also known as the QS-9000 7 Pack!"]
["a","Greek letter Alpha. See Alpha value, Alpha Risk"]
["a Risk","See ALPHA RISK."]
["ABC Flowcharter","Previous version of MicroGrafx iGrafx process mapping software"]
["ABSCISSA","The horizontal axis of a graph. See also Ordinate"]
["Accelerated Testing","Methods to accumulate large volumes of test data intended tp represent RWUP over a short period of time. It can be challenging to correlate accelerated conditions to RWUP."]
["ACCEPTANCE REGION","The region of values for which the null hypothesis is accepted;"]
["Accuracy","Closeness of Mean to target. Measured as a bias from a true value. Improved through Calibration to remove measurement system bias. See also Precision."]
["Actual Process Capability","Same as Long Term Process Capability. \nAlso known as  Between Capability, Overall Capability."]
["AD*","Anderson-Darling test value of conformity of data to a distribution. The lower the A-Squared value the closer the conformity. Same as A-Squared value. See also P-Value."]
["Alias","The Alias structure identifies those main effects & interactions that are confounded in order to reduce the no of runs in a Fractional Factorial DOE. Same as Confounding."]
["Alpha","Greek Letter a."]
["Alpha Level","1-(%Confidence). Nomenclature: a. E.g. a = 0.05 for 95% confidence. See also P-Value"]
["Alpha Risk","The probability of accepting the alternate hypothesis when, in reality, the null hypothesis is true. See also Beta Risk"]
["Alternative Hypothesis","A tentative explanation which indicates that an event does not follow a chance distribution; What we want to prove/disprove - the contrast to the null hypothesis.\nNomenclature: HA."]
["Analyse","The Analyse phase of a DMAIC 6-Sigma project. This includes: Micro process map, quantify KPIV's & improvement opportunities, root cause andalysis & performance objectives."]
["Analyse Phase","Identify vital few X’s:\nEstablish baseline capability for key output variables (y’s) \nTo identify and manage high risk input variables (x’s) (e.g. FMEA)\nTo reduce the number of process input variables (x’s) to a manageable number of critical variables (e.g. hypothesis testing, multi-vari studies and ANOVA techniques)\nIdentify potential sources of variation\nTo plan and document initial improvement activities"]
["Analysis of Means","Analysis Of Means. Test to see if the means of two data samples are from the same population. Nomenclature: ANOM. See also ANOVA - Analysis of Variance."]
["Analysis of Variance","Test to see if the variances of two data samples are from the same population. See  ANOVA. See also ANOM."]
["And/Or Diagram","An element of Structured Inventive Thinking (SIT). Part of the Particles Method."]
["And/Or Tree","An element of Structured Inventive Thinking (SIT). Part of the Particles Method."]
["Anderson-Darling","Test of conforminty of data to a statistical distribution e.g. Test for Normality or other goodness of fit. Expressed as A-Squared or AD* value or as P-Value (0-1)."]
["ANOM","Analysis Of Means. Test to see if the means of two data samples are from the same population. See also ANOVA - Analysis of Variance."]
["ANOVA","Analysis of Variance studies - A wide range of statistical tools including support for Gage R&R and DOE. Steps include Main Effcts graphical representation, Normality & equal variance determination to support 1 and 2 way  ANOVA analysis of means. \nSteps: Test normality; Test for Equal Variances, ANOVA (n-way according to no of factors), Assess P-Value for H0, assess e2 for extent to which variation is explained by model/factors. Handles multiple sample sets. \nSee also Gage R & R, P/TV, ANOM, MANOVA. \nIf data is non-normal use Kruskal-Wallis or Moods Median Test."]
["APEL","Assessment for Prior Learning & Experience"]
["Arbitrary Censoring","In Reliability Testing: allowing for the intervals at which failure readings are taken . \nSee also Right Censoring."]
["ARR","Analytical Reliability & Robustness. \nSee also enCORE, SLAM, Auto-SLAM, QMC, DACE, EMARS."]
["Array","A matrix of values. See Balanced Array, Geometric Array, Non-Geometric Array, Inner Array, Outer Array, L8, L12."]
["ASQC","American Society for Quality Control"]
["A-Squared","Anderson-Darling test value of conformity of data to a distribution. The lower the A-Squared value the closer the conformity. See also P-Value."]
["Assessor Variation","Assessor Variation in a Gage R&R study. Same as Reproducability.\nSee also Equipment Variation."]
["Assignable Cause","A source of variation which is non-random; a change in the source (“Vital Few” variables) will produce a significant change of some magnitude in the response (dependent variable), e.g., a correlation exists; the change may be due to an intermittent in-phase effect or a constant cause system which may or may not be highly predictable; an assignable cause is often signalled by an excessive number of data points outside a control limit and/or a non-random pattern within the control limits; an unnatural source of variation; most often economical to eliminate. May be identifiable and correctable by the operator. Also known as Special Cause. See also Common / Random Cause."]
["ASSIGNABLE VARIATIONS","Variations in data which can be attributed to specific causes. See also Random Variation"]
["Attribute Control Charts","Control Charts for attribute data. For constant sample size see: \nnp Chart (# Defective Units), c Chart (# Defects/Non-conformities), \nFor constant sample size see: \np Chart (Proportion Defective), u Chart (Proportion Defects/Non-conformities)."]
["ATTRIBUTE DATA","Numerical information at the nominal level; subdivision is not conceptually meaningful; data which represents the frequency of occurrence within some discrete category, e.g., 42 solder shorts."]
["Attribute R & R","The process for measuring the repeatability & reproducability of the value of an attribute"]
["AV","Assessor Variation in a Gage R&R study. Same as Reproducability.\nSee also Equipment Variation."]
["Availability","Proportion of total time that item is available for use.\nAvailability = MTBF / (MTBF + MTTR).\nSee also OEE, Performance Efficiency, Quality Yield."]
["b","1. The Greek Letter Beta\n2. Beta Risk\n3. The Shape of the Weibull Distribution.\n4. The Intercept (b0) The stress (b1) Coefficients of a Probit Analysis.\n5. The slope of the Ideal Function: y=bM"]
["B Life","Time at which x% of parts are expected to have failed. E.g. B5 Life = time at which 5% of parts have failed. Nomenclature originates in the Bearings industry and related to Bearing life expectancy."]
["b Risk","See BETA RISK."]
["B/S","Batch Sample"]
["B10 Life","Time at which 10% of parts are expected to have failed"]
["B5 Life","Time at which 5% of parts are expected to have failed"]
["Back To Mark","A bolt torque measuring method by detorquing \"Back to Mark\":\nMark the current position on the bolt and surround. Loosen bolt. Retighten \"back to mark\"\nRecord torque needed to get back to original position. This is the most informative detorque reading. See also Crack On."]
["BACKGROUND VARIABLES","Variables which are of no experimental interest and are not held constant.  Their effects are often assumed insignificant or negligible, or they are randomized to ensure that contamination of the primary response does not occur."]
["Balanced ANOVA","ANOVA on two columns of data of equal length. Where data is missing use GLM."]
["Balanced Array","For use in an Experiment the array of factors and levels must be Balanced. To satisfy this there must be: \n1. Equal numbers of levels in each column\n2. The Sum Product of each pair of columns equals zero (Taking levels as \"+\" & \"-\").\nBalanced arrays may be Geometric or Non-Geometric Arrays."]
["Balanced data","A fully matching set with equal numbers of data values for all permutations of all factors. \nSee also Cross Tabulation, Tally."]
["Baldrige","The Baldrige National Quality Program and the Malcolm Baldrige National Quality Award of the US National Institute of Standards and Technology (NIST). An endeavour to promote performance excellence among U.S. manufacturers, service companies, educational institutions, and health care providers; conducts outreach programs and recognizes performance excellence and quality achievement;"]
["Bartlett","A form of f-Test for > 2 samples of data. Used for showing equal variances for normal data. Use Levene for non-normal data."]
["Baseline","The initial or starting value e.g. of the Process Capability or DPMO. A commonly used term in American English, less common in general usage in British English."]
["Baseline Capability","Starting Process capability. Before process improvements are made. Same as Zbench. May bes expressed as Short or long term capability. \nSee also Final Capability, Baseline DPMO."]
["Baseline DPMO","The initial or starting value of the process DPMO. Also known as Initial DPMO. \nSee DPMO. See also Baseline Capability."]
["Basic Quality","Musts: CTQ's which must be satisfied. Part of the Kano Model. See also Performance Quality, Excitement Quality."]
["Bathtub Curve","The sum of all reliability failures over time. Consists of the effects of Early life failures, Usefule life failures and Wear out failures."]
["Bayes Analysis","Conducting a reliability data analysis using an assumed shape and/or scale."]
["Bench","See ZBench. Same as Short Term Sigma Level & ZST"]
["Benchmarking","Study & Indetification of best in class process elements across vehicle lines, competotors, related processes and other industry and research sources so as to maximise variability reduction and quality improvement. See also BIC, CAB Events."]
["Beta","Greek Letter b."]
["Beta Risk","The probability of accepting the null hypothesis when, in reality, the alternate hypothesis is true. Beta risk is the converse of Power. See also Alpha Risk."]
["Between Capability","Process Capability between subgroups or samples. \nThe additional component of variability, which added to the Within/Potential/Short Term Process Capability determines Overall/Long Term Process Capability."]
["Bias","Distance between True Value and the observed value. The measure of Accuracy. See also Linearity, Mallow's C-p Value."]
["BIC","Best In Class - The highest quality level achievable in this class of process across current industry achievements. See also Benchmarking"]
["Big 7","Safety and 7 other key areas of Failure Effects in a PFMEA:- Breakdown, Setup & Adjustment, Idling/minor stoppages, Reduced Cycle, Start Up, Defective Parts, Tooling."]
["Bimodal","A statistical distribution which has two dominant peaks. This is visible on the baisic stats histogram graph. There is no commonly available standard test to reflect the significance of potential bimodal trends. See also Uni-Modal"]
["Binomial Distribution","The distribution of a Binomial Response variable. I.e. one which has two possible outcomes: success & failure"]
["Binomial Response Variable","A binomial response variable is an outcome variable that has only two possible outcomes. These outcomes are typically defined as a success or a failure. The distribution, or pattern, of these outcomes follows a binomial distribution."]
["Black Belt","A certified full time practicioner of 6-Sigma problem solving methodologies. Will deliver successful projects using the Breakthrough Strategy. Will train and mentor the local organization on Six Sigma. Mentored by a Master Black Belt. May train and/or be supported by Green Belts"]
["Block","See Blocking Variables."]
["Blocking Variables","A relatively homogenous set of conditions within which different conditions of the primary variables are compared.  Used to ensure that background variables do not contaminate the evaluation of primary variables.\nA Blocking Variable is a factor in an experiment that may have an undesired influence as a source of variability is called a “block.  A block can be “lots” of material, operators, days, etc…, which are likely to produce experimental runs that are more homogenous within the block than between the blocks.”\nA blocking variable is generally one which is not thought to be a Critical Few X, but which may be introducing noise which we want to spread across the sampling rather than having it confound (mask) variation in a specific Critical X.\nThe blocking factor is placed into the DOE in place of the highest level interaction which is usually insignificant. e.g. Put the blocking value in place of ABCD and do not then look for ABCD in the DOE.\nSee also: Confounding."]
["Bogey Testing","Gather data by testing to a set no. of cycles / duration. Measure state at end of test period. A low cost data collection method gleaning limited data. Nomenclature said to originate from Golf where Bogey is one over par - maybe representing testing to one measure beyond specified life expectancy."]
["Bonus Parts","Surplus parts dropped of left in vehicle e.g. washers in door linings, potentially causing S&R or other TGW"]
["Boundary","See Process Boundary"]
["Boundary Diagram","A functional block diagram showing a procust/process and it's interfaces to: Assembly, Use, Service and other parts, subsystems and systems. Interfaces are described. A dotted line is then drawn around those elements and interfaces which are incluided in scope. Part of the FMEA process. See also Interface Matrix & P Diagram."]
["Box Cox Transformation","Means of transforming non-normal data to normal. Uses the equation W=Yl.\nE.g. W=Y2,  W=Y-1i.e.1/Y.  W=Y0.5 i.e.sq rt.,  W=Y0=Ln(Y)\nAn alternative to transforming the data is to try the Weibull distribution."]
["Box Plot","A graphical representation of the spread of data like:    --[.|]-- *\nConsists of whiskers at each end, being the initial & final 25% of the data. \nThe box shows the 1st & 3rd quartiles of the data.\nThe line within the box is the median.\nThe mean may be shown as a dot within the box.\nOutliers beyond 1.5 * IQR are shown by asterix (*)."]
["Box-Behnken","A form of Response Surface Method. See RSM."]
["Bq Life","Time at which q% of parts are expected to have failed. E.g. B5 Life = time at which 5% of parts have failed. "]
["C & E Chart","Ambiguous reference to C & E Diagram or C & E Matrix"]
["C & E Diagram","Cause & Effect diagram - Same as  Fish Bone diagram "]
["C & E Matrix","Cause & Effect matrix - Same as CT Matrix"]
["c Chart","An attribute Run Chart for measuring Defects. A simple chart used to track the number of defects sampled (not the percent defective) assuming the sample size is constant. Requires a constant inspection sample size. See also u Chart."]
["C CHARTS","Charts which display the number of defects per sample."]
["c2","See Chi Square."]
["c2  ","May appear in place of c2 if the fonts have not been set right! Change the font of the 'c' to Symbol Font and apply Font Superscript to the '2' to get c2. See Chi-Square "]
["Calibration","Processes to ensure the Accuracy of a tool or gauge."]
["CAP","1. Change Acceleration Process - GE's change management methodology. Helps speed & sustain change.\n2. Cause Action Prevention/Profit (VRT's reaction to out of control situation)"]
["Capability","See Process capability. See also Sigma Level, Capability Assessment"]
["Capability Assessment","Assessing capability to predict true quality levels for products. A guide to the product or process sigma level. See Process Capability.\nSee also: Cp, CPK, Pp, Ppk."]
["Capability Index","Same as Long Term Process Capability. \nAlso known as  Between Capability, Overall Capability."]
["Capability Six Pack","A combined process capability report including: X-Bar & R Chart, Last 25 Subgroups, Capability Histogram, Normal probability plot and process tolerance capability plot."]
["Cascading","Breaking down high level factors to lower level factors (little x's)"]
["Categorical Variable","A random variable whose value will be one of a limited number of categories in some specified variable.e.g. Day number, Shift, Operator number. As opposed to a Continuous Variable. May be used interchangeably with Discrete Variable"]
["Category","See Distinct Category, Categorial Variable, Cat 'A', Cat '1'/'2'/'3'."]
["CAUSALITY","The principle that every change implies the operation of a cause. See X's"]
["CAUSATIVE","Effective as a cause. See X's"]
["Cause","That which produces an effect or brings about a change. An independant variable. See X's"]
["Cause & Effect Diagram","A tool used to identify & organise possible causes of a problem. Same as fish bone diagram. Also known as Ishikawa, Fish or Herring Bone diagram or (in FMEA) Cause & Mode Diagram, \"5M's and a P\", MEPEM. See also X's, Y's"]
["Cause & Effect Matrix","A Simple QFD (Quality Function Deployment) matrix to emphasize the importance of understanding the customer requirements. Method: \n1. List the output variables (Y's) along the top section of the matrix.  These are outputs which the team and / or the customer deem to be important.  These may be a subset of the list of  Y's identified on the process map. 2.  Rank each output numerically using an arbitrary scale (possibly 1-10).  The most important output receives the highest number. 3.  Identify all potential inputs or causes (X's) that can impact the various Y's and list these along the left hand side of the matrix. 4.  Numerically rate (correlate) the effect of each X on each Y within the body of the matrix.  This is based on the experience of the team. 5.  Use the totals column to analyze and prioritize where to focus your effort when creating the preliminary FMEA.  \nNB: LH Column contains all potential causes. Top Rows contains all Customer CTQ's - not just the TGW and Warranty Claim headings; and their importance to the customer. Matrix elements are correlation measures (0-10) between the causes & the CTQ's."]
["Cause & Mode Diagram","The FMEA version of a Cause & Effect or Fish Bone Diagram. In FMEA the granularity of the analysis matches Failure Causes to Failure Modes. Only via them is the link established to Failure Effects."]
["CC","See Critical Characteristic."]
["Cd","Coefficient of Drag"]
["CDF","See Cumulative Distribtion Function."]
["Censoring","In Reliability Testing: allowing for the effects of parts that have survived the test time - See Right Censoring; and allowing for the intervals at which failure readings are taken (Arbitrary Censoring)."]
["Center Points","Centre points, midway between the Low and High values of a two level factor. This can be done to reduce the number of experimental runs needed by adding extra levels. \nThere will only ever be one centre point - being the mid point of all factors. Reference to 'Number of Center Points' means number of repetitions of the same centre point.\nE.g. 3 factor 2 levels plus a center point needs 9 runs (2*2*2 + 1); \n3 factor 3 levels needs 27 runs (3*3*3)"]
["Central Composite Design","A common DOE matrix used to establish as valid second order model. Formed by adding Star Points to a standard Cube/Square+Centre Point design.\n"]
["Central Limit Theorem","Technique to reduce sample variation by processing the means of set of samples. \nReduces variation by 1 / Sq Rt n where n is the number of samples in each agregated set.\nMethod: Add sub-group no col; Unstack using sub-group as subscript; Calc row means from unstacked data."]
["CENTRAL TENDENCY","Numerical average, e.g., mean, median, and mode; centre line on a statistical process control chart."]
["CENTRE LINE","The line on a statistical process control chart which represents the characteristic’s central tendency."]
["Centre Points","See Center Points."]
["CFMEA","Concept FMEA. Application of FMEA to solution independent concept design . See also DFMEA, PFMEA, MFMEA."]
["Cg","Capability of Gauge. Type 1 Gauge R & R capability.\nCg = TotalTolerance*0.15 / 6*Sgauge.\n%Variation = 15 / Cg. For acceptance %Variation must be <=15%."]
["Change Management","Stages in the management of intruducing change form a Transition Curve including: Shock, Denial, Awareness, Acceptance, Testing, Search for Meaning, Integration."]
["Character Graph","A Minitab function to produce graphs for text only output devices by plotting the required graph using only text characters instead of lines/vectors etc. Very useful if you only have a teletype for output. Not much use now since these became obsolete around 1980!"]
["Characterise","See Characterize."]
["CHARACTERISTIC","A definable or measurable feature of a process, product, or variable."]
["Characterize","1. The Measure & Analyse phases of a 6-Sigma DMAIC project. Focuses on Y's - outputs. \n2. The Characterize of a DfSS DCOV 6-Sigma project. In this phase we:\nFlow CTS Y’s down to lower level y’s, e.g., Y -> y1, y2, … yn\nRelate y’s to CTQ parameters (x’s and n’s), y = f(x1, … xk, n1, … nj)\nCharacterize robustness opportunities (including high mileage opportunities)\nSee also Optimize."]
["Check Sheets","Forms used to collect data by making tally marks to indicate # occurrences"]
["Chi","Greek Letter c. See Chi-Square for Chi distribution and values. See Chi-Square Test for Chi-Squared Tests."]
["Chi-Square","1. A skewed probability distribution and hypothesis tests using this.\n2. Chi-Square Test for goodness of fit / strength of relationship between discrete variables. \n3. ChiSquare Test for Variances\n4. Chi-Square values (from stats tables) are also used in calculating Confidence Intervals. \nCalculation: Chi-Sq = (Expected - Observed)^2 / Expected\nNomenclature: c2. See Proportion Test"]
["Chi-Square Test ","1. Test for goodness of fit between two discrete variables\n\n2. Statistical Test for variance of a process to target variance.\nH0: s1=st. HA: s1¹st. \nIf target s falls between confidence intervals for s then fail to reject H0."]
["CI","Confidence Interval e.g. 95% CI"]
["CL","Control Limit. See Upper & Lower Control Limits, UCL & LCL."]
["Closed World Diagram","An element of Structured Inventive Thinking (SIT). Part of the Closed World Method."]
["Closed World Method","An element of Structured Inventive Thinking (SIT). Use of Closed-World Diagram & Qualitative Change graphs to gain understanding of the system e.g. to identify conflicts to be resolved. Used before applying the SIT solution techniques of Uniqueness, Dimensionality, Pluralization & Redistribution. Used in preference to the Particles Method where there is a known solution technology to improve rather than a clean sheet approach."]
["CLT","See Central Limit Theorem"]
["Cluster Sample","Sampling by diving the population into arbitrary cluster groups and drawing a sample from each group. I.e. where no correlation/dependance is expected between variables and cluster."]
["Coded","Arbitrary -1 and +1 values to represent Low and High levels for a factor e.g. in a 2K Factorials DOE. In contrast to Uncoded real life actual values."]
["Collinearity","Collinearity means that within the set of input variables, some of the variables are (nearly) totally predicted by other variables. When input variables are correlated in this way, there are problems in estimating regression coefficients. Often referred to as Multicollinearity. See also Variance Inflation Factor."]
["COMMON CAUSE","See RANDOM CAUSE. Variation within process capoability. Not controllable by the operators."]
["Complexity","“Complexity” is a measure of how complicated a particular good or service is.  Increased process complexity increases opportunities for defects. May be represented by the “Opportunity Count”."]
["Compounding","Combining a number of factors / variables into a single measure. See broader use under Confounding."]
["Concept To Customer","See CTC."]
["Concern Code","See CCC"]
["Condition Code","See CC Code"]
["Confidence Interval","Reflects the range into which you can expect the mean response to fall. \nPI = confidence that a point will fall in the range. \nCI = confednce the mean will fall in the range. \nSee ConfidenceLevel and Confidence Limit.See also Prediction Interval"]
["CONFIDENCE LEVEL","The confidence / probability that a random variable x lies within a defined interval."]
["CONFIDENCE LIMITS","The two values that define the confidence level."]
["Confounding","Allowing two or more variables to vary together so that it is impossible to separate their unique effects. If an experiment does not vary A & B separately then the effects of the 2 factors cannot be distinguished. Blocking may be used to separate these effects where one is expected to be a critical X and one appears to be noise. The main effects & interactions can be confounded in order to reduce the no of runs in a Fractional Factorial DOE. Same as Alias structure. See also Blocking Variables, Compounding."]
["CONSUMERS RISK","Probability of accepting a lot when, in fact, the lot should have been rejected (see BETA RISK)."]
["Contingency Table","a two-dimensional table constructed for classifying count data, the purpose of which is to determine whether two variables are dependent (or contingent) on each other."]
["CONTINUOUS DATA","Numerical information a the interval of ratio level; subdivision is conceptually meaningful; can assume any number within an interval, e.g., 14.652 amps."]
["Continuous Data","Data from continuous measurement e.g. temperature. See M-07 Introduction to Data.ppt for selecvting statistical techniques according to Discrete/Continuous nature of input & output attributes & variables. See also Discrete Data, Attribute"]
["CONTINUOUS RANDOM VARIABLE","A random variable which can assume any value continuously in some specified variable."]
["Contribution","Contribution (proportion) of variance attributable to one factor. Often refers to  Gage Contribution. Minitab field: %Contribution. AIAG standards require: \n<1% = Ideal, 1-4%=Acceptable, 4-9%=Marginal, >=10%=Unacceptable."]
["Control","1. See Levels of Control, Control Chart, -Limit, -Phase, -Plan, -Specifications, Controllable Variable.\n2. The Control phase of a DMAIC 6-Sigma project. Where sustainability is built in to the process to ensure that improvement benefits are sustained. This includes: mistake proofing, control metrics (SPC/SPM), long term MSA, update SOP's & validation of control."]
["Control Chart","A graphical rendition of a characteristic’s performance across time in relation to its natural limits and central tendency. Records measurements / counts and highlights variation within and outside Control Limits (not Spec Limits).\nA Run Chart with Centre Line, USL, LSL. Quickly detects specific types of out of control process changes. Statistical Process Control Control Plan specifies data collection on a Control Chart which monitors data in control within Control Limits. Out of control data triggers a Reaction Plan."]
["Control Limit","Upper & lower statistical data limits (UCL & LCL). Always calculated from the data  - typically (always) set at +/- 3 Std Deviations (c.99.73%). Measure of whether the process is in control. See Upper & Lower Control Limit. A process is In Control if its' data falls cosistently within its Control Limits. This does not imply the product is within customer specification - it can by In Control but off target and out of spec - See Spec Limit. \nA process is Out Of Control if data falls outside the Control Limits. "]
["Control Phase","Optimize, eliminate, automate, and / or control vital few inputs\nDocument and implement the control plan\nSustain the gains identified\nRe-establish and monitor long-term delivered capability\nImplement continuous improvement efforts (Green Belts at the functional area)"]
["Control Plan","A plan to control CTQ's on an ongoing basis. Defines the Who/Where/When… control methods for each process step. Statistical Process Control Plan specifies data collection on a Control Chart which monitors data in control within Control Limits. Out of control data triggers a Reaction Plan."]
["CONTROL SPECIFICATIONS","Specifications called for by the product being manufactured."]
["Controllable","Process inputs that you can adjust or control while the process is running (e.g. speed, feed rate, temperature, pressure, etc) "]
["Controllable Variable","Controlled or Controllable Variables: Inputs that you can adjust or control while the process is running (i.e...., speed, feed rate, temperature, pressure, etc). See X's"]
["COP","Conformity Of Production - Tests applied to verify the extent to which goods produced and quality assured do actually meet production specifications"]
["COPQ","Cost Of Poor Quality \n- Cost of not being RFT = VCSI plus the hidden:\n   - Cost of failing to meet customer expectations the first time \n   - Opportunity for increased efficiency\n   - Potential for higher profits\n   - Loss in market share\n   - Increase in production cycle time\n   - Labour associated with ordering replacement material\n   - Costs associated with disposing of defects \n- \"A one sigma improvement will result in 20% margin improvement, 12-18% capacity increase, 12% reduction in or redeployment of employees, and 10-30% capital reduction.\""]
["Corner Point","As in a Cube Plot: No Factors * Levels. Part of DOE sample size. See also Effect."]
["Correlation","A measure of association between two quantitative variables. \nCorrelation measures  the degree of linearity between two variables assumed to be completely independent of each other. Correlation coefficient, r, always lies between -1 and +1. May be for linear, quadratic or cubic relationship. Calculated by regression methods e.g. Pearson. Nomenclature: rxy R-Value, R2-Value (R-Sq), R-Sq(adj). \nRange of R-Values: -1 = negative correlation, 0 = no correlation, 1 = high correlation\nE.g. |/ shows high linear correlation between x & y.\nNB Correlation can provide a strong message about correlation between 1 X & 1 Y. It conveys a strong message about NON-correlation of multiple variables. It does not, however, give a strong message about positive correlation between many variables as it works only on individual linear association. For positive messages about correlation of multiple variables always use Regression which takes account of interactions between multiple variables. See also Multicollinearity, Variance Inflation Factor."]
["CP","Confirmation Prototype - Not to be confused with: \n  Cp (process capability) \nor \n  C-p (Mallow's C-p Statistic for bias vs. closeness of fit)!"]
["Cp","A measure of Process Capability reflecting how good the process would be, if it were centred on the target. It is ambiguously used for: \n1. Short Term Process Capability (6-Sigma & MiniTab). \n2. Long Term Process Capability (Ford & Big 3 SPC Reference Manual). \n\nCp takes no account of whether the process is centred. Cpk takes this into account.\nPp = (USL-LSL) / 6*s.                    See also Cpk, Pp, Ppk.\nNote: Nothing to do with CP or  C-p !"]
["C-p","See Mallow's C-p Statistic."]
["Cpk","A measure of Process Capability which takes account of both the spread and centering of the data. It is ambiguously used for: \n1. Short Term (6-Sigma & MiniTab) and 2. Long Term (Ford SPC Ref Manual). \n\nCpk equals the minimum of CPU & CPL. This is the shortest distance between the mean and the nearest spec limit. If the mean is outside both spec limits Cpk becomes negative but Cp is unaffected. Cpk approximates to Cpk = Z / 3.\n\nCpk=min(Cpk(USL),Cpk(LSL))   Cpk(USL)=(USL-Xbar)/3s   Cpk(LSL)=(Xbar-LSL)/3s\nso: Cpk=2 equivalent to Sigma level of 6 (i.e. 6-Sigma).\nSee also Cp, Pp, Ppk, Sigma Level."]
["CPL","LSL Cpk. Also shown as Cpk(LSL)=(Xbar-LSL)/3s.   See also Cpk"]
["Cpm","Cpm is a capability index that is the ratio of the specification spread (USL - LSL) to the square root of the mean squared deviation from the target.\nCpm is available only when a target is specified."]
["CPMT","Component Program Module Team"]
["CPU","1. USL Cpk. Also shown as Cpk(USL)=(USL-Xbar)/3s   See also Cpk.\n2. Cost Per Unit"]
["CQIS","Global Common Quality Indicator System - CQIS is a Ford database providing world-wide access to vehicle concern data. A variety of research and analytical tools are available to assist the problem solver and quality analyst in the early identification and definition of product concern issues"]
["CQTS","Customer Quality Tracking Survey (?) by Research International. Previous Rover customer survey. Similar to GQRS."]
["CR","Capability Ratio. The reciprocal of Cp.    I.E. 1/Cp"]
["Crack On","A bolt torque measuring method by Cracking On beyond the current position.\nApply incremental torque until bolt moves.\nThis is a more resource efficient method but is less informative of the existing relaxed torque condition than is the Back to Mark method."]
["Critical Characteristic","A characteristic affecting safety. Identified in an FMEA as having a severity of 9 or 10 (YC). If confirmed on review of process controls will be designated with an Inverted Delta (Ñ). See also Significant Characteristic."]
["Critical X's","The \"Vital Few\" input varibles to control to reduce variation and achieve CTQ's. \nSee also Critical Characteristics."]
["Criticality","Severity x Occurrence."]
["Cross Tabulation","A very usefule check for balanced data. See also Tally."]
["CRT","Component Review Team"]
["CSI-2","Revised CSI measurement criteria from 1999MY forwards"]
["CT Matrix","Matrix of the CTX (Process) Tree & CTY (Product) Tree. See Y = f(X) and C & E Matrix"]
["CT Tree","See CTX, CTY, CTQ"]
["CTA","Cycle Time Analysis. See Cycle Time."]
["CTC","1. Critical To Cost factors - See CTQ\n2. Concept To Customer. Product development process preceeding World Class Process & FPDS."]
["CTD","Critical To Delivery factors - See CTQ"]
["CTP","Critical To Process factors - See CTQ"]
["CTQ","Critical To Quality factors - Producer measurable quality factors to meet Customer Satisfaction (CTS) needs \n(Similarly for CTC, CTD, CTP). Generically may be used to include CTC, CTD, CTP & potentially CTS."]
["CTQ Tree","CTQ Tree Diagram showing break down of CTQ factors and causes affecting these processes"]
["CTS","Critical To Satisfaction - Customer specified critical satisfaction factors of product / service"]
["CT's","See CTS; CTC, CTD, CTP, CTQ"]
["CtW","Cost to Warranty. LR COPQ measure of %CPU. CtW = 1.3*PartCost + Handling + Markup + Supplement + CPW handling + Landing + Labour + Other charges!"]
["CTX Tree","Process & Sub-Process hierarchy tree - \"X\" inputs. Supports the left hand side of the CT Matrix"]
["CTY Tree","Product System-Sub-System-Part hierarchy tree - \"Y\" outputs. May be the top line of the CT Matrix"]
["Cube","1. See Quality Data Cube.\n2. See Ice Cube Chart."]
["Cumulative Distribtion Function","Cumulative Distribtion Function. The cumulative representation of the Probability Density Function (PDF). When plotted on Log scale for time/cycles and Log-Log scale for Median Ranked frequency becomes a straight line slope in the Weibull Analysis."]
["Cumulative Hazard","Sum of Hazard Intervals to date. Also = Cumulative Hazard up to previous interval + Hazard Interval for this period. Used in Hazard Plot to determine Failure Type."]
["Customer Concern Code","See CCC"]
["CUSUM","Cumulative Sum (CUSUM) a control chart which detects abrupt but slight changes in the process mean past data are combined with current data"]
["CUT-OFF POINT","The point which partitions the acceptance region from the reject region."]
["CVA","Brand/Consumer Value Added"]
["CW","Closed World. See Closed World Method & Closed World Diagram. An SIT concept."]
["Cybernetics","The science of effective organization and management. The manager's perogative: To organize effectively.See also: Viable System Model."]
["Cycle Time","Time to complete a manufacturing operation. Incorporates Operator Cycle Time & Machine Cycle Time. Can be mapped in a Yamazumi Board to identify Value added and non-value added steps. Part of Kaizen Lean Manufacturing method. \nSee also Takt Time, Work Sequence, Standard WIP."]
["d","Difference / effect size. See also d/s"]
["D","Delta. Step size in one of the process variables in a Response Surface Method DOE or EVOP. Note: D must be estimated heuristically based on process understanding and potential risks. See also Inverted Delta (Ñ)."]
["D","1. See Desirability. \n2. See FMEA, Detection.\n3. The Define phase of a DMAIC 6-Sigma project.\n4. The Define phase of the Ford DfSS 6-Sigma project methodology.\n5. The Design phase of the SSA DfSS 6-Sigma project methodology.\nSee also Multiple Response Optimizer and Response Surface Method."]
["d/s","The magnitude or size of the difference being tested.  This is sometimes called the Test Sensitivity."]
["D0","A Discipline of the Global 8D problem solving methodology.\nPreparing for the Global 8D. Includes: Defining & quantifying the Symptoms \nand taking Emergency Response Actions                                  ERA."]
["D1","A Discipline of the Global 8D problem solving methodology.\nIncludes: Establishing the                                                        Team."]
["D2","A Discipline of the Global 8D problem solving methodology. \nIncludes: Describe The                                                            Problem.\nProblem statement: Object & defect. Problem Description: IS/IS NOT description. Observations (Not Conclusions), Sub-divide complex problems. Sheet 1 of Problem Solving Worksheet."]
["D3","A Discipline of the Global 8D problem solving methodology. \nIncludes: Develop Interim Containment Actions                          ICA."]
["D4","A Discipline of the Global 8D problem solving methodology. \nIncludes: Define & Verify                                                         Cause & Escape Point\nComparative Analysis from IS/IS NOT problem description; \nSheet 2 of Problem Solving Worksheet; Conclusions …"]
["D5","A Discipline of the Global 8D problem solving methodology. \nIncludes: Choose & verify Permanent Corrective Actions             PCA.\nDecision Making Worksheet: Givens, Wants, How Important & Good (1-10). \nRisk Analysis (mini FMEA) to Givens, Wants, Universal factors."]
["D6","A Discipline of the Global 8D problem solving methodology. \nIncludes: Implement & validate Permanent Corrective Actions      PCA."]
["D7","A Discipline of the Global 8D problem solving methodology. \nIncludes: Lessons Learned & Preventing Recurrence -                Control."]
["D8","A Discipline of the Global 8D problem solving methodology. \nIncludes: Team & Individual                                                      Recognition."]
["DACE","Design and Analysis of Computer Experimentation (DACE) for building Nonlinear Emulator Model. Part of AR&R. Includes:  \nDesign of Computer Experiment: AOSCE\nAnalysis of Computer Experiment: EMARS"]
["Daniel Plot","Test for significance. Named after industrial statistician Cuthbert Daniel. Maps absolute values of Effect of each factor and each Interaction vs RH half of a normal distribution. Insignificant factors will be small and in a line from the origin. Significant factors will be higher. Also known as Half Normal Plot. Minitab equivalent is the Normal Effects Plot with the axes reversed. See FTEP Experimental Design course notes."]
["DARE RIMCY","Acronym for the Deisgn Of Experiment (DOE) methodology: \nDefine, Analyse, Reduce, e2, \nResiduals, Interactions, Main, Cube, Y=c+mx…Response Optimisation"]
["DATA","Factual information used as a basis for reasoning, discussion, or calculation; often refers to quantitative information."]
["Data Cube","See Quality Data Cube."]
["Day 1 Deviation","See Never Been There problems."]
["DCE","Design of Computer Experiment. See AOSCE. Part of DACE & AR&R."]
["DCOV","DfSS project phases: Define, Characterize, Optimise, Verify. "]
["DCP","Dynamic Control Plan"]
["Defect","An event which does not meet the specification of a CTQ. Occurrence is measured in DPU, DPO, DPMO or Sigma Level. So a Defect is an individual non-conformance. A given unit may potentially have any number of Defects - according to the number of Opportunities. A unit with one or more Defects is a Defective. Defects are caused by Errors. Measured using c Chart or u Chart. See also Defectives, Opportunity, Error."]
["Defect Opportunity","See Opportunity"]
["Defectives","Defectives are nonconformities in products or services that render the product or service unusable. You have defectives when data can take only one of two values, such as pass/fail, go/no-go, or present/absent.The binomial distribution characterizes defectives data. So a Defect is an individual non-conformance. A given unit may potentially have any number of Defects - according to the number of Opportunities. A unit with one or more Defects is a Defective. Measured using np or p Chart. See also Defect, Opportunity. "]
["Define","The DMAIC and DfSS 6-Sigma project phase which identifies the scope of the project and its' objectives. \nThis phase includes: Identify Critical to Satisfaction (CTS) drivers (Y's).\nFor DMAIC also: process map & metrics - See Define Phase.\nFor DfSS also: Establish operating window for chosen Y’s for new and aged conditions (a£Yi£b)."]
["Define Phase","The 1st DMAIC project phase. Define the Project Scope, Problem Statement (identify Defects), Objective (reduce Defects), Benefits and Metric (track Defects - % defects, overtime, RTY, etc…) "]
["Degrees Of Freedom","The number of independent measurements available for estimating a population parameter. Calculating degrees of freedom. Nomenclature: n\n· DF Total = n - 1, where n = total number of observations\n· DF Operator = b - 1, where b = the number of operators\n· DF Part = b(a -1), where a = the number of parts measured by each operator and b = the number of operators\n· DF Repeatability = n - (a*b)"]
["Delta","1. d - Difference / effect size\n2. D - Step size in one of the process variables in a Response Surface Method DOE or EVOP. Note: D must be estimated heuristically based on process understanding and potential risks.\nSee also Inverted Delta (Ñ)."]
["Demerits","Faults found in a quality inspection"]
["Demings 14 Management Principles","1. Create Constancy of Purpose for improvement of products and services\n2. New Philosophy (Right First Time Defect Prevention)\n3. Cease Dependence on Mass Inspection (Make processes capable)\n4. End awarding contracts on 'lowest tender' only\n5. Constantly & forever improve systems & processes\n6. Institute Training on the Job\n7. Institute Leadership / Supervision which helps people & machines do a better job\n8. Drive Out Fear - Encourage effective 2-Way communication\n9. Break Down Barriers between organisations\n10. Eliminate Exhortations - numerical goals, slogans & posters\n11. Eliminate Targets - arbitrary work  standards, numeric quotas & goals\n12. Permit Pride in Workmanship - remove barriers to the hourly worker\n13. Encourage Education - Institute a vigorous program of education & training\n14. Top management Commitment to daily push these principles. Taking action to accomplish transformation."]
["Deming-Shewhart Cycle","See Plan Do Study Act."]
["DENSITY FUNCTION","The function which yields the probability that a particular random variable takes on any one of its possible values."]
["DEPENDENT VARIABLE","A Response Variable; e.g., y is the dependent or “Response” variable where Y=f (X1…Xn) variable. See Y's"]
["Design","The second phase of the SSA DfSS IDOV project methodology. \nDESIGN, represents both the initial concept generation or any re-designs required accommodating technology limitations.\nFollows Identify and proceeds the Optimization & Verification phases. \nIn the Ford DfSS methodology the 1st step is Define and the 2nd step is Characterize."]
["Design Resolution","A Roman numeral notation that allows you to describe the confounding scheme associated with the design of an experimant. See Resolution, Fractional factorials."]
["Design Synthesis","The Systems Engineering process which generates & evaluates alternative designs using the Trade-Off process. Iterates with Requirements Analysis & Verification at vehicle, system & component levels. "]
["Desirability","An output measure fromMultiple Response Optimizer and Response Surface Method. \nA desirability function translates each response scale to a zero-to-one desirability scale. The most desirable values of the response have desirability one. The least desirable values have desirability zero.\nNomenclature: D=0-1. See also Weight, Importance."]
["Detection","See D Level, FMEA"]
["DF","See Degrees of Freedom. Nomenclature: n"]
["DFMEA","Design FMEA. Application of FMEA during the design process to assess product relaibility. See also PFMEA."]
["DfSS","Design For Six Sigma - A methodology to apply to the design process to generate RFT quality designs. Should pre-empt the need for DMAIC projects to fix quality issues."]
["DIF-DOF","Design In Function; Design Out Failure. Complimentary strategies to achieve quality and reliability"]
["Dimensionality","An Structured Inventive Thinking solution technique. Follows use of the Closed World Method or the Particles Method. Used with the other SIT solution techniques of Uniqueness, Pluralization & Redistribution."]
["Discrete Data","Data from counting categories of conditions e.g. Pass/Fail counts. See M-07 Introduction to Data.ppt for selecvting statistical techniques according to Discrete/Continuous nature of input & output attributes & variables. See also Attribute, Continuous Data"]
["DISCRETE RANDOM VARIABLE","A random variable which can assume values only from a definite number of discrete values.As opposed to a Continuous Variable. May be used interchangeably with Categorical Variable"]
["Discrim","See Discrimination Index"]
["Discrimination","The number of decimal places that can be measured by the system.  Increments of measure should be at least one-tenth of the width of the product specification or process variation. Same as resolution. See also Discrimination Index."]
["Discrimination Index","The Discrimination Index provides the number of divisions that the Measurement System can accurately measure across the part (sample) variation.  If this index is less than 4, then it is inadequate to provide data for a study.  If the index is 4, then it is equivalent to a go/no-go gage.  We would like to see the value of 5 or greater. \nDiscrim = 1.41 * sp / sms. Same as number of Distinct Categories"]
["Distinct Category","Number of Distinct Categories. Aletrnative (MiniTab) nomenclature for Discrimination Index. Values: 1: Measurement System cannot discriminate between parts\n2 - 4: Parts can be divided into high/low groups only. Equiv to attribute data. Can provide Insensitive control charts, Coarse estimates of process parameters and capability indices\n5 or more: The gage system is acceptable, can differentiate between parts and can provide: Control charts, process parameters, and capability indices"]
["Distributions","Tendency of large numbers of observations to group themselves around some central value with a certain amount of variation or “scatter” on either side. See Normal, t-, Z-, F- , Weibull Distributions"]
["D-Level","FMEA Detection Level. Rated 1-10 by the Big 3 for Certain, High, moderate, Low, Almost imposible liklihood of detection by process controls before next process step or process completion. An assessment of the likelihood (or probability) that your current controls will detect 1) when the X fails or 2) when the failure mode occurs."]
["DMAIC","Define-Measure-Analyse-Improve-Control - The major phases of a 6-Sigma project, representing the core 6-Sigma methodologies. In contrast to 6-Sigma DFSS methodology. The DMAIC process may be further broken down as:\nM1 Select Output Characteristic and identify key process input and output variables, M2 Define Performance Standards, M3 Validate Measurement System\nA4 Establish Product Capability, A5 Define Performance Objectives, A6 Identify Variation Sources\nI7 Screen Potential Causes, I8 Discover Variable Relationships, I9 Establish Operating Tolerances\nC10 Validate Measurement System, C11 Determine Process Capability, C 12 Implement Process Controls"]
["DOE","Design Of Experiments - Systematic study multiple factors & levels to determine their effects & identify CTQ's. Includes: Full Factorials, 2K Factorials, Fractional Factorials.\nDOE can be used to:\n- Establish the relationship between the X’s & Y's\n- Establish the vital few X’s from the trivial many (possible factors)\n- To establish if several input factors act together to influence the output (Y)\n- Determine the optimal settings of the input factors\nMethod: DARE RIMCY - \n             Define, Analyse, Reduce, e2, Residuals, Interactions, Main, Cube, Y=c+mx... \n             Resource Optimisation\nSee DOE Worksheet at: ...\\SSAnavProcessDataFiles\\DOE Worksheet.doc. See also Type Of Experiment. Extensions to DOE include: Parameter Design & Tolerance Design."]
["dof","Degrees of Freedom"]
["DOPU","Defect Opportunities Per Unit. See also DPU, DPMO."]
["DOT","US Department of Transportation"]
["dpm","Defects Per Million. See DPMO."]
["DPMO","Defects Per Million Opportunities. See also Initial, Baseline & Target DPMO, DPO, DPU. Where opportunities per unit = 1 will be same as PPM."]
["DPO","Defects Per Opportunity. See also DPMO."]
["DPU","Defects Per Unit - A measure of customer impact vs. process capability. See also DPMO. Related to RTY: Yrt = e-DPU"]
["DPV","Production Daily Planning Volumes"]
["DQR","World-class Durability, Quality, Reliability. One of Jaguar's Core Marque Values. See also Durability, Reliability."]
["Drift","Changes in quality over time. See also stability, Shift, short & long term sigma values"]
["DTD","1. Demand To Delivery. The full product realisation process from concept through design and development to product delivery.\n2. Dock to dock time."]
["DTS","Durability Tracking Study - TGW survey data for products 12 & 36MIS"]
["Dumb Little People","A more apt desciption of Particles than TRIZ's Smart Little People"]
["Durability","Reliability over the specified life-time of the product. \nSee also Robustness, Homeostasis, DQR."]
["DVM","Design Verification Method"]
["DVP","Design Verification Plan. Managed in FDVS. The planned analysis and vehicle/rig tests to confirm achievement of design specification. Achievement is approved in the DVP Sign-Off Report (DVP&R or DVP-SOR)."]
["DVP&PV","Design Verification, Production and Process Validation"]
["DVP&R","Design Verification Plan & Report. Same as DVP-SOR."]
["DVP-SOR","Design Verification Plan & Sign-Off Report"]
["Dynamic System","A System which has a variable Signal. E.g. Accelerator. In contrast to a Static System where there is a fixed Signal. E.g. ignition switch. A system where there is interaction between factors. A dynamic system with a highly complex logical relation of function to purpose may be referred to as a Purposive System."]
["e","A constant: 2.71828182845905. The base of natural logarithms. \ne is such that the gradient of the function y=ex  always equals y.\ne is defined as the limit of (1 + 1/n)n as n approaches infinity."]
["E","Energy Exchange interface in FMEA Interface Matrix."]
["e ","Greek letter Epsilon. See Random Error, Epsilon-Squared"]
["e2","See Epsilon-Squared"]
["EAM","See Engineering Activity Matrix."]
["EAR","Engineering Action Request - Generic term for product change request. Follow Ford/Jaguar AIMS & WERS processes to introduce product change."]
["Early Life Failures","Failures early in product life. Generally manufacturing problems. \nSee also Useful Life Failures, Wear Out Failures, Bathtub Curve."]
["ED","Experimental Design - See DOE"]
["EDA","Exploratory data Analysis - preliminary high level data analysis e.g. Pareto, Run charts etc."]
["eFDVS","The web interface to the FDVS system."]
["EFFECT","1. That which was produced by a cause. See Y's. See also Failure Effect.\n2. In DOE Sample Size: The d difference that the experiment needs to detect. \n    See also DOE, Corner Point, Failure Effect, Main Effects."]
["Effects List","List of Failure Effects as the impact:\nDFMEA: Part, Assy, System, Veh/Product, Customer, Regs, Other.\nPFMEA: Op Safety, Next user, Downstream user, veh Operation, Int & Ext cust, Regs.\nSee also Big 7."]
["Elimination of Waste","See Waste."]
["ELP","Evil Little People. An extension of the SIT Particles Technique technique to diagnose root causes."]
["EMARS","EMARS:Enhanced Multivariate Adaptive Regression Spline. Part of DACE & AR&R."]
["Emergency Response Action","See ERA."]
["End Of Life Failures","See Wear Out Failures."]
["Energy Transfer","The means by which a process generates responses & error states from signal input.\nAlso known as Energy Transformation. See also P Diagram."]
["Energy Transformation","The means by which a process generates responses & error states from signal input.\nAlso known as Energy Transfer. See also P Diagram."]
["Engineered System Model","A representation of a System as shown in a P Diagram."]
["Engineering Activity Matrix","Five stage matrix of engineering activities (\"Things Engineers Do\"). Covers Analysis and Diagnosis of reliability; Define Requirements; Design for Robustness; Design Verification; Maintenance & Improvement of Quality in Production."]
["Entitlement","As good as it gets - maximum process potential. Potential (Within) Capability for centred normal data. See Short Term Process Capability."]
["EOQ","European Organisation for Quality"]
["Epsilon","Greek Letter e."]
["Epsilon-Squared","Proportion of behaviour is explained by the factor. Part of ANOVA One Way analysis - \nSum of Squares (SS) of Factor over SS Total I.e. SS(Factor) / SS(Total). \nNomenclature: e2.     Units: Expressed as a %\nNote: Minitab shows SS values, but does not calculate e2 for you - its DIY!"]
["Equal Variance","See HOMOGENEITY OF VARIANCE"]
["Equipment Variation","Equipment Variation in a Gage R&R study. Same as Repeatability.\nSee also Assessor Variation."]
["ERA","Emergenct Response Action. Immediate action to isolate the customer from the symptom of the problem. Part of Global 8D step D0. See also ICA, PCA."]
["Error","Errors are the cause of defects…an error occurs when the conditions for successful processing are either incorrect or absent. See also Experimental Error, Random Error. \n10 most common errors: 1. Processing omissions, 2. Processing errors, 3. Error in setting up job, 4. Assembly omissions ( missing components), 5. Inclusion of incorrect component, 6. Incorrect job, 7. Operation error, 8. Measurement error, 9. Tool or equipment error, 10. Defects in job components\n10 common human errors: 1. Forgetfulness, 2. Misunderstanding, 3. Identification, 4. Lack of effective training, 5. Willful errors, 6. Inadvertent errors, 7. Delay in task execution, 8. Lack of standards, 9. Inability to compensate for new situations, 10. Intentional errors\nSee also Error Proofing, Error State, Defect."]
["Error Proofing","See Mistake Proofing."]
["Error State","An unintended response from a process. See P Diagram."]
["Escape","A control system failure allowing the progress of sub-standard parts. See also Escape Point, Escape Path."]
["Escape Path","The flow of the product / process from the causal point through to the point the product is declared OK. See also Escape, Escape Point."]
["Escape Point","The earliest location in the process, closest to root cause, where the problem should have been detected but was not. See also Escape, Escape Path."]
["E-Squared","See Epsilon-Squared"]
["ESSE","Ford Engineering Standards & Systems Engineering department. Where all generic VDS, SDS, CDS's are stored. Formerly SSO."]
["EST","Engineering Systems Team"]
["EV","Equipment Variation in a Gage R&R study. Same as Repeatability. \nSee also Assessor Variation."]
["Evil Little People","An extension of the SIT Particles Technique technique to diagnose root causes."]
["EVOP","EVolutionary OPerations. A process improvement tool used while the process is running in the production mode for the optimization of plant performance. Method that uses 22 or 23 factorials with replicates and center points. Empowers operators to conduct experiment with minimal engineering support during normal operations. Each experimental run is called a CYCLE: One cycle is one of the following:  (0,0)=>(1,1)=>(1,-1)=>(-1,-1)=>(-1,1)\nEliminate randomization to minimize disruption & document effect estimates at the end of each cycle. Cycle continues in the hopes of collecting “sufficient evidence” of significant change in the Y for the various levels of X. See also PLEX."]
["EWMA","Exponentially Weighted Moving Average - a control chart which smoothes data to emphasize trends. Uses weights to emphasize the importance of the most recent data. See also Moving Average."]
["Excitement Quality ","Delights: CTQ's which are over and above customer expectations and which have a wholey positive effect when present. \nPart of the Kano Model. See also Basic Quality, Performance Quality."]
["EXPERIMENT","A test under defined conditions to determine an unknown effect; to illustrate or verify a known law; o test or establish a hypothesis."]
["Experimental Error","Variation in observations made under identical test conditions.  Also called residual error or Random Error.  The amount of variation which cannot be attributed to the variables included in the experiment. \nSee Type I, II Errors, Alpha & Beta Risks. See also Error, Defect."]
["External Validity","Part of ensuring DOE Validity. Include representative samples from possible Noise Variables. Examples: Insure experimental units represent supplier variability.\nDo experiment across shifts and days. Include different product families. See also Internal Validity."]
["Extreme Value Distribution","A logarithmic distribution releated to the Weibull distribution which can be useful for reliability testing of failures relkated to loads and strength e.g. bridges!"]
["f","Function - Y = f(X) - Outputs are a function of the inputs. See also F, F(t)."]
["F","1. Number of Failures. See Failures, F/C.\n2. The F-Statistic. Used by ANOVA in calculating P-Value from adjusted mean squares.\nSee also f."]
["F&T","Facilities and Tooling - Capitalized F & T Spending"]
["F(t)","The Hazard Function. The distribution of Failures over time. The inverse of the Reliability Function R(t). See also F, f."]
["F/C","Number of Failures and number of items Censored. E.G. F/C = 27/3 means that the Reliability test measured 27 failures and 3 parts which survived."]
["Factorials","Part of DOE. Includes: Full Factorials, 2K Factorials, Fractional Factorials"]
["FACTORS","Independent variables. See X's"]
["Failure","1. An observation that does not demonstrate the characteristic of interest. See Success\n2. Just a plain ordinary failure to meet required standard of performance"]
["Failure Cause","FMEA Failure Cause: something which can be corrected or controlled (X) that describes “how the failure mode could have occurred”. Sources of process variation that causes the Failure Mode to occur.  What is the root cause of the failure."]
["Failure Effect","FMEA Failure Effect: the effect a particular failure mode will have on the customer (attempt to quantify with respect to Y's). The impact on customer requirements.  Generally external customer focus, but can also include downstream processes."]
["Failure Mode","FMEA Failure Mode: a description of a “non-conformance” at a particular process step (also know as a shop floor defect). The way in which a specific process input fails - if not detected and either corrected or removed, will cause Effect to occur. See also Failure, Failure Type."]
["Failure Rate","No Failures / No units (opportunities) during this period sampling without replacement. Same as Hazard Interval, Hazard Rate. See also Cumulative Hazard."]
["Failure Type","Type of failure according to:-\n1. Point of failures in product lifes cycle. Shown by the Bathtub Curve or Hazard Plot to be Early Life, Usefule Life or Wear Out failures.\n2. Partial, Intermittent, Total loss, Degradation or Excessive Function\n3. Severity: Dependability/Safety, High: Confidence, Irritaion, Cost of ownership"]
["FAQ","Frequently Asked Questions"]
["Fault Tree","See Fault Tree Analysis."]
["Fault Tree Analysis","Fault Tree Analysis. A graphical representation of the causes of a problem. Structured using boolean And & Or gates. Captures interactions as well as individual factors. Frequency of occurrence can then be assessed & prioritised. A more powerful representation than a Cause & Effect Diagram. A useful tool to assist preparation of an FMEA which also incorporates Severity & Detection."]
["F-Distribution","Fishers-Distribution. The statistical distribution of data being ratio of two Chi-Squared variables "]
["FFT","Fit, Focus & Timing."]
["Final Capability","Process capability after process improvements are made. Same as Z???. May bes expressed as Short or long term capability. See also Baseline Capability."]
["Final Yield","Measure of % defect-free output after rework. \nSee Yield for formulas\nSee also First Pass Yield & Rolled Throughput Yield/First Time Through, Normalised Yield"]
["First Pass Yield","Measure of % defect-free output at any process step before rework. \nSee Yield for formulas\nSee also Final Yield & Rolled Throughput Yield/First Time Through, Normalised Yield"]
["First Time Yield","Same as First Pass Yield. Nomenclature: YFT"]
["FIRTFT","Fix It Right The First Time "]
["Fish bone diagram","Fish bone shaped map of inputs, sub-inputs and root causes. \nSee Cause & Effect Diagram and \"5M's and a P\", Also known as Ishikawa or Herring Bone diagram:\nx\\  x\\   y\n x/  x/"]
["Fits","The place on the best fit line where the Y value was expected to be for this X. See also Residuals, Regression, Correlation."]
["FIXED EFFECTS MODEL","Experimental treatments are specifically selected by the researcher.  Conclusions only apply to the factor levels considered in the analysis.  Inferences are restricted to the experimental levels."]
["Fixture","A device used to hold a part in place during the manufacturing process \ne.g. in relationship to a cutting tool, e.g. milling – turning etc. \nSee also Jig, Tool, Setting Aid, Gauge."]
["Flier","Term used for a point plotted onto control chart that exceeds control limits and is considered a one-off."]
["FlowCharter","MicroGrafx iGrafx process flow charting software"]
["FLUCTUATIONS","Variances in data which are caused by a large number of minute variations or differences."]
["FMEA","FMEA: Failure Mode Effects Analysis - Systematic method of risk assessment for identifying and preventing product & process problems before they occur. Identify ways the product or process can fail and eliminate or reduce the risk of failure in order to protect the customer. Always done as a team exercise. Process:Select Team, define roles scope product/process; Identify Functions and Failure Modes. Path 1: Define Failure Effects, Seveity & critical actions. PAth 2: Define Causes, Occurence & critiocal Actions. Path 3: Define preventaive (P) & detective (D) Controls & Actions to reduce RPN.\nSee also S, O & D Levels, PFMEA, DFMEA, CFMEA, MFMEA, Failure Mode, -Effect, -Cause, RPN, Boundary Diagram, Interface Matrix, Critical, Significant Characteristic, YC, YS, Effects List, OS, HI, Big 7, Criticality, FMEA Plus"]
["FMEA Plus","Ford FMEA software package"]
["Focus Elements","The 8 elements of th 1996 APQP which were deemed most critical above the other essential elements, plus one Jaguar element. NB. 2001 APQP treats all as Focus!\nThese get a 0-3 Quality Of Event rating as well as GYR. 0 = 0-50% complete, 1=51-99% complete. 2=100% complete. 3=100% complete plus extra actions. \nFocus Elements are: DFMEA, DVP, PBCP, Mfg Porcess Flow, PLCP, OPI & PCP(Jag)\ncf. FPDS Inviolable Elements."]
["Fold Over","The ability to add sequential factorial experiments to an existing fractional experiment with the intention of estimating specific main effects or interactions free of particular confounding problems. Fractional Factorials can be “folded over” to add to the design.  Example:  A half-fraction folded over can become a full factorial with two blocks.  Folding over is just changing the signs of the original fraction and rerunning the experiment.\nSee Fractional Factorials."]
["Forming","Stage 1 in Tuckman's model of team development. Includes: establishment of purpose, objectives & method and cautiously exploring the boundaries of acceptable group behaviour."]
["FPU","Faults Per Unit. LR FPU = # incidents / sample size. See also %FPU, %Incidence."]
["FPV","1. Faults Per Vehicle\n2. Functional Proveout Vehicle \n3. Financial Planning Volume "]
["Fractional Factorial","Application of screening to reduce no of DOE runs by exploiting the Sparsity of Effects Principle Each resulting effect will be the sum of an individual effect plus an interaction effect which can no longer be distinguished (confounded).\nMay be done using Resolution III, IV or V Designs:\nIII:  No main effects are aliased with other main effects. Main effects aliased with 2-factor interactions. \n      2-factor interactions are aliased with each other.\nIV: No main effect aliased with other main effects OR with two-factor interactions. \n      2-factor interactions aliased with other 2-factor interactions.\nV:  No main effect or 2-factor interaction is aliased with any other main effect or 2-factor interaction. \n      2-factor interactions are aliased with 3-factor interactions.\nNomenclature: 2Rk-p. Where k is the number of factors 2k-p is the number of runs & R is the resolution (III/IV/V). \nSee also: Full Factorials, 2K Factorials, Blocking Variables, Resolution, Alias.\nNB: Fractional factorials will lead to wrong conclusions when interactions are dominant factors e.g. Sweetness from Sugar/Srirring/Cup colour would highlight cup colur rather than the sugar-stirring interaction. Similarly draughts from Door size/Frame Size/Door colour."]
["F-Ratio","SS / MS. Sum od Squares divided by the Mean Square. Used to calculate P in ANOVA."]
["FREQUENCY DISTRIBUTION","The pattern or shape formed by the group of measurements in a distribution."]
["FSNP","Forming, Storming, Norming, Performing. Tuckman's model of team development stages."]
["FTA","See Fault Tree Analysis."]
["FTEP","Ford Technical Education Program. Ford Design Institute program of quality training modules. See also EQUIP."]
["F-Test","A type of t-Test. Tests the assumption of equal variance. An F-test is done when you have only two samples; test assumes your data is normally distributed. Use Levene or Bartlett tests for > 2 samples."]
["FTT","First Time Through - Jaguar speak for RTY - Rolled Throughput Yield."]
["Full Factorial","Part of DOE. See also: 2K Factorials, Fractional Factorials, |.\nStep 1:  State the Practical Problem and Objective using the DOE Worksheet.doc\nStep 2:  State the factors and levels of interest, \nStep 3:  Select the appropriate sample size.\nStep 4: Create a Minitab experimental data sheet with the factors in their respective columns. Randomize the experimental runs in the data sheet\nStat > DOE > Factorial> Create Factorial Design\nStep 5:  Conduct the experiment.\nStep 6:  Construct  the ANOVA table for the full model, use either: Stat>ANOVA>BalANOVA or Stat>DOE>Factorial>Analyze\nStep 7:  Review  the ANOVA table and eliminate effects with p-values above .05.  Run the reduced model to include those p-values that are deemed significant.\nStep 8:  Analyze the residuals of the reduced model to ensure we have a model that fits.  Calculate the Fits and Residuals. \nStat>ANOVA>Storage. Next create the residual plots Stat>Regression>ResidPlots \nStep 9:  Determine optimal settings by graphically analyzing significant interactions (p-value <.05).  Assess the significance of the highest order interactions first.\nFor 3-way interactions unstack the data and analyze.  Use either:\nStat > ANOVA > Interaction Plot or Stat > DOE > Factorial> Factorial Plots > Interaction Plot\nOnce the highest order interactions are interpreted, analyze the next set of lower order interactions.\n  Investigate significant Main Effects (p-value <.05) use either. \nStat > ANOVA > Main Effects; Stat > DOE > Factorial> Factorial Plots > Main Effects Plot & Cube Plots\nStep 10:  For practical purposes calculate epsilon square values\nStep 11:  Replicate optimum conditions.  Plan the next experiment or institutionalize the change.\nStep 12:  Document final report"]
["Function","1. y=f(x) - Outputs are a function of the inputs.\n2. In FMEA: Measurable and actionable statements of all product / process deliverables to meet customer needs and wants. Expressed as verb & noun. May be gathered or grouped in a Function Tree.\n3. See also Density Function, Probability Density & Cumulative Distribution Function, Reliability, Hazard & Survival Function, Quality Loss Function etc."]
["Function Diagram","See SIT Oval Wheels notes."]
["Function Trees","A hierarchical representation of product/process Functions. Typically showing around 5 primary functions with lower level functions below that. Contructed by breaking down primary functions by asking the question: How is this function achieved? And verified in reverse direction with the question: Why does this function contribute or lead to the higher level function?"]
["Functional Relationship Diagram","See SIT Closed World Method."]
["Funnel","Process of converging from multiple X's (inputs/causes) to the CTQ's"]
["G8D","See Global 8D."]
["Gage","American spelling of Gauge. A device for measuring an important product feature or combination of features (attribute or variable) during the manufacturing process. See Gage R & R. See also Jig, Tool, Fixture, Setting Aid."]
["Gage Contribution","Contribution (proportion) of cause attributable to the gage or measurement system. Minitab field: %Contribution. AIAG standards require: \n<1% = Ideal, 1-4%=Acceptable, 4-9%=Marginal, >=10%=Unacceptable\nNomenclature: P = s2ms / s2total or P = 5.15* sms.  \nSee also Gage R & R, Part Contribution"]
["Gage Precision","Same as Gage Contribution"]
["Gage R&R","A method of measuring (gauging) an attribute or varibles Value relative to its' Operational Definition. Further used to determine the Repeatability & Reproducibility of those values. Generically used to mean any measurement system. \nWhen quoted as a number this is the P/T ratio. (or in some companies the P/TV).\nAcceptance criteria:                          Ideal     Accept    Marginal      Fail\nGage Contibution                              <1%     1-4%         4-9%       >10%\n%Tol=P/T and %R&R=Study Var       <10%    <20%      20-30%      >30%\nDiscim Index & Distinct Categories     >14       >7            7-5           <5\nNomenclature: %R&R. %R&R = sMS / stotal .\nSee P/T, P/TV. See also Attribute R & R, Variable R & R, Correlation."]
["Gain","Improvement in target resonse (Y) over baseline."]
["Gantt Chart","Graph showing steps of plan and durations"]
["Gauge","British English spelling of Gage. A device for measuring an important product feature or combination of features (attribute or variable) during the manufacturing process. \nSee Gage R & R. See also Jig, Tool, Fixture, Setting Aid."]
["Generification","An element of Structured Inventive Thinking (SIT). "]
["Geometric Array","A Balanced Array for a Factorial Experiement where: the product of each pair of columns is another column in the table. E.g. L8. Geometric arrays confound each 2-way & many-way interaction onto individual Main Effects factors/columns. See also Non-Geometric Array."]
["GLM","General Linear Model. A DOE analysis where there are missing value(s) which prevent a balanced ANOVA analysis"]
["Global 8D","Ford Global Problem Solving methodology applying 9 (!) Disciplines: See D0-D8."]
["Glossary","Other 6-Sigma, Statistics and/or SPC glossaries can be found at:\nMinitab>Help>Help>Glossary\nMinitab>Help>Search Stat Guide\nhttp://www.animatedsoftware.com/statglos\nhttp://www.jaguar.ford.com/manuf/prodop1/spc/reports/glossary.pdf\nImplementing Six Sigma, F. W. Breyfogle III, Glossary"]
["GOAL","Growth Opportunity Alliance of Lawrence. Publishers of the 6-Sigma and Quality Tools \"Memory Jogger\""]
["Goals","The 1st element of the GRPI leadership strategy:\nGOALS: Core Mission of Team - Performance Objectives\n* Goals are clear and people are committed to them\nSee also Roles & Responsibilities, Processes, Interpersonal Relationships, 4P's, GOAL."]
["Green Belt","A certified part time practicioner of 6-Sigma problem solving methodologies. Will deliver successful localized projects using the Breakthrough Strategy. Mentored by and may support a Black Belt. "]
["GRPI","Goals, Roles, Processes & Interpersonal relationships. The original 6-Sigma Leadership Team Management Model. Now referred to as 4P's. \nSee Goals, Roles & Repsonsibilities, Processes, Interpersonal Relationships."]
["GRR","Same as Gage R & R"]
["GYR","Green-Yellow-Red traffic light status e.g. in APQP or FPDS deliverables. Green = On target deliverable. Red = Off target, no recovery plan. Yellow = Off target, but with recovery plan in place."]
["h","1. Greek letter Eta ???\n2. The Scale of the Weibull Distribution."]
["H","Historic APQP GYR status."]
["H(t)","The Hazard Function."]
["H0","Nomenclature for Null Hypothesis"]
["H1","Alternative nomenclature for Alternative Hypothesis. Preferred nomenclature: Ha"]
["Ha","Nomenclature for Alternative Hypothesis"]
["Half Normal Plot","Same as Daniel Plot."]
["Hard Failure","Failures can be either SOFT (degraded performance to an unacceptable level) and/or HARD (product function ceases)."]
["Hazard","Probability of failure at a given point in time. See Hazard Function."]
["Hazard Function","Plot of probability of failure vs time. Nomenclature: F(t). See also Survival Function."]
["Hazard Interval","No Failures / No units (opportunities) during this period sampling without replacement. Same as Hazard Rate, Failure Rate. See also Cumulative Hazard."]
["Hazard Plot","A Log-Log plot of Cumulative Hazard vs Time. It's shape and slope can be used to identify where the issues are in the Bathtub Curve. Early Life failures will appear as sharply risung then trailing off. Useful Life failures will appear as a straight line at gradient = 1 i.e. 45o.  Wear Out failures will appear as a steep straight line or may feather off if late submission of readings occurs. To differentiates between Early Life and Wear Out failures observe readings for successive collection months. If the trend is followed failures are Early Life. If the feathering moves up the failures are Wear Out failures."]
["Hazard Rate","No Failures / No units (opportunities) during this period sampling without replacement. Same as Hazard Interval, Failure Rate. See also Cumulative Hazard."]
["Herring Bone Diagram","Same as Cause & Effect Diagram. See also Fish bone diagram, \"5M's and a P\". Also known as Ishikawa diagram"]
["Heteroscedasticity","A process where variation changes with the input variable (e.g. variation increases over time). See also homoscedasticity."]
["HI","1. High Impact. An additional Significant Characteristic classification in Power Train FMEA's. See also YC, YS.\n2. hi: Hazard Interval.\n3. Hi: Cumulative Hazard."]
["hi","Hazard Interval. See also Hi: Cumulative Hazard."]
["Hi","Cumulative Hazard. See also hi: Hazard Interval."]
["Hidden Factory","Scrap & Rework costs inc. Time, Money, Resources, Floor space etc. 6-Sigma seeks to identify and eliminate the Hidden Factory"]
["HISTOGRAM","Vertical display of a population distribution of terms of frequencies; a formal method of plotting a frequency distribution."]
["Homeostasis","The stability of a system's internal environment, despite having to cope with an unpredictable external environment. The same principle as Robustness.\nPart of the Viable System Model (VSM) developed by Stafford Beer. \nSee also Viable, Recursion, Self-Reference, Homeostasis, Invariant, P Diagram."]
["Homogeneity Of Variance","The variances of the groups being contrasted are equal (as defined by statistical test of significant difference). Use Bartlett test if data Normal, or Levene if any variables are non-Normal. Also known as Equal Variance."]
["Homoscedasticity","A process where variation remains constant with change in the input variables. \nSee also Heteroscedasticity."]
["Hypothesis Test","Statistical test showing whether data sets are from same statistical population. T-Tests can be used to test hypotheses relating to Continuous data. Chi-Square tests can be used on discrete data. See Null Hypothesis, Alternate Hypothesis.\nVariable Data:\n* Normal Data\n + Means Tests:       1 Sample t-Test,  2 Sample t-Test,  Paired t-Test\n                               General Linear Model (for >2 factors); Correlation; Regression\n + Variance Tests:    Chi2, 2 Variances,  ANOVA Equal Variances \n* Non-Normal Data\n + Means:Correlation,Sign Test,Wilcoxon,Mann-Whitney,Kruskal-Wallis,Mood's, Friedman. \n + Variance: ANOVA > Equal Variances\nAttribute Data: See Chi2 contingency tables, Proportion testing."]
["I","1. Individual data point(s). See I-Chart.\n2. Information Interface in FMEA Interface Matrix.\n3. The Improve phase of a DMAIC 6-Sigma project.\n4. The Identify phase of the SSA DfSS project methodology."]
["IC","Important Characteristic (quality/customer related characteristic)"]
["ICA","Interim Containment Action. Short term action to remove immediate risk or impact pending full long term solution. A verified refinement of the Emergency Response Action. Preceeds determination of the Permanent Corrective Action. Part of Global 8D step D3. See ERA, PCA."]
["ICCD","Intensified Customer Concern Definition - Global customer concerns database. 30 days in service telephone survey which asks in depth questions on specific concerns."]
["Ice Cube Chart","Alternative name for a Paynter Chart. Nomenclature: Chart looks like an ice-cube tray."]
["I-Chart","Run Chart of Individual data points. See also I-MR Chart, X-Bar Chart."]
["ICIS","In Control and In Specification. \nSee Out of Control, Control Limits, Specification Limits."]
["ICOS","In Control but Out of Specification. \nSee Out of Control, Control Limits, Specification Limits."]
["Ideal Function","The Response as function of the Signal. Y=bM. Where y=Response, M=Signal, b=Slope. If the signal and response are measured in energy units the ideally y=M. i.e. b=1."]
["Identify","The first phase of the SSA DfSS IDOV project methodology. \nIDENTIFY represents market needs, customer requirements (CTS), regulatory requirements, process optimization requirements, sub-system design requirements, etc. It can occur in a variety situations and phases. The data should be in a ranking format (Ordinal scale) to permit some cost and performance trade-off decisions to be made. \nProceeds the Design, Optimization & Verification phases. \nIn the Ford DfSS methodology the 1st step is Define and the 2nd step is Characterize."]
["IDOV","DfSS project phases: Identify, Design, Optimise, Verify. SSA definition of the DfSS project phases. See DCOV for Ford DfSS project phases."]
["iGrafx","Software package provided by MicroGrafx for process mapping. Also known as FlowCharter"]
["III","Resolution III. A Fractional factorial distinguishing only the main effects."]
["Importance","An input to Multiple Response Optimizer and Response Surface Method. \nDetermines the relative importance of multiple response variables. Often, there is no factor setting that simultaneously maximizes the desirability of the individual responses. That is why we maximize the composite desirability. The importance determines how much influence each response has on the composite desirability.\nSee also Multiple Response Optimizer, Desirability, Weight."]
["Improve","The Improve phase of a DMAIC 6-Sigma project. This includes: Performance objectives, identify alternate solutions, select optimal solution, update FMEA, cost/benefit analysis, pliot, implement & validate improvements."]
["Improve Phase","Discover variable relationships between x’s and y’s (e.g. DOE, regression)\nUtilize critical variables to achieve the desired objective such as reducing variation or shifting the mean \nEstablish operating tolerances (e.g. realistic tolerancing)"]
["I-MR Chart","The I-MR chart is a combined chart consisting of:\n· An Individuals (I) chart, which plots the values of each individual observation, and provides a means to assess process center.\n· A Moving Range (MR) chart, which plots the range calculated from artificial subgroups created from successive observations, and provides a means to assess process variation.\nUse an I-MR chart to draw a combined control chart for assessing whether process center and variation are in control when your data are individual observations. \nWhen subgroups are available use an X-Bar & R Chart."]
["In Control","A process is in control when its data falls within the upper and lower Control Limits. "]
["INDEPENDENT VARIABLE","A controlled variable; a variable whose value is independent of the value of another variable.See X's, Parameter."]
["Individuals Chart","A Run Chart of individual values. Often displayed with a Moving Range chart. The \"I\" in an IMR chart."]
["Infant Mortality","Early Life failures."]
["Inference Space","Part of DOE. The area within which you can draw your conclusions. Two classifications:  Broad and Narrow:-\nNarrow Inference: Experiment focused on specific subset of overall operation. Examples:  Only one shift, one operator, one machine, one batch, etc. Narrow inference studies are not as affected by Noise variables\nBroad Inference: Usually addresses entire process (all machines, all shifts, all operators, etc.). Generally, more data must be taken over a longer period of time. Broad Inference studies are affected by Noise variables"]
["Initial DPMO","The process DPMO currently. Also known as Baseline DPMO. See DPMO"]
["Inner Array","The Array of Control Factors & Levels used in Experimental Design & Paramater Design. See also the Outer Array where Noise factors are added. See also Balanced Array, Geometric Array, Non-Geometric Array, L8, L12."]
["Input Variables","See X's, Parameter."]
["INSTABILITY","Unnaturally large fluctuations in a pattern."]
["Inter Quartile Range","The data between the 1st and 3rd quartiles. Also known as IQR. Shown on Box Plots."]
["INTERACTION","When the effects of a factor A are not the same at all levels of another factor B. The tendency of two or more variables to produce an effect in combination which neither variable would produce if acting alone. If the Interaction Plot lines are close to parallel then there is NO significant interaction. Note: If 3-way interaction is significant then MTB cannot show 3-way interaction plot. To represent this unstack data and produce multiple interaction plots - See carpet1.mtw example file. Examples:\n2-Way: Sugar & Stirring effect on sweetness. Door & Farme size on goodness of fit.\n3-Way: Wet weather, Lawn mowing & Fire drill on grass on carpet!"]
["Intercept Coefficient","A parameter of the probability function for a Probit Reliability analysis. \nNoemclature: b0."]
["Interface Matrix","A Robustness tool providing input to the Design FMEA. A matrix of all sub-systems, showing the positive or negative effects of any interface between them. \nInterfaces are identified as: Physically touching (P), Energy Transfer (E), Information Exchange (I) or Material Exchange (M). \nInteraction values are set as: +2 = Interaction necessary for function; +1 = Beneficial; 0 = No effect; -1 negative effect; -2 Prevents function.\nThese 4 values are shown in a square for each interaction. See also P Diagram."]
["Interim Containment Action","See ICA."]
["Internal Validity","Part of ensuring DOE Validity. Randomization of experimental runs “spreads” the noise across the experiment\nBlocking insures Noise is part of the experiment and can be directly studied\nHolding Noise Variables constant eliminates the effect of that variable but limits Broad Inferences. See also External Validity.\n"]
["Interpersonal Relationships","The 4th element of the GRPI leadership strategy:\nINTERPERSONAL RELATIONSHIPS:\n* There is trust and openness in communication and relationships.\n* Time is taken to examine our process (how we are relating to each other, communicating, handling conflict, etc.) in order to improve the way we work. \n* Flexibility, sensitivity to the needs of others, and creativity are encouraged. \nSee also Goals, Roles & Responsibilities, Processes, 4P's."]
["INTERVAL","Numeric categories with equal units of measure but no absolute zero point, i.e., quality scale or index."]
["Interval Censoring","Same as Arbitrary Censoring."]
["Invariant","A factor which is unaffected by all the changes around it. I.e. one which demonstrates total homeostatsis / robustness. e.g. the speed of light or p!\nPart of the Viable System Model (VSM) developed by Stafford Beer. \nSee also Viable, Recursion, Self-Reference, Homeostasis, Invariant, P Diagram."]
["Inverted Delta","Inverted Delta. Classification of the Effect of a Failure Mode which is Critical to Quality, Safety or Regulator compliance. Nomenclature: Ñ"]
["Inviolable Elements","Those elements of the FPDS deliverables which are deemed to be most critical over and above the other mandatory elements. Cf. APQP Focusu Elements."]
["IPD","In Plant Date. Date of delivery of Production Parts to plant."]
["IPO","Inputs, Processes & Outputs. See also SIPOC."]
["IPV","Incidence in Percent of Vehicles. Same as %incidence. See also FPV."]
["IQA","Institute of Quality Assurance - \"the UK's leading body for the advancement\nof quality practices\""]
["IQR","Inter-Quartile Range. The difference between the 1st Quartile (Q1) and 3rd Quartile (Q3) values."]
["IQS","J.D. Powers Initial Quality Survey at 3MIS. Measured in PP100 faults per 100 not TGW/1000. Offers analysis on reported defects during the first 90 days of ownership, providing insight and information on 135 problem issues affecting vehicle quality across nine vehicle systems."]
["IQS2","Latest version of J.D. Powers Initial Quality Survey - See IQS"]
["IR","Improvement Ratio. IR = Benefit/Cost (CPU). \nIR>1.0 shows positive benefit. IR<1.0 reflects costs > benefits"]
["Is / Is Not","Defining the characteristics of the defective product/process and a similar non-defective product. Categorise factors as: IS, COMMON or IS NOT. Only those factoprs which are unique to the IS defective condition that can be the cause. The What, Where, When & How Big characteristics are recorded on sheet 1 or 4 of the Problem Solving Worksheet. This is then followed by the question: What's Changed? - See Something Changed."]
["Ishikawa Diagram","Same as Cause & Effect Diagram. See also Fish bone diagram, \"5M's and a P\". Also known as herring bone diagram"]
["ISO 9000","Series of Quality processes standards. \nEthos: Document what you do. Do what you've documented. Demonstrate that you do. \nImplemented in ISO 9001 & ISO 9002. Later known as QS-9000. Now being superceded by TS16949."]
["ISO 9001","Quality processes standard for product producers. \nEthos: Document what you do. Do what you've documented. Demonstrate that you do. \nLater known as QS-9000. Now being superceded by TS16949."]
["ISO 9002","Quality processes standard for service providers. \nEthos: Document what you do. Do what you've documented. Demonstrate that you do. \nLater known as QS-9000. Now being superceded by TS16949."]
["IV","1. Resolution IV. A Fractional factorial distinguishing main effects & 1st order interactions.\n2. Input Variable."]
["Jig","A device used to hold a part in place and to guide via bushings, dowels etc a drill / boring bar etc. in a specific relationship to it. \nSee also Fixture, Tool, Setting Aid, Gauge."]
["JIT","Just-In-Time manufacturing. A key element of Lean Manufactring. Full use of all the resources to produce the right product, at the right time, in the right quantities, based on customer demand. 1. Continuous one piece flow, 2. Kan Ban demand pull scheduling, 3. Simplify to get rid of waste"]
["Joiner Triangle","A 4th generation Quality System control management process. Consists of:\nQuality - Understanding that quality is defined by the customer; developing an obsession for delighting customers.  This understanding is no longer the special domain of special groups, it is shared with everyone.\nScientific Approach - Learning to manage the organization as a system, developing process thinking, basing decisions on data, and understanding variation.\nAll One Team - Believing in people; treating everyone in the organization with dignity, trust, and respect; working toward win-win instead of win-lose for all stakeholders (customers, employees, shareholders, suppliers, and our communities)"]
["K","1. The number of Factors in a 2K Factorial DOE. \n2. 1000's or multiple of 1024."]
["Kaizen","Kai=Change, Zen=For the Better. The philosophy of continual improvement, that every process can and should be continually evaluated and improved in terms of time required, resources used, resultant quality, and other aspects relevant to the process.\nRules: Keep an open mind to change, Maintain a positive attitude, Never leave in silent disagreement, Create a blameless environment, Practice mutual respect ever day, Treat others as you want to be treated, One person , one vote -- no position, no rank, No such thing as a dumb question, Understand the thought process and then Do It.\nElements: Takt Time, Cycle Time, Work Sequence, Standard WIP.\nSee also Lean Manufacturing=5S>Visual Factory>Standardized Work>Kaizen>KanBan."]
["Kan Ban","Kan Ban a Japanese word for communication signal or card. It is a technique used to “pull” products and material through and into the lean manufacturing system. The actual “Kan Ban” can be a physical signal such as an empty container or a small card. Kan Ban provides production conveyance, and delivery information.  In it’s purest form the system will not allow any goods to be moved within the facility without the appropriate Kan Ban (signal) attached to the goods.\nSee also Lean Manufacturing=5S>Visual Factory>Standardized Work>Kaizen>KanBan."]
["Kano Model","A graphical representation of quality achievement in the three categories of:\n- Musts: Basic Quality - CTQ's which must be satisfied.\n- Desires: Performance Quality - CTQ's where better performance gives more satisfaction\n- Delights: Excitement Quality - CTQ's which are over and above customer expectations\n                and which have a wholey positive effect when present.\n   J|/\n ---|-->\n  / | /"]
["KBE","Knowledge Base Engineering"]
["KCA","Knowledge Centred Activity - wisely obtaining and using knowledge of the organisation and its' processes"]
["KE","Kinetic Energy: 0.5mv2. (+ rotational energy Iw2). See also PE, Energy Transfer."]
["KPIV","Key Process Input Variables. Same as X's"]
["KPOV","Key Process Output Variables. Same as Y's"]
["Kruskal-Wallis Test ","Non-parametric test equivalent to ANOVA. Samples should have same shape and distribution. If not, then use Moods Median Test."]
["Krytox","Proprietary anti-squeak lubricant – used by some suppliers (e.g. Valeo on door handle) and by dealers to address S&R complaints. Tends to be a temporary solution as the treatment wears out with time"]
["Kurtosis","Kurtosis refers to how sharply peaked a distribution is. A kurtosis statistic is provided with the graphical summary:\n· Values close to 0 indicate normally peaked data\n· Negative values indicate a distribution that is flatter than normal\n· Positive values indicate a distribution with a sharper than normal peak"]
["l","1. Greek letter Lamda. \n2. Used in the Box Cox Transformation to achieve normality of data.\n3. In Reliability l = 1/MTBF."]
["L","Quality Loss Function. See also Average Quality Loss Function L-Bar.\nL(y)=k(y-t)2. Where: k is the coefficient of Loss, y is the current response, t is the target response."]
["L8","Latin Square orthogonal array for an 8 run experiment (And similarly for other numbers of runs). Array filled with the permutations of factors and levels for a factorial experiment."]
["Lambda","Greek letter Lambda; See l."]
["LB","Lower Bound. Same as the Lower Control Limit."]
["L-Bar","Average Quality Loss Function. Quality loss function plus effects of variation.\n`L(y)=k[s2y+(y-t)2]. Where: k is the coefficient of Loss, y is the current response, t is the target response, s2y is the variance of the response."]
["LCC","Life Cycle Costing"]
["LCL","Lower Control Limit"]
["LEA","Lead Engineering Activity"]
["Lean Manufacturing","Definition:  A systematic approach to manufacturing which based on the premise that anywhere work is being done, waste is being generated. A vehicle through which organizations can identify and reduce waste. A manufacturing methodology which will facilitate and foster a living quality system. The GOAL:  Total elimination of waste through: Defining Waste, Identifying the sources, Planning for Waste elimination, Establish “PERMANENT” control to prevent its reoccurrence.\nSee 5S>Visual Factory>Standardized Work>Kaizen>KanBan, TPS, Waste."]
["LET","Logic, Emotion & Threat: 3 key Negotiating leverage criteria. e.g.:\n\"The reasons to do this are A, B & C\"\n\"The [feeling related] consequences of doing this are …\"\n\"If you don't do this the consequences are …\""]
["Level","The value of a Factor. E.g. Temperature may have a high and a low setting. In this case the factor temperature has two levels.\nSee also Alpha Levels, Sigma Levels etc etc."]
["Level Average","Average of values for a given factor level."]
["Level Contribution","S/N or b contribution from a given factor level. Level Average - T-Bar"]
["Level of Control","1. Autonomic Operation\n2. Automatic Control e.g. Termostatic temperature control\n3. Self-Regulation i.e. Verification by operator\n4. Regulation by others"]
["Levene","A form of f-Test for > 2 samples of data. Used for showing equal variances for non-normal data. Use Bartlett for normal data."]
["LF","L to the F. No of Runs for a Full Factorial experiment = No Levels (L) to the power of no. Factors (F). See also 2K."]
["Likert Scale","Discrete ordinal measurement scale e.g. 5-Point Grading A-C, 7-Point numerical rating, Verbal rating (Poor, Good Excellent etc.)"]
["LINE CHARTS","Charts used to tract the performance without relationship to process capability of control limits."]
["Linear Regression","See Regression."]
["Linearity","A measure of the difference in Accuracy or Precision over the range of instrument capability. Measured as the gradient of a line through the averages. We seek for that line to be horizontal. Therefore Good data has a low (close to zero) measure of variance due to linearity. See also Bias"]
["Location","Reliability Testing: Generically, an average e.g the mean of a Normal Distribution. This mayl be a value other than the mean for non-normal distributions. See also Scale, Shape."]
["Long Term","1. Long Term Process Capability - Generally measured as Pp.\n2. Long Term Sigma Level - The Sigma Level of the Long Term Process Capability. "]
["Long Term Process Capability","Overall Process Capability between subgroups and over time. \nThis is the sum of the Within Capability and the Between Capability.\nGenerally measured as Pp/Cp, or, when taking account of how centred the process is, Ppk/Cpk. Min acceptable value: 1.33 or Sigma Level 4.\n(See Cp or Pp for explanation of terminology usage).\n\nData constitutes Long-term data where: it reflects the influence of random causes as well as assignable phenomena; is taken across a broad inference space; across many shifts of production; using many machines; with many operators; using many lots of raw material.\n\nAlso known as: Between Capability, Overall Process Capability, Actual Process Capability or the Capability Index. \nSee also Process Capability, Short Term Process Capability."]
["Long Term Sigma Level","Long Term Process Sigma Level capability. = Short Term Sigma Level - ZShift (typically 1.5). Nomenclature: ZLT. Note: In Excel ZLT = NORMSINV(1-DPO)"]
["Lot Rot","Product failures / deteriation pre-sales whilst on dealer lot."]
["LOWER CONTROL LIMIT","A horizontal dotted line plotted on a control chart which represents the lower process limit capabilities of a process. See Control Limit"]
["LOWESS","Locally Weighted Scatter plot Smoother. An algorithm for smoothing the graphical display of data. Generally helpful on gently sloping data."]
["LSL","Lower Specification Limit. The lower limit of customer acceptability of CTQ."]
["LST","Living Systems Theory of organisational behaviour by James Miller. See also VSM, Cybernetics."]
["LVT","Local Vehicle Teams"]
["m","See Population Mean"]
["M","1. Material Exchange interface in FMEA Interface Matrix.\n2. The Modulated (I.e. changeable) Signal in the Ideal Function. Y=bM.\n3. The Measure phase of a DMAIC 6-Sigma project."]
["Machine Cycle Time","Machine Cycle Time - The total time for a machine to finish one complete cycle.\nTogether with Operator Cycle Time contributes to total Cycle Time."]
["Magic Devices","Same as Particles. An SIT technique."]
["MAIC","The formally defined steps in the DMAIC process."]
["Main Effects","Main Effects plot of a Multi-Vari analysis. Shows the effects of each factor on the output. See also DOE, RSM."]
["Maintainability","Expectation of a product or machine being restored to full function in a specified period of time. Measured by MTTR. See also Availability."]
["Malcolm Baldrige","The Baldrige National Quality Program and the Malcolm Baldrige National Quality Award of the US National Institute of Standards and Technology (NIST). An endeavour to promote performance excellence among U.S. manufacturers, service companies, educational institutions, and health care providers; conducts outreach programs and recognizes performance excellence and quality achievement;"]
["Mallow's C-p Statistic","Mallow's C-p Statistic. A measure of bias vs. closeness of fit of subsets of variables in multiple regression. The lower the C-p the better.\nCp = (SSEp/MSEm) - (n - 2P) where:\nSSEp is SSE for the model with p parameters (including the intercept term)\nMSEm is the mean square error for the model with all m predictors\nP is the number of parameters in your model.\nIn general, we look for models where C-P  is small and close to P.  \ne.g. for a model with three predictors that includes the constant (or intercept), you would like for a model with C-P  close to four.\n"]
["Mann-Whitney Test ","Non-parametric test equivalent to 2 Sample t-Test. Samples should have same shape and distribution."]
["MANOVA","Multi-variate ANalysis Of Variariances. See ANOVA."]
["Master Back Belt","An advanced expert in 6-Sigma methodologies. Trains, mentors and certifies Black Belts"]
["Master Value","The reference standard used to represent the True Value. E.g. the National Physics Laboratory official 1 metre rule; or a master gauge."]
["Material Yield","Goods in / Goods out. Measurable of scrap, but not rework"]
["MBWA","Management By Walking About. Visual inspection of product, process, site, defects to uncover potential causes."]
["Mean","Arithmetic average i.e. sum / number. Nomenclature: m for sample mean and X-Bar for population mean. See also sample mean, population mean, Total Mean, Mode, Median"]
["Mean Life","Average product life before failure. \nSame as Mean Time Before / To Failure (MTTF / MTBF). See also MLR."]
["Mean Square","A measure of Variance. Calculated as the Sum of Squares (SS) divided by Degrees of Freedom"]
["MEARS","Mechanized Appropriation Request System"]
["Measure","The Measure phase of a DMAIC 6-Sigma project. This includes: Identify KPIV's, develop data collection plan, MSA/gauge R&R, FMEA, Baseline DPMO/Sigma Level and data collection."]
["Measure Phase","Document the existing process (using a Process map, C&E Matrix, and an FMEA)\nIdentify key output variables (Y’s) and key input variables (X’s)\nEstablish a data collection system for your X’s and Y’s if one does not exist to determine the performance baseline\nEvaluate measurement system for each key output variable (e.g. Gage R&R)"]
["Measurement Capability Index","Measurement system study variation (sMS) divided by total variation (sTOTAL). \nAssesses data relative to total sample data spread irrespective of spec limits.\nRange: <10% = good, 10-20% = Acceptable, 20-30% = Marginal. \nAlso known as: P/TV, %R&R, %Study Tolerance, %SV, Study Variation, Measruement Capability Index."]
["Measurement System","Measurement System - Any method of measuring values relative toi the Operational Definition. Analysis of MS should lead to creative continuous measurement of CTQ's"]
["Measurement System Analysis","See Gage R & R"]
["Measurement System Evaluation","Same as Measurement System Analysis. See Gage R & R."]
["Measurement System Mean","Accuracy / mean of the measurement system. Nomenclature mmeasurement. See also Product Mean, Total Mean"]
["Measurement System Variance","Measurement System Variance (MSV). The variance within or introduced by the measurement system. Consists of the repeatability variance plus the reproducability variance. \nAs opposed to the True Variance of the defect itself. See also Total Variance.\nMSV is also the QS9000 term for the whole Gage R&R process.\n"]
["Median","Middle value in sample. See also Mean, Mode"]
["Median Rank","Adjusting averaged values to reflect point on curve / sequence. Used on sequenced sample to reflect representative proportion of population represented by the sample. May be approximated by (J-0.3)/(N+0.4) where J is the ranking of the test and N is the sample size. Usually expressed as a %. Used in Weibull Analysis."]
["MEPEM","Cause factors: Materials, Equipment, People, Environment, Methods. \nAn alternative representation of 5M's & a P which additionally categorises Measurement System related failures. See also Process Block Diagram."]
["Method of Steepest Ascent","A procedure for moving sequentially along the direction of the maximum increase (steepest ascent) or maximum decrease (steepest descent) of the response variable using the following first order model: Y (predicted) = b0 + S bi Xi"]
["Method of Steepest Descent","Same as Method of Steepest Ascent."]
["MFMEA","Machinery FMEA. Application of FMEA to the design and development of new plant machinery. See also DFMEA, PFMEA, CFMEA."]
["MH & PE","Material Handling & Package Engineering. Engineering of physical delivery packaging & logistics."]
["MicroGrafx","Supplier of iGrafx Flowcharter process mapping software. Previously known as ABC Flowcharter"]
["MIL","Malfunction Indicator Light"]
["MiniTab","Software package for statistical analysis"]
["MIS","Months In Service. See also TIS, YIS, MPP."]
["Mistake Proofing","Ensuring benefits introduced are sustained by ongoing control methods. Methods to prevent Errors and, therefore, Defects. See also Poka-Yoke."]
["MIXED EFFECTS MODEL","Contain elements of both the fixed and random effects models."]
["ML","Mean Life. Average product life before failure. \nSame as Mean Time Before / To Failure (MTTF / MTBF). See also MLR."]
["MLR","Mean Life Ratio. The ratio of the MTBF's of two samples in a Weibull Analysis."]
["mmeasurement","See Measurement System Mean"]
["Mode","Most frequently occuring value in sample. Nomenclature: ^. See also Mean, Median, Failure Mode."]
["Moment Of Truth","Point of customer contact with product / process. Initial point of formulation of positive or negative impression."]
["Moods Median Test ","Non-parametric test equivalent to ANOVA. Used in lieu of Kruskal-Wallis Test where samples have different shape or distribution."]
["MOP","Month Of Production"]
["Moving Average","A control chart which smoothes data to emphasize trends. Each point includes effect of current value plus a specified number of past points. Assumes past and present data equally important."]
["Moving Range","In an I & MR Chart the Moving Range is a chart of the difference between each value and the next."]
["MPP","Months Past Production. A BMW, and hence, Land Rover, measure of Time In Service. See also MIS."]
["mproduct","See Product Mean"]
["MR","Moving Range chart. See I-MR"]
["MR Chart","Moving Range Chart. See I-MR Chart."]
["MS","1. Measurement System\n2. Mean Square"]
["MS Variance","See Measurement System Variance"]
["MS-9000","Materials management Systems requirement. Ford supplementary guideline to QS-9000, to suppliers on material management / shipment packaging engineering responsibilities."]
["MSA","Measurement System Analysis. See Measurement System, Gage R & R"]
["MSE","1. Measurement System Evaluation. See MSA.\n2. Mean Square Error. The mean of the squares of the residuals."]
["MSV","MSV is the QS9000 term for Gage R&R. See Measurement System Variance. "]
["MTBF","1. Mean Time Before Failure. Same as MTTF & ML.\n2. Mean Time Between Failures. MTBF=t*U/F Where t = time e.g. No Days, U=Units e.g. No Parts & F = No Failures. (Note: MTBF is not a Minitab supported measure). "]
["mtotal","See Total Mean"]
["MTTF","Mean Time To Failure. The average life of the product. Also known as Mean Life (ML)."]
["MTTR","Mean Time To Repair. The measure of Maintainability. \nSee also MTBF, MTTF, Availability."]
["Mu","Greek Letter m."]
["Multicollinearity","Multicollinearity occurs when two or more predictor variables are highly correlated with one another.  For example, say we are trying to use the number of hours of sunshine and the amount of rainfall to predict the yield of a particular crop.  Since hours of sunshine will decrease as the amount of rainfall increases, these two variables will be correlated.  Using both variables in a regression model will result in what is known as multicollinearity. Note: Collinearity and multicollinearity are used synomymously. See also VIF."]
["Multiple Response Optimizer","A Minitab tool to estimate optimal input settings to achieve target/min/max outpuit variable values. See also Desirability, Weight, Importance."]
["Multi-Vari","Multi-Vari analysis: A graphical tool which, through logical sub-grouping, analyzes the effects of categorical X’s on continuous Y’s.  A great graphical tool to help visual how various X’s (both controllable and noise) impact our response Y. A technique to reduce the number of process input variables (x’s) to a manageable number of critical variables Part of the Analyse project phase. Where data is continuous more specific correlation techniques can be applied - see Correlation and Multivariate Regression.See also ANOVA, Hypothesis Testing, Main Effects."]
["Multi-Variant Regression","A multivariate regression model is a functional representation of the relationship between a response variable and several predictors. When there is one response and one factor, the regression model simply represents a line in a two-dimensional space. When there is one response and two factors, the regression model can be graphed by a curve in two-dimensions. When there is one response and two-factors, the regression model can be graphed by a curve in three-dimensions. See also Multicollinearity, VIF.\nSee also Multi-Vari for mapping categorical variables"]
["MY","Model Year"]
["n","Greek letter Nu. Degrees of Freedom."]
["n","Sample size"]
["N","1. Noise.\n2. Population Size."]
["Ñ","Inverted Delta. Classification of the Effect of a Failure Mode which is Critical to Quality, Safety or Regulator compliance."]
["National Quality Council","A USA quality institution."]
["Natural Response Rate","A parameter of the probability function for a Probit Reliability analysis. \nNoemclature: C."]
["Never Been There","1.5% of problems where the cause has been there all along but has only now been detected. Also  known as Day 1 Deviation. See also Something Changed problems."]
["NIH","\"Not Invented Here\". An key aversion to change factor."]
["NIST","US National Institute of Standards and Technology. Founded in 1901, NIST is a non-regulatory federal agency within the U.S. Commerce Department's Technology Administration. NIST's mission is to develop and promote measurements, standards, and technology to enhance productivity, facilitate trade, and improve the quality of life. See also Malcolm Baldrige National Quality Award."]
["Noise","Factors affecting responses that are difficult, impossible, or expensive to control (e.g. ambient temperature or humidity, operator training). Inner Noises: part-to-part variation & variation over time. Outer Noises: Conditions of use & Operating Environment.\nAlso known as Uncontrollable Variables. See also X's. "]
["Noise Control","A prime objective of Parameter Design which seeks to optimise factors so as to maximise resilience to noise. "]
["NOMINAL","Unordered categories which indicate membership or non-membership with no implication of quantity, i.e., assembly area number one, part numbers, etc."]
["Nominal ","Desired Engineering location within specification."]
["Nominal Group Technique","See NGT"]
["NONCONFORMING UNIT","A unit which does not conform to one or more specifications, standards, and/or requirements."]
["NONCONFORMITY","A condition within a unit which does not conform to some specific specification, standard, and/or requirement; often referred to as a defect; any given nonconforming unit can have the potential for more than one nonconformity."]
["Non-Geometric Array","A Balanced Array for a Factorial Experiement where: the product of each pair of columns is not another column in the table. E.g. L12. Non-Geometric arrays spread the confounding of  each 2-way & many-way interaction across many individual Main Effects factors/columns. See also Geometric Array."]
["Nonparametric","Data or tests which do not assume any specific distribution. \nSee Non-Parametric Distribution, Non-Parametric test."]
["Non-Parametric","Data or tests which do not assume any specific distribution. \nSee Non-Parametric Distribution, Non-Parametric test."]
["Non-Parametric Distribution","Reliability analysis where the data distribution is not assumed or cannot be established. \nIn contrast to Parametric Distribution tools which are used where the data distribution has been determined to be e.g. Normal."]
["Non-Parametric Test ","A statistical test applicable to data demonstrating non-normal distribution."]
["Non-Value added step","A Process Map step which is not an operation which transforms the product in a way that is meaningful to the customer. A process step for which the customer would not be willing to pay."]
["Normal Distribution","A continuous, symmetrical density function characterized by a bell-shaped curve. e.g., distribution of sampling averages. Fully defined by the Mean and the Standard Deviation. Non-normal distributions may be transformed to normality by the application of e.g. the Box Cox Transformation."]
["Normalised Yield","Average process Yield. Commonally calculated as DPO. Can also be calculated as the nth root of RTY, where n = # process steps (i.e. nÖYRT)"]
["Norming","Stage 3 in Tuckman's model of team development. The process of coming together as a team, building relationships and establishing an effective foundation. Sperad the word on the team's progress. This is the springboard for Performing."]
["NOVA-C","New Overall Vehicle Assurance-Concern. Vehicle quality / defect measurement system. Now replaced by FCPA."]
["np","Number of Parts defective. See np Chart."]
["np Chart","An attribute Run Chart for measuring Defectives. A simple chart used to track the number of nonconforming units (percentage of defective parts) assuming the sample size is constant. See also p Chart."]
["NQC","National Quality Council - a USA quality institution."]
["Nu","Greek letter Nu: n. Degrees of Freedom."]
["Null Hypothesis","The basic proposal of the status quo, no effect - that nothing has changed. The explanation which indicated that a chance distribution is operating; \nNomenclature: H0. In contrast to the Alternative Hypothesis"]
["N-VA","Non-Value Added. A Non-Value Added process step"]
["O","The Optimize phase of a DfSS 6-Sigma project."]
["Ö","Square Root. Öx means the number which multipled by itself equals x"]
["OBD","On Board Diagnostics"]
["Objectives","Targets that have been verified through the Systems Engineering target cascade process."]
["Occurrence","See O Level, FMEA, Criticality."]
["OCIS","Out of Control but In Specification.\nSee Out of Control, Control Limits, Specification Limits."]
["OCOS","Out of Control and Out of Specification. \nSee Out of Control, Control Limits, Specification Limits."]
["ODI","NHTSA Office of Defects Investigation Consumer Complaints Database"]
["OE","OE documents (Acronym for ???) - Process Instructions, SOP, QPS etc."]
["OEE","See Overall Equipment Effectiveness."]
["OFAT","One Factor At a Time. See DOE."]
["O-Level","FMEA Occurrence Level. Rated 1-10 by the Big 3 for Remote, Low, Moderate, High probability of failure. An assessment of the frequency with which the failure cause occurs.  “How often does this X fail in a specific way”? See also Criticality."]
["OMS","Operational Method Sheets. Standard Operating Procedures (SOP's), Process Instructions. Quality Process Sheet (QPS). See also Quality System, SPC, Poka-Yoke..."]
["ONE-SIDED ALTERNATIVE","The value of a parameter which has an upper bound and a lower bound, but not both."]
["OOC","Out of control (see out of control guidelines)"]
["OOS","Out of specification (measurement exceeding Engineering specification)"]
["Operational Definition","The precise description that defines the Defect free condition. Defines the defect free product in specific and concrete criteria. Defines how to measure those criteria (CTQ's)"]
["Operational Method Sheets","Operational Method Sheets. Standard Operating Procedures (SOP's), Process Instructions. Quality Process Sheet (QPS). See also Quality System, SPC, Poka-Yoke..."]
["Operator Cycle Time","Operator Cycle Time - The total time required for a worker to complete once cycle of an operation. Includes:  Manual Operations, Walking, Inspecting, Loading and machines and gauging.  Does not include waiting for the equipment/machine auto cycle to finish.\nTogether with Machine Cycle Time contributes to total Cycle Time.\n"]
["OPI","Operator Process Instructions"]
["Opportuity Count","Count of the total number of opportunities for defects from the process. A measure of process complexity"]
["Opportunity","Opportunity for a defect. Any event which can be measured that provides a chance of not meeting a CTQ customer requirement. Number of ways you may not meet the customer requirements (CTQ's). I.e. multiple opportunities for defects per unit produced. E.g. length, weight & material may all be opportunities for defects on a component. See also Defect."]
["Optimise","See Optimize."]
["Optimize","1. The Improve & Control phases of a 6-Sigma DMAIC project. Focuses on X's - inputs.  See also Characterise. \n2. Minitab Response Optimizer.\n3. The Optimize of a DfSS 6-Sigma project. In this step we\nUnderstand capability and stability of present processes\nUnderstand the high time-in-service robustness of the present product\nMinimize product sensitivity to noise, as required\nMinimize process sensitivity to product and manufacturing variations, as required\n"]
["Order","1. The no. of factors interacting e.g. factors A*B*C interacting is a 3rd order interaction.\n2. See Value Ordering, Standard Order, Randomised Order."]
["ORDINAL","Ordered categories (ranking) with no information about distance between each category, i.e., rank ordering of several measurements of a output parameter."]
["ORDINATE","The vertical axis of a graph.See also Abscissa"]
["ORT","Ongoing Reliability Testing"]
["Orthogonal","Same number of replications of each level of each factor and independence of variability of factors. Minitab always generates orthogonal designs of experiment."]
["OS","Operator Safety. An additional Critical Characteristic classification in Power Train FMEA's.See also YC, YS, HI."]
["OSNA","Out Side North America - All markets except North America. See also NA"]
["OTA","Off Track Area. Production area off the main assembly line. Often for rectification work. See OTR."]
["OTJ","On The Job training."]
["OTR","Off Track Recification. A production area off the main assembly line for product rectification work. See also OTA."]
["Out Of Control","A process is in control when some of its data falls outside of the upper and lower Control Limits. "]
["Outer Array","The Array of significant Noise Factors & Levels used in Experimental Design & Paramater Design. These are added to the Inner Array where Control factors are stored. See also Balanced Array, Geometric Array, Non-Geometric Array, L8, L12."]
["Outlier","An extreme data point. Shown on Box Plots as an asterix (*) beyond the end of the whisker when the whisker is longer than 1.5 times the inter-quartile range (IQR)."]
["Output Variables","See Y's"]
["Overall Capability","Overall Long term process capability across subgroups. \nThis is the sum of the Within Capability and the Between Capability."]
["Overall Equipment Effectiveness","Availability x Perfomance Efficiency x Quality Yield."]
["P","1. Probability - liklihood of occurrence. 0=never, 1=Certain. See also P-Value\n2. Proportion Defective - P = Population percent, p = Sample percent. \n      See also p Chart.\n3. Gage Contribution.\n4. Parameter - See P Diagram.\n5. Preventative Control - See FMEA.\n6. Physically touching interface in FMEA Interface Matrix."]
["p","1. The greek letter Pi.\n2. The ratio of the circumferance of a circle to its diameter. p = 3.14159265358979..."]
["p Chart","An attribute Run Chart for measuring Proportion of Defectives. A simple chart used to track the proportion of defectives per lot or subgroup. p Charts can be used when the sample size is either constant or not constant. More flexible than np charts. Use if sample size is variable. Can also be used to compare two or more similar processes. \nSee also np Chart. Note. Nothing to do with P Diagram."]
["P Diagram","Parameter Diagram of a system. Mapping Inputs (Signals), Outputs (Responses & Errors) and Parameters - Noises and Controllable factors for a system. An extension of a process block diagram to study and improve robustness.\n             _v_Noises_v_\nSignal->|___Process__|-->Intended Responses\n                ^ Controls ^  \\->Error States\nNote: Nothing to do with p Charts, Pareto Diagrams. \nSee also Process Block Diagram, Static System, Dynamic System, VSM."]
["P to T","See P/T"]
["P to TV","See P/TV"]
["P/T","Precision-to-Tolerance Ratio. “P to T” is used to qualify a measurement system as capable of measuring to a given product specification. P/T = 5.15 * sMS / Tolerance\nRange: <10% = good, 10-20% = Acceptable, 20-30% = Marginal. \nAlso known as: %Tolerance, P/T, SV/Toler, Tolerance Ratio.See also Gage R & R."]
["P/T Ratio","See P/T"]
["P/TV","Precision to product/process Total Variation.\nMeasurement system study variation (sMS) divided by total variation (sTOTAL). \nAssesses data relative to total sample data spread irrespective of spec limits.\nRange: <10% = good, 10-20% = Acceptable, 20-30% = Marginal. \nAlso known as: P/TV, %R&R, %Study Tolerance, %SV, Study Variation, Measruement Capability Index."]
["P2P","Piece-to-Piece or Part-to-Part variation."]
["Packaging","1. Product design of the packaging of systems and sub-systems within the vehicle.\n2. Design of the shipment packaging and stillages for delivery of parts to plant."]
["Paired t-Test","A t-Test where there is a known relationship between equivalent samples from a two sample survey."]
["Parameter","1. A constant defining a particular property of the density function of a variable.\n2. A factor, X, Input or Variable (Note 6-Sigma does not generally use this term) \n    - See P Diagram."]
["Parameter Design","Improving Robustness by making changes to parameters that can be changed easily and cheaply. Parameter Design optimises performance by identifying which Noise factors do/don't interact with the Control Factors and optimising these. An extension of Experimental Design. One of the ideas implemented by Dr. Genichi Taguchi. Should be pursued before considering Tolerance Design. Methodology:            \n1. Define project & Team  2. Formulate Engeneered System:- Ideal Function     3. - Parameters\n4. Assign Control Factors to Inner Array       5. Assign Noise Factors to Outer Array.\n6. Measure: Conduct experiment & collect data.           7. Analyse data & select optimal design \n8. Predict Improvement & Confirm (... Control)"]
["Parametric","Data or tests which are based on given distribution - usually the Normal Distribution.\nSee also Non-Parametric Distribution, Non-Parametric test."]
["Parametric Distribution","Reliability analysis having established the data distribution e.g. Normal. \nIn contrast to Non-Parametric Distribution tools which are used where the data distribution cannot be determined."]
["Parametric Test ","A statistical test applicable to data demonstrating normal distribution."]
["Parato","See Parato Diagram"]
["Parato Analysis","See Parato Diagram"]
["Parato Chart","See Parato Diagram"]
["PARETO DIAGRAM","A chart which ranks, or places in order, common occurrences. Used to break a big problem down to its' major parts and causes. May be applied on a strict 80:20 basis, or more generically to highlight highest impact/priority items."]
["Part Contribution","Contribution (proportion) of variation attributable to the part, product or process. \nSee also Gage R & R "]
["Particle","Magic' devices invoked to find conceptual problem solutions. Used in the SIT Particle Method. Helps capture vague ideas. Supports the thinking process that Function Follows Form"]
["Particles Method","An element of Structured Inventive Thinking (SIT). Applying Particles to Initial & Final States, before drawining And/Or Tree to identify the necessaryproperties of the things that can be used to solve the problem. Used before applying the SIT solution techniques of Uniqueness, Dimensionality, Pluralization & Redistribution. Used in preference to the Closed World Method where there is free choice of solution technology rather than an existing solution to improve. The particles method requires some understanding of how to go about identifying the solution. Where this is not the case, the Closed World Method may be more effective."]
["Partitioning","Systems Engineering process of clarifying interfaces between functions."]
["Paynter Chart","A matrix of problems / faults / failure types vs occurrence e.g. weeks/months. On this Emergercy Response Actions (ERA), Interim Containment Actions (ICA), & Permanent Corrective Actions (PCA) and their effects can be marked.\nNomenclature: Named after its' originator, Marv Paynter, Ford, Dearborn."]
["PBT","Profit Before Tax"]
["PC","1. Project Champion.\n2. Project Charter.\n3. Personal Computer."]
["PCA","Permanent Corrective Action. Action which resolves the root cause of the problem. Part of Global 8D step D5/6. See also ERA, ICA."]
["PCC","Proactive Customer Call. A form of customer satisfaction quality survey. See also GQRS."]
["PCM","Process Control Methods - See SPC."]
["PCP","Production Control Plan."]
["PDCA","Plan Do Check Act. See Plan Do Study Act."]
["PDF","See Probability Density Function."]
["PDG","See 6-Sigma"]
["PDI","Pre-Delivery Inspection. See also PoE."]
["P-Diagram","See P Digram."]
["PDSA","See Plan Do Study Act."]
["PE","Potential Energy: PE = mgh. See also KE, Energy Transfer."]
["Pearson","A correlation coefficient from the Pearson regression method. See Correlation. Nomenclatrue: R."]
["Performance Capability","Same as Short Term Process Capability. Best Potential process capability within subgroups. Also known as Within Capability, Process Entitlement, Preliminary Process Capability, Performance Index or Process Potential.\nSee also Between, Overall Capability."]
["Performance Efficiency","Ideal Cycle Time / Actual Cycle Time.\nSee also OEE, Availability, Quality Yield."]
["Performance Index","Same as Short Term Process Capability. Best Potential process capability within subgroups. Also known as Within Capability, Process Entitlement, Preliminary Process Capability or Process Potential.\nSee also Between, Overall Capability."]
["Performance Quality","Desires: CTQ's where better performance gives more satisfaction. \nPart of the Kano Model. See also Basic Quality, Excitement Quality."]
["Performing","Stage 4 in Tuckman's model of team development. The exercising of effective team roles with energy focused collaboratively on achievement of the goals. Pay attention to detail, to planning towards effective completion. Build stakeholder support for implementation."]
["Permanent Corrective Action","See PCA."]
["PERTURBATION","A non-random disturbance."]
["PFMEA","Process FMEA. Application of FMEA to assess manufacturing process relaibility. See also DFMEA."]
["PI","1. Product Investigation - The department which gathers and processes AWS information. Provides Jaguar interface to dealers on problem solving. Interprets service part numbers. Land Rover Single Product Investigation Team.\n2. Prediction Interval"]
["Pilot","A small scale test of a solution to learn how to acjieve an effective full scale implementation"]
["PIPC","Percentage of Indices which are Process Capable. Part of PPAP. See also PIST."]
["Pipe","The Pipe Symbol is used in Minitab to indicate all permutaions of factors.\nNomenclature: |"]
["PIST","Percentage of Inspection Points which satisfy tolerance. = No of measured characteristics / Total no characteristics to be measured * 100. Part of PPAP. See also PIPC."]
["Plan Do Study Act","The Deming-Shewhart Cycle. Plan to achieve objectives. Do: Execute the Plan. \nStudy: Analyse the results to determine next course of Action."]
["PLCP","Pre-Launch Control Plan"]
["PLEX","PLant EXperimentation. A process improvement tool for on-line use in full-scale production. Uses simple factorial designs, two-level designs in two or three factors. Usually, requires several iterations of experimental design, analysis, and interim improvements. Goal is to minimize disruption to production, but make big enough changes to quickly see effects on output variables. See also EVOP."]
["Pluralization","An Structured Inventive Thinking solution technique. Follows use of the Closed World Method or the Particles Method. Used with the other SIT solution techniques of Uniqueness, Dimensionality & Redistribution."]
["PM","Preventative Maintenance"]
["PMIE","Physically touching (P), Energy Transfer (E), Information Exchange (I) & Material Exchange (M) interfaces in FMEA Interface Matrix."]
["PND","Program Need Date. The last possible date the element can be completed without adversely affecting quality, cost or time to the program. NB: target dates should be earlier than PND in order to manage risk. Part of APQP Status Reporting."]
["PoE","Port Of Entry - Point of entry into overseas territory following shipment from plant. Another potential measurement point in the total order fulfilment process. See also PDI"]
["POFD","Point Of Fit Delivery – delivery of parts/assemblies to point of fit on track."]
["Poka-Yoke","To avoid (yokeru) inadvertent errors (poka). A mistake proofing method. Ensuring benefits introduced are sustained by ongoing control methods. Developed by Shigeo Shingo to achieve zero  defects. Multiple ingenious devices to prevent the processing of bad parts or materials. Human errors are inadvertent - Think of potential ways to “Mistake Proof” the process with a Poka-Yoke device. Helps build quality into the product. Allows only good product to go to the next operator or customer. Can’t build it wrong ... Can only build one way ... If wrong, detected 100% ... Can’t be built into next assy. Strategies: \nGood: Detects defects before they continue to the next operation\nBetter:  Allows for the detection of errors during processing\nBest Strategy:  Makes occurrence of errors impossible"]
["Pooled Standard Deviation","A weighted average of the standard deviation of each subgroup. The pooled standard deviation provides an overall measure of the average spread of individual data points about their subgroup mean. Used as Gage R&R Reppeatability measure.\nE.g. The average std dev of the following samples is the same a \"I\" rather than wider:-\n   |  I       I\n   |-----I---------\n   |    I         I"]
["POPULATION","A group of similar items from which a sample is drawn.  Often referred to as the universe."]
["Population Mean","Average (Sum divided by the population size) across entire population. Nomenclature: m (Greek letter Mu). See also Sample Mean"]
["Potential Capability","Same as Short Term Process Capability.\nBest Potential process capability within subgroups. \nAlso known as Short Term, or Within Capability. See also Between, Overall Capability."]
["Power","The Power of an experiment is the probability of rejecting the null hypothesis when it is false and accepting the alternate hypothesis when it is true. = 1 - b Risk. Power & Sample Size Proportion Test shows Beta risk. Use Proportion Test to show the Alpha likelihood. "]
["Pp","A measure of Process Capability reflecting how good the process would be, if it were centred on the target. It is ambiguously used for: \n1. Long Term Process Capability (6-Sigma & MiniTab). \n2. Short Term Process Capability i.e. Preliminary or Potential Process capability. (Ford & Big 3 SPC Reference Manual). \n\nPp takes no account of whether the process is centred. Ppk takes this into account.\nPp = (USL-LSL) / 6*s.                    See also Ppk, Cp, Cpk."]
["PP100","Problems Per 100 Vehicles - J.D. Powers TGW analysis unit of customer surveyed problems reported per 100 vehicles. Note: J.D. Powers measures per 100 in contrast to other TGW measures which are per 1000."]
["Ppk","A measure of Process Capability which takes account of both the spread and centering of the data. It is ambiguously used for: \n1. Long Term (6-Sigma & MiniTab) and 2. Short Term (Ford SPC Ref Manual). \n\nPpk equals the minimum of PPU & PPL. This is the shortest distance between the mean and the nearest spec limit. If the mean is outside both spec limits Ppk becomes negative but Pp is unaffected. Ppk approximates to Ppk = Z / 3.\n\nPpk=min(Ppk(USL),Ppk(LSL))   Ppk(USL)=(USL-Xbar)/3s   Ppk(LSL)=(Xbar-LSL)/3s\nPpk approximates to Ppk = Z / 3 so Ppk=1.5 equivalent to Sigma level of 4.5 or Z 6\nSee also CP, PP, Cpk, Sigma Level"]
["PPL","LSL Ppk. Also shown as Ppk(LSL)=(Xbar-LSL)/3s.   See also Ppk"]
["PPM","Parts Per Million defective. E.g. if an assembly which has 40 parts and 4 process steps, has, on average has one defect. This equates to 1,000,000/160 = 6,210 PPM. Same as DPMO (?)"]
["PPU","USL Ppk. Also shown as Ppk(USL)=(USL-Xbar)/3s.   See also Ppk"]
["PQR","Plant Quality Review - Weekly or Monthly review of top plant quality issues"]
["PR","Performance Ratio. The reciprocal of Pp.  I.E. 1/Pp"]
["Precision","Spread / variance of values. Improved through addressing Repeatability & Reproducability. See Gauge R&R. See also Accuracy."]
["Pre-Control","A Red-Yellow-Green Traffic light process which can flag up data approaching the control limits so as to help keep processes cntred and in control. See SSA Navigator for details."]
["Prediction Interval","The prediction bands (or prediction intervals, PI) illustrate the range of likely values for new observations. They represent a series of prediction intervals that span the range of observed density values. \nPI = confidence that a point will fall in the range. \nCI = confednce the mean will fall in the range. See also Confidence Interval(CI)"]
["Preliminary","First time assessment of a process/operation over limited data or time period. \nSee Pp, Ppk."]
["Preliminary Performance","Same as Short Term Process Capability. Best Potential process capability within subgroups. Also known as Within Capability, Process Entitlement, Preliminary Process Capability, Performance Index or Process Potential.\nSee also Between, Overall Capability."]
["Preliminary Process Capability","First time assessment of a process/operation over limited data or time period. \nSame as Short Term Process Capability. Also known as Within Capability, Potential Capability, Performance Index, Process Potential, Process Entitlement."]
["Premature Failures","See Wear Out Failures."]
["PREVENTION","The practice of eliminating unwanted variation of priori (before the fact), e.g., predicting a future condition from a control chart and when applying corrective action before the predicted event transpires."]
["PRIMARY CONTROL VARIABLES","The major independent variables used in the experiment."]
["Probabilistic Optimisation","Selecting optimal x value which provides maximum worst case Y response having giben consideration to to the variability of x. I,e, select so as to maximise Y=f(x) for all x +/- n-Sigma."]
["PROBABILITY","The chance of something happening; the percent or number of occurrences over a large number of trials."]
["Probability Density Function.","One of several functions that can be used to define a distribution. The distribution of failures over time. It indicates when a new item is likely to fail. Also known as PDF. Can also be expressed as a CDF - Cumulative Distribution Function."]
["PROBABILITY OF AN EVENT","The number of successful events divided by the total numbers of trials."]
["Probability Paper","A linear representation of cumulative normal distribution. A visual way of identifying % out of spec from sample data; and of calculating Cp & Cpk."]
["Probe","1 MIS customer satisfaction telephone survey"]
["Probit Analysis","Regression analysis which examines the relationship between a binomial response variable (I.e. two possible outcomes, success & failure) and a continuos stress variable.\nP = C + (1-C)*F(b0 + b1*Stress) Where C = the natural response rate, b0 is the intercept coefficient and b1 is the stress coefficient."]
["PROBLEM","A deviation from a specified standards."]
["Problem","1. An independant variable. See X's\n2. A Defect"]
["PROBLEM SOLVING","A process of solving problems; the isolation and control of those conditions which generate or facilitate the creation of undesirable symptoms."]
["Problem Solving Worksheet","A 4 page spreadsheet used in the Global 8D problem solving process. \nSheet 1: The IS/IS NOT problem description completed in step D2. \nSheet 2: The Comparative Analysis of Differences & Changes between the IS & IS NOT conditions. Sheets 2-4 are completed in step D4.\nSheet 3: Theorise Possible Causes: How could \"this change\" cause \"the problem stmt\".\nSheet 4: Test causes for probability (+, -, ?)."]
["PROCESS","A particular method of doing something, generally involving a number of steps or operations.May also ambiguously be used to reference the data resulting from a process. May be represented by a P Diagram."]
["PROCESS AVERAGE","The central tendency of a given process characteristic across a given amount of time or a specific point in time."]
["Process Block Diagram","Process modelling. A graphical representation of a process. Mapping Inputs (The Materials of MEPEM), Outputs (Good & Bad product plus Process & Control Information) and Controls (The Methods of MEPEM) and Resources (The Equipment, People & Environment of MEPEM).\n             _v_Controls_v_\nInputs->|___Process___|->Outputs\n               ^ Resources^  \nNote: Nothing to do with p Charts. Confusiningly similar to p Diagrams.\nSee also MEPEM."]
["Process Boundary","In Process mapping, the start and end point of the process being mapped."]
["Process Capability","The measure the capability of a process to meet its' requirements (specification).\nThere are a variety of measures and a diversity of terminology applied to these:\n \nThe capability may be estimated from the data within a single sample. This is referred to as the Within Capability. Also known as the Short Term or Potential Process Capability, the Process Entitlement, Preliminary Process Capability, Performance Index or the Process Potential.\n\nWhere the capability is determined from data between many samples over a longer period of time it is referred to as the Between Capability. Also known as the Long Term or Overall Process Capability or the Capability Index.\n \nThe indexes of capability may be quoted irrespective of process centering. These are the Cp & Pp indexes. The indexes of process capability which reflect the off-centre measure of the data are the Cpk & Ppk indexes.\n\nThe units of measure of these indices are Sigma Levels - the number of standard deviations between the mean and the Specification Limits.\n\nSee  Short & Long Term Process Capability, Cp, Cpk, Pp, Ppk, Sigma Level."]
["PROCESS CONTROL","See STATISTICAL PROCESS CONTROL, Process Control Chart."]
["PROCESS CONTROL CHART","Any of a number of various types of graphs used for SPC upon which data are plotted against specific control limits. Often X-Bar and R Charts. See Control Chart."]
["Process Entitlement","First time assessment of a process/operation over limited data or time period. \nSame as Short Term Process Capability. Also known as Within Capability, Potential Capability, Preliminary Process Capability, Performance Index, Process Potential, Process Entitlement."]
["Process Instruction Sheets","Operational Method Sheets. Standard Operating Procedures (SOP's), Process Instructions. Quality Process Sheet (QPS). See also Quality System, SPC, Poka-Yoke..."]
["Process Map","A diagram showing process flow including: sub-processes, value added & non-value added steps, inputs (X's), outputs (Y's), measures, decisions, bottlenecks, re-work loops, control points. Typically has three representations: How you thought it was; how it actually is; how it should be. Graphically represented using iGrafix. See also SIPOC, \"7 Step Method\", Process Boundary"]
["Process Mean","Same as Population Mean"]
["Process Model","See Process Block Diagram."]
["Process Potential","Same as Short Term Process Capability. Best Potential process capability within subgroups. Also known as Within Capability, Process Entitlement, Preliminary Process Capability, Performance Index or Process Potential.\nSee also Between, Overall Capability."]
["Process Sigma Level","See Sigma Level."]
["PROCESS SPREAD","The range of values which a given process characteristic displays; this particular term most often applies to the range but may also encompass the variance.  The spread may be based on a set of data collected at a specific point in time or may reflect the variability across a given amount of time."]
["Process Stability","See Stable Process."]
["Processes","The 3rd element of the GRPI leadership strategy:\nPROCESSES: Team Processes - Decision Making,  Problem Solving, Conflict Management & Communications\n* Decisions are made based on who has the expertise and best information, not on hierarchy or authority\n* Conflict on the team is confronted openly and constructively.\nSee also Goals, Roles & Responsibilities, Interpersonal Relationships, 4P's."]
["PRODUCERS RISK","Probability of rejecting a lot when, in fact, the lot should have been accepted (see ALPHA RISK)."]
["Product Mean","Accuracy / mean of the product itself. Nomenclature mproduct. See also Measurement System Mean, Total Mean"]
["Product Variance","Precision / variance of the product itself. Same as True Variance. See also Total Variance"]
["Production Trial Run","Same as Prove Out Build."]
["Profiles01","A tabulated report of LR warranty data for a given marque & fault code. For each build month and TIS (0, 3, 6, 9, 12 MIS etc) this shows three figuresfor the chosen report out:\nFPU: Incidents, Veh Comp, FPU.\nCPU: Cost (£), Veh Comp, %CPU (pence)"]
["PROJECT","A problem, usually calling for planned action."]
["Project Champion","Strategic business leader and agent of change. Manages 6-Sigma project resources, mentors Black Belts, provides infrastructure support. Advises on project priorities."]
["Projective Property","A concept of Fractional Factorial Experiments:\nFractional Factorials can represent full-factorials once some effects demonstrate weakness\nSee also Fractional Factorials, DOE, Sparsity of Effects, Sequential Experimentation."]
["Proportion","Proportion of sample meeting criteria. Testing of proportion hypothesis for discrete data can be done with the 1 or 2 Propotion Tests. See also P."]
["Proportion Test","Test for whether 1 or 2 discrete samples reflect equal proportions. Note a 2 Proportion test is equivalent to a Chi-Squared test with 1 degree of freedom. Proportion Test shows the Alpha likelihood. Use Power & Sample Size Proportion Test to show Beta risk."]
["Prove Out Build","Series of vehicle builds after the last prototype build and before volume production build. A representative production trial run using production tools, processes, volumes, rates, tolerances, operators etc. Same as Production Trial Run."]
["PTAR","Plan-Train-Apply-Review - The training process breakthrough strategy of the Black Belt certification program. A closed-loop feed-back system."]
["PTS","Project Tracking System - Ford web based database of all 6-Sigma projects. New projects are registered, project numbers assigned, latest project status and reports are stored. Search here for details of other projects potentially related to yours."]
["PUB No","LR Power Unit Build Number: Engine derivative / configuration code"]
["Purposive System","A Dynamic System with a highly complex logical relation of function to purpose. \nSee also Static System."]
["P-Value","Probability Value. Range 0-1. Likelihood of the Null Hypothesis. \nGenerally P < 0.05 implies statistically significant.\n\"If P is Low, H0 must go\"\nNB: In the Anderson-Darling Normality test P > 0.05 implies likely Normal Distribution. i.e. The Null Hypothesis is that the data is Normal. \nSee also Alpha & Beta Risks, Type I & II Errors, P."]
["PVP","Process Validation Plan. The Process equivalent of a DVP."]
["PVT","Plant Vehicle Teams"]
["Q&R","Quality & Reliability. See also R&R."]
["Q1","1. A Ford & Big 3, plant and supplier quality standard (and Flag!)\n2. The first quartile. The point covering the lowest 25% of the data"]
["Q101","A previous and pre-requisite quality standard to achieving the current Q1 standard."]
["Q3","The third quartile. The point covering the lowest 75% of the data"]
["QFD","Quality Function Deployment - Techniques & matrices to highlight process issues - Taking customer needs through the design process to ensure the product meets customer needs - inc Cause & Effect Matrices. Now known as ACF."]
["QITD","Quality is In The Detail!"]
["QLF","See Quality Loss Function."]
["QMC","QMC:  Quasi Monte Carlo with integrated Web-based interface to perform AR&R using external code or explicit equation. "]
["QOE","Quality Of Event"]
["QPAT","Quality Program Activity Team. The team on a vehicle program which progresses the program quality deliverables and reports to the program steering team (PST)."]
["QPS","Quality Process Sheet. See also OMS, Operational Method Sheet, Process Instruction."]
["QRP","Quality Review Process"]
["QRSOW","Quality / Reliability Statement Of Work. Detailed expectations of supplier in meeting Ford quality processes."]
["QRT","Quality Responsible Team - a cross-functional organizational team responsible for the quality of a particular vehicle system."]
["QS-9000","Quality processes standards. Consists of 7 standards and 2 supplements.\nEthos: Document what you do. Do what you've documented. Demonstrate that you do. \nFormerly known as ISO 9000. Now being superceded by TS16949. \nSee also 7-Pack, MS-9000, QSA, APQP, PPAP, MSA, FMEA, SPC, TS-9000 / TE Supplement, QSA-TE."]
["QS9000 7 Pack","The full set of 7 QS-9000 standards and 2 supplements: QS-9000, QS-9000 QSA, APQP, PPAP, MSA, FMEA, SPC and TE Supplement with its' QSA-TE. Also known as the 9 Pack!"]
["QSA","Quality System Assessment standard - the process for verifying compliance to a standard. Part of QS-9000."]
["QSA-TE","Quality System Assessment standard - the process for verifying compliance to the QS-9000 Tooling & Equipment standard TE-9000 / TE Supplement."]
["QSF","Quick Service Fix"]
["Qualitative Change Diagram","An element of Structured Inventive Thinking (SIT). Part of the Closed World Method."]
["Qualitative Change Graph","An element of Structured Inventive Thinking (SIT). Part of the Closed World Method."]
["Quality","Quality is defined by the customer. The customer wants products and services that meet, or exceed, their needs, throughout their life, at a cost that represents value."]
["Quality Analysis Cube","See Quality Data Cube."]
["Quality Loss Function","Determining the relationship between deteriation of product characteristics (X) and the quality loss of that to the customer (Y). Methods to seek to reduce variability in Y for given variability in X, thus minimising costs of tolerance control whilst maximising customer satisfaction. i.e. reducing the slop of the signal to response graph, thereby reducing the effects of variability. Movement away from target increases waste to Society: loss of material, energy, labour, customer satisfaction, performance etc. Used to optimise parameters to maximise quality rather than simply setting go/go-go tolerances.\n`L(y)=k[s2y+(y-t)2].     See L, L-Bar."]
["Quality System","A Quality System is an organization’s agreed upon method of doing business.\nIt is not to be confused with a set of documents that are meant to satisfy an outside auditing organization (i.e. ISO 900x, QS9000, TS16949). This means a Quality System represents the actions, not the written words of an organization. \nIncludes: Quality Policy; Organization for Quality (does not mean Quality Department!); Management review of Quality; Quality Planning (APQP) (how to launch and control products and processes); Design Control; Data Control; Purchasing: Approval of materials for on-going production, Evaluation of suppliers, Verification of Purchased Product (does not mean incoming inspection!); Product identification and traceability; Process Control; Inspection and Testing; Control of Inspection , Measuring, and Test Equipment (Calibration, MSA); Control of Nonconforming Product; \nCorrective and Preventative Action; Handling, Storage, Packaging, Preservation, and Delivery; Control of Quality Audits (do what we say we do?); Training; Service; Use of Statistical Techniques. See OMS, SPC, Poka-Yoke, Joiner Traingle."]
["Quality Yield","No. Good Parts / Total Parts. See Yield. See also OEE, Availability, Performance Efficiency."]
["Quartile","The points differentiating the 1st, and 3rd quarters of the data distribution. \nShown on Box Plots. See also Q1, Q3, IQR."]
["R","See Range, Regression Coefficient, Repeatability, Reproducibility, Resolution ..."]
["r","Percent"]
["R & M","Manufacturing Reliability & Maintainanbility."]
["R & R","Repeatability & Reproducibility. See Gage R & R"]
["R Chart","Measure of variability within subgroups. Plot of the difference between the highest and lowest in a sample.  Range control chart. When greater than 10 samples available then S Chart can provide addition standard Deviation data. When no subgroups are available use MR Chart. See also X-Bar Chart, I-MR Chart."]
["R&R","1. In Gage R&R: Repeatability & Reproducability\n2. In Sampling: Random & Representative\n3. In DfSS: Reliability & Robustness"]
["R(t)","The Reliability or Survival Function."]
["r/1000","Repairs per thousand"]
["R2-Value","The proportion of the variability in the response that is explained by the correlation equation. Square of the Pearson measure of correlation. Range 0 to 1. See also R-Value"]
["RANDOM","Random Sampling: Selecting a sample so each item in the population has an equal chance of being selected; lack of predictability; without pattern.\nSee also other applications: Random*"]
["RANDOM CAUSE","A source of variation which is natural / random and is inherent in the process; a change in the source (“trivial many” variables) will not produce a highly predictable change in the response (dependent variable), e.g., a correlation does not exist; any individual source of variation results in a small amount of variation in the response; cannot be economically eliminated from a process; an inherent natural source of variation. Not controllable by the operator. Also known as Common Cause variation. See also Special / Assignable Cause."]
["RANDOM EFFECTS MODEL","Experimental treatments are a random sample from a larger population of treatments.  Conclusions can be extended to the population.  Interferences are not restricted to the experimental levels."]
["Random Error","Errors not accounted for by idendified factors. Also called Experimental Error or Residual Error. See also Error, Defect. Nomenclature:e "]
["RANDOM SAMPLE","One or more samples randomly selected from the universe (population)."]
["RANDOM VARIABLE","A variable which can assume any value of a set of possible values."]
["RANDOM VARIATIONS","Variations in data which result from causes which cannot be pinpointed or controlled. See also Assignable Variation"]
["Randomized Order","Conducting a data collection expreiment sampling readings in a random sequence of factors and levels. In contrast to Standard Order."]
["RANDOMNESS","A condition in which any individual event in a set of events has the same mathematical probability of occurrence as all other events within the specified set, i.e., individual events are not predictable even though they  may collectively belong to definable distribution."]
["RANGE","In an X-Bar & R Chart this is the difference between the highest and lowest values in a set of values or “subgroup.”\nIn an I & MR Chart the Moving Range is the difference between value and the next."]
["RANKS","Values assigned to items in a sample to determine their relative occurrence in a population."]
["RAPID","Part of the Ford2000 product and process improvement strategies. \nShort term Rapid Action Process Improvement Deployment actions eliminating waste and reducing bureaucratic drag."]
["RATIO","Numeric scale which ha a absolute zero point and equal units of measure through, i.e., measurements of an output parameter, i.e., amps."]
["RDM","Reliability Demonstration Matrix"]
["REA","Reliability Evaluation Assessment (?) as measured by the Reliability Evaluation Index. A TGW equivalent measure of customer satisfaction at 0, 6,000 and 15,000 miles in service."]
["Reaction Plan","Statistical Process Control Control Plan specifies data collection on a Control Chart which monitors data in control within Control Limits. Out of control data triggers a Reaction Plan."]
["Recursion","The situation where a sub-system, organization has an identical system model to the parent and is itself viable. E.g. A viable foetus within its' mother's womb.\nPart of the Viable System Model (VSM) developed by Stafford Beer. \nSee also Viable, Self-Reference, Homeostasis, Invariant, P Diagram."]
["Recursive Dimension","A chain of systems which, in the Viable Systems Model, demonstrate recursion of the system model at each level. More generically, the inputs & outputs at each level may be equated to the Y=f(X) cascade."]
["Red Flag","Red flag conditions in the process will contribute to error and defect generation.  Watch for and react to Red flag conditions! Ideal Applications for Poka-Yoke! e.g. 1. Frequent changes to a job, 2. Complex processes, 3. Lack of standards, 4. Lack of measurement systems, 5. Lack of training, 6. Long cycle times, 7. Infrequent jobs, 8. High output, 9. Environmental conditions, 10. Attitude ( Motivation)"]
["Red Tagging","The process of marking potentially superfluous items with Red Tags before removing them from the work place. A key enabler for Sorting and the 5S strategy."]
["Redistribution","An Structured Inventive Thinking solution technique. Follows use of the Closed World Method or the Particles Method. Used with the other SIT solution techniques of Uniqueness, Dimensionality & Pluralization."]
["Reduced Model","Simplification of the factors to the critical few using DOE analysis."]
["Region of Curvature","The region where one or more of the significant inputs will no longer conform to the first order model.  Once in this region of operation most responses can be modeled using the following fitted second order model: Y (predicted) = b0 + S bi Xi + S bii XiXi + S bij XiXj"]
["Regression","Linear Regression:  Models the linear relationship between variables when one response variable depends on the others.  Regression is used with a continuous response variable (Y) and one or more predictor variables that are most often continuous, but can be categorical. When used on two X's regression calculates correlation. When used on X & Y values regression can be used for prediction. See also Correlation, Multi-Variant Regression, Multicollinearity, Variance Inflation Factor, Best Subsets, C-p."]
["REJECT REGION","The region of values of which the alternate hypothesis is accepted."]
["Reliability","Consistency of output across time. The probability that an item will adequately perform for a specified time, under specific environmental conditions. Robustness over time. \nSee also Repeatability, Reproducability, Reliability Function, R(t), Robustness (to Noise), Homeostasis, Durability (throughout product life)."]
["Reliability Function","Same as Survival Function."]
["Repeatability","Consistency of output across different machines. Same as Equipment Variation.\nSee also Reproducability, Reliability, Repetition, Assessor Variation."]
["Repeatability Variance","Precision / variance of the machine. See also Total Variance"]
["Repeated Whys","See Stair Stepping."]
["Repetition","Repetition: Running more than one sample of a single run.\nReplication & Repetition help reduce noise in a designed experiment.\nBoth can be used in the same experiment & each contributes to sample size required."]
["Replication","Observations made under identical test conditions.\nReplication: Replicating the entire experiment in a time sequence.\nReplication & Repetition help reduce noise in a designed experiment.\nBoth can be used in the same experiment & each contributes to sample size required."]
["REPRESENTATIVE SAMPLE","A sample which accurately reflects a specific condition or set of conditions within the universe."]
["Reproducability","Consistency of output across different operators. Same as Assessor Variation.\nSee also Repeatability, Equipment Variation, Reliability."]
["Reproducability Variance","Precision / variance of the people operating the machines/process. See also Total Variance"]
["RES","Things In Process Done More Than Once"]
["RESEARCH ","Critical and exhaustive investigation or experimentation having for its aim the revision of accepted conclusions in the light of newly discovered facts."]
["RESIDUAL ERROR","See EXPERIMENTAL ERROR. Check for: Normal Residual plot approximating to straight line, I Chart of Residuals showing process in control, Residuals vs Fits being random +/- the zero line (Histogram of residuals is for info only - no special test on this). See Breyfogel p.367. See also Fits, Residual Error, Regression, Correlation."]
["Residuals","The difference between individual points and best fit line. Note: DOE, ANOVA etc will do a fitted line as part of their analysis. See also Fits, Residual Error, Regression, Correlation."]
["Resolution","1. Resolution: The number of decimal places that can be measured by the system.  Increments of measure should be at least one-tenth of the width of the product specification or process variation. In Gage R&R this is the same as discrimination.\n2. Resolution III, IV, V Designs: Used to assess what needs to be lost, in terms of our ability to estimate higher order interactions, in order to reduce DOE Fractional Factorials no of runs. A Fractional factorial distinguishing: III Main effects only,   \nIV Main effects & 1st order interactions. V Main effects and 1st & 2nd order interactions. \nNomenclature: R."]
["Response","A dependant variable. The output of a process. See Y's. See also P Diagram."]
["Response Optimizer","Minitab Response Optimizer. Tool for optimising paramters in a reduced model. Apply after Defining, Analysing & Reducing DOE."]
["Response Optimizer","See Multiple Response Optimizer."]
["Response Surface","The surface represented by the expected value of an output modeled as a function of significant inputs (variable inputs only): Expected (Y) = f(x1, x2, x3,…xn)"]
["Response Surface Method","See RSM."]
["RFT","Right First Time"]
["Right Censoring","In Reliability Testing: allowing for the effects of parts that have survived the test time. I.e. You only know that the failure occurred after a particular time. NB: To perform reliability Analysis where there is no censoring use Right Censoring techniques. \nSee also Arbitrary Censoring, Left Censoring, Interval Censoring."]
["RIMES","Rover Integrated Manufacturing Engineering System. Land Rover system for managing process sheets (Engineering Operation Standards / EOS's) and process assignment information."]
["Risk Priority Number","See RPN"]
["Ro","Greek Letter r."]
["Robustness","Maintainance of required functionality across operating range despite all noise factors. The conditions or state in which a response parameter exhibits hermeticity to external cause of a non-random nature; i.e., impervious to perturbing influence. \nSee also Reliability, Durability, Parameter Design, Homeostasis."]
["ROCOF","Rate OF Change OF Failure - Relibality Analysis of failure types to assess where in the Bathtub Curve failures are occuring and, therefore, where root cause(s) are likely to be."]
["ROIC","Return On Investment Capital"]
["Roles & Responsibilities","The 2nd element of the GRPI leadership strategy:\nROLES & RESPONSIBILITIES: Allocation of Work - Role Responsibility & Work Processes\n* The work is organized in a way which clearly leads to accomplishing the teams goals\n* There is maximum use of the different resources of individuals on the team. \n* Everybody is clear about what responsibilities they have and the jobs they're supposed to do. \n* The leadership is shared.\nSee also Goals, Processes, Interpersonal Relationships, 4P's."]
["Rolled Throughput Yield","See RTY"]
["RPN","FMEA Risk Priority Number: =  Severity X Occurrence X Detection. Rated 1-1000.\nUsed to prioritize recommended actions.  Special consideration should be given to high Severity ratings even if Occurrence and Detection are low."]
["RS","Response Surface. See RSM."]
["RSM","Response Surface Method is a technique to find maximum or  minimum condition.\nThe basic strategy is to consider the graphical representation of the yield as a function of the two significant factors.  This graphic could be considered to be similar to the contours on topographical maps.  The higher the “hill”, the better the yield.  \nThe idea is to gather data to enable us to plot the contours.  Once done, we can use the resulting map to find the path of steepest ascent or descent to the maximum or minimum, respectively. See also Method of Steepest Ascent/Descent, Region of Curvature, Central Composite Design, Star Points, Multiple Response Optimizer."]
["R-Sq","The proportion of the variability in the response that is explained by the correlation equation. See R2-Value and Correlation. See also R-Sq(adj)"]
["R-Sq(adj)","The adjusted R (R-Sq(adj)) takes into account the fact that R tends to overestimate the actual amount of variation accounted for in the population. See R2-Value and Correlation"]
["RTV","Room Temperature Vulcanizing. A type of silicon rubber sealant."]
["RTY","Rolled Throughput Yield - The product of the First Pass Yields of all process steps / stations - Known at Jaguar as First Time Through (FTT). Probability that a product will pass through the entire process without rework and without defects. Identifies the hidden factory. Nomenclature: Yrt   Related to DPU: Yrt = e-DPU\ne.g. Stn 1 90%, Stn 2 90%, Stn 3 90% gives 73% RTY.   \nSee also Yield, Final Yield, First Pass Yield. "]
["Run Chart","Provides a plot of Measures of problem vs. Time plus analysis to look for evidence of patterns in your process data and perform two tests for non-random behavior. See also Time Series Plot for basic plot of Measures vs. Time with no analysis. See also I-MR, R, S, X, X-bar, np, p, c, u Charts."]
["R-Value","Pearson measure of correlation. Range 0 to 1. See also R2-Value and R-Sq(adj)"]
["RWUP","Real World Customer Usage Profile - the full scope of customer product utilisation covering normal use, misuse and abuse conditions."]
["rxy","See Correlation"]
["RYG","Red-Yellow-Green traffic light status e.g. in APQP or FPDS deliverables. Green = On target deliverable. Red = Off target, no recovery plan. Yellow = Off target, but with recovery plan in place."]
["s","Greek letter lower case sigma. See Sigma, Z."]
["S","Greek letter capital sigma. Sum of. e.g. S Xn = sum of the sample values X1, … Xn"]
["s","Sample Standard Deviation or Sigma"]
["S Chart","Standard Deviation Chart. A Control Chart of subgroup standard deviations. Can be used in preference to R Charts when subgroups have 10 or more data points enabling standard deviations of sub-groups to be calculated. See also X & R Charts."]
["s Level","See Sigma Level."]
["S Limits","An alternative nomenclature for Sigma Limit as used in Upper & Lower Control Limits to designate the range of data variability.\nNot to be confused with Sigma Level which is a measure of Process Capability."]
["S&R","Squeaks & Rattles"]
["S/C","Supercharged cf. Normally Aspirated See NA"]
["S/N","The Signal-to-Noise Ratio (S/N Ratio) relates the product variation to the measurement system variation.  The S/N Ratio should be as large as possible. S/N ratio = sp / sMS. \nS/N = Energy used to produce intended result / Energy for unintended results. \nS/N =  10Log10 (m/s)2         For Static System: Nominal is Best\nS/N = -10Log10 1/n Sy2      For Static System: Smaller The Better\nS/N = -10Log10 1/n S(1/y)2  For Static System: Larger The Better\nS/N =  10Log10 (b2/s2)       For Dynamic Systems.\nThese give S/N in decibels, so *6 is equivalent to double output.  Also known as SNR.\nWhen the response is proportional to the change in any of the factors then S/N is a better measure of noise than Standard Deviation or Variance. (Advocated by Taguchi)."]
["s2","The Variance"]
["s2","The Variance"]
["s2MS","Variance due to the Measurement System "]
["s2ms","Variance due to the Measurement System "]
["s2product","Variance due to the Product "]
["s2repeatability","Variance due to lack of Repeatability"]
["s2reproducability","Variance due to lack of Reproducability"]
["s2total","See Total Variance"]
["s2true","See True Variance"]
["s4","Smarter Six Sigma Solutions - The Forrest W. Breyfogle III methodology for wisely applying 6-Sigma process principles to improve the bottom line"]
["SAM","Single Agenda for Manufacturing"]
["SAMPLE","One or more observations drawn from a larger collection of observations or universe (population). Samples seek to be random, independent and unbiased. See Simple Random, Stratified, Cluster & Systematic Sample"]
["Sample Mean","Average (Sum divided by the sample size) across sample. Nomenclature: X with a line above it. See also X-Double Bar. See also Population Mean, X-Bar, SE Mean, Tr Mean"]
["Sampling","The process of taking a sample from the population"]
["SAQ","Single Agenda for Quality. The Ford consumer TGW driven prioritisation of 6-Sigma projects."]
["SC","SC Significant characteristic derived from FMEA (quality/customer related characteristic)"]
["Scale","Reliability testing: A measure of the spread of the data. E.g. the std dev of a normal distribution. See also Location, Shape."]
["SCATTER DIAGRAMS","Charts which allow the study of correlation, e.g., the relationship between two variables."]
["Scedasticity","The relationship between the value of input variables and the variation (rather than the mean) of the output. Where variation changes with the input variable (e.g. variation increases over time) then the process demonstrates heteroscedasticity. Where variation is constant the process demonstrates homoscedasticity."]
["Screening","Investigation of Main Effects and/or lower order factors/interactions. Application of Fractional Factorials to reduce DOE no of runs"]
["SE Mean","Standard Error of the Mean. Calculated as the Std Dev divided by the Square Root of n."]
["Self-Reference","The property of a system whose logic closes in on itself: each part makes sense precisely in terms of the other parts. Self-reference supports Identity & self-awareness, facilitiates self-repair and explains recursion.\nPart of the Viable System Model (VSM) developed by Stafford Beer. \nSee also Viable, Recursion, Homeostasis, Invariant, P Diagram."]
["Sensitivity ","See Test Sensitivity, d/s"]
["Sequential Experimentation","A concept of Fractional Factorial Experiments:\nFractional Factorials can be combined into more powerful designs\nHalf-Fractions can be “folded over” into a full factorial\nBy eliminating uninteresting Input Variables, fractions can become full factorials.\nSee also Fractional Factorials, DOE, Sparsity of Effects, Projective Property"]
["Setting Aid","A device used to set the relationship of a cutting tool to the fixture and part. \nOr to set two or more specific parts in relationship to each other during assembly. \nSee also Jig, Fixture, Tool, Gauge."]
["Severity","See S Level, FMEA, Criticality."]
["Sgauge","Standard Deviation of Type 1 Gauge R & R readings. Same as sMS."]
["Shape","Reliability testing: The shape of the Weibull or Exponential distribution. See also Location, Scale. Nomenclature: b"]
["Shewhart","Dr. Walter Shewhart. A lead innovator of SPC techniques in the 1920's at Bell Laboratories. See Deming-Shewhart Cycle, Shewhart Control Chart."]
["Shewhart Control Chart","An X-Bar and R Control Chart."]
["Shift","Changes in quality over time. This will incorporate a growing number of general cause variation which broaden the overall data spread. Data typically shifts 1.5 Sigma Levels between short term & long term. See also stability, drift, short & long term sigma values"]
["Shining","Sweep and cleanliness. Part of 5S. To keep your area clean on a continuing basis. A clean workplace is indicative of a quality product and process. Dust and dirt cause product contamination & potential health hazards. A clean workplace helps to identify abnormal conditions. See also Sorting, Storage, Standardise, Sustain."]
["Shop Floor Defect","See Failure Mode"]
["Short Term","1. Short Term Process Capability - Generally measured as Cp.\n2. Short Term Sigma Level - The Sigma Level of the Short Term Process Capability. "]
["Short Term Process Capability","Best Potential process capability within subgroups with all other sources of variability removed. Generally measured as Cp/Pp, or, when taking account of how centred the process is, Cpk/Ppk. Min acceptable value: 1.67 or Sigma Level 5. \n(See Pp or Cp for explanation of terminology usage).\n\nData constitues a Short-term sample where it: is free of assignable causes; represents the effect of random causes only; is collected across a narrow inference space; across one shift of production; using only one machine; with one operator; using raw components from only one lot of raw material, etc…\n\nAlso known as Within Capability, Potential Process Capability, the Process Entitlement or the Performance Index. \nSee also Cp, Cpk, Process Capability, Long Term Process Capability."]
["Short Term Sigma Level","Short Term Process Sigma Level capability. = Long Term Sigma Level + Z-Shift \n(typically 1.5). Nomenclature: ZBench or Zst"]
["Sigma","1. A letter in the Greek alphabet\n2. Standard Deviation = Root of sum of squares of deviations from the mean\n3. Sigma Level measure of population process capability. E.G. 6-Sigma."]
["Sigma Level","Key measure of process capability of the population. \nThe number of Std Deviations between the mean and the nearest Spec Limit (LSL or USL). \nAlso known as Process Sigma Level or Z-Score (\"Zee Score\").\nMay be Short Term Sigma Level or Long Term Sigma Level. \nTypically reported as Short Term Sigma Level, See also Capability, Cp\nNote: Nothing to do with S-Level"]
["Sigma Limit","Sigma Limits are used in Upper & Lower Control Limits to designate the range of data variability. \nNot to be confused with Sigma Level which is a measure of Process Capability."]
["Sign Test","See 1 Sample Sign Test."]
["Signal","What the customer does to make the process work. The desired product characteristic. The input to a process. E.g. customer selects parameter values, and applies trigger force to switch. See also Y's, SNR, P Diagram.\nNomenclature: M (i.e. Modulated Signal) in the Ideal Function model y=bM. "]
["Significance Level","a Alpha level"]
["Significant Characteristic","A characteristic significantly affecting customer satisfaction. Identified in an FMEA as having a severity of 5-8 & an occurrence >3 (YS). If confirmed on review of process controls will be designated with an Inverted Delta (Ñ). See also Critical Characteristic."]
["SIL","Satisfaction Inhibitor List. Same as SSA"]
["SIM","Supplier Improvement Metrics"]
["Simple Random Sample","Sampling where all samples of n objects is equally likely - such unbiased independent samples are reare in the real world."]
["SIP","???"]
["SIPOC","Suppliers - Inputs - Process steps (with action verb) - Outputs - Internal & External Customers - Variables - A business process mapping methodology. Presented in textual rather than graphical form. See also P Diagram."]
["SIT","See Structured Inventive Thinking. "]
["Six Pack","See Capability Six Pack"]
["SL","Not generally used in isolation. Used as part of two very different concepts:\nUSL & LSL refer to the Upper & Lower Specification Limit.\n3.0SL & -3.0SL are alternative nomenclature for the Upper & Lower Control Limits."]
["SLAM","SLAM: Successive Linear Approximation Method:   a Web-based program to perform probabilistic analysis and AR&R assessment. "]
["S-Level","FMEA Severity Level. Rated 1-10 by the Big 3 for Minor, Low, Moderate, High, Hazardous effect of failure. An assessment of the seriousness of the failure effect on the customer.  The customer can be the end customer and / or the next process operation. (Note: Nothing to do with Sigma Level). See also Criticality, Occurrence, Detection."]
["SLI","Safety Leadership Initiative"]
["Smart Little People","TRIZ name for Particles. An SIT technique."]
["SMART Objectives","Project objectives which are: Specific & Stretch, Measurable, Aligned with business objectives, Realistic and Time targetted."]
["SMED","Single Minute Exchange of Dies. Processes to minimise press die change over times by maximising offline preparation."]
["SNR","Signal to Noise Ratio. Also known as S/N."]
["SOD","Severity x Occurrence x detection = Risk Priority Number (RPN)."]
["Soft Failure","Failures can be either SOFT (degraded performance to an unacceptable level) and/or HARD (product function ceases)."]
["Something Changed","98.5% of all problems are caused by something which has changed. In contrast Never Been There problems which have always been there."]
["SOP","Standard Operating Procedures: Work or Process Instructions, Operational Method Sheets etc. \n"]
["Sorting","Deciding what is needed. Part of 5S. To sort out necessary and unnecessary items. To store often used items at the work area, infrequently used items away from the work area and dispose of items that are not needed. Removes waste, Safer work area, Gains space, Easier to visualize the process. See also Red Tagging, Storage, Shining, Standardise, Sustain."]
["Sparsity of Effects Principle","A concept of Fractional Factorial Experiments:\nProcesses are usually driven by Main Effects and Low-order interactions.  \nHigher order interactions are extremely unlikely to be significant.\nSee also Fractional Factorials, DOE, Projective Property, Sequential Experimentation."]
["SPC","Statistical Process Control - Methods of improving and controlling process capability by statistical analysis and monitoring of quality inputs. A Control Plan specifies data collection on a Control Chart which monitors data in control within Control Limits. Out of control data triggers a Reaction Plan."]
["Spec Limit","See Specification Limit"]
["Special Cause","See ASSIGNABLE CAUSE, Assignable Variation"]
["Specification Limit","Limit of customer acceptable range. See also USL, LSL, Control Limits\nNB. Data within Spec Limit does not necessarily imply process within Control Limits and vice versa"]
["SPIT","Single Point In Time vehicle warranty analysis - comparison of quality levels across vehicle lines at same model year"]
["SPM","Statistical Process Measurement"]
["Square Root","The number which multipled by itself equals x. Nomenclature: Öx means Square Root of x"]
["SRO","Standard Repair Operations. A description of the work undertaken by the dealer."]
["SS","1. Six Sigma - Measure of process variation - See 6-Sigma. \n    This particular abbreviation is used in the formula: SStotal = SSbetween + SSwithin. \n    i.e. Total variation = variation between subgroups + variation within subgroups\n2. Sum of Squares - The summation of the squared deviations relative to zero, \n    or to the mean of an experiment. See also Variance and Standard Deviation\n3. Swallow Sidecars - The name of the company founded by Sir WIlliam Lyons \n    which later became Jaguar Cars Ltd."]
["Stability","The distribution of measurements remains constant and predictable over time for both mean and standard deviation\nNo drifts, sudden shifts, cycles, etc… Lack of shift / drift over time."]
["STABLE PROCESS","A process which is free of assignable causes, e.g., in statistical control."]
["Stair Stepping","To fully define a problem: \"What's wrong with what?\" by repeatedly ask \"Why?\". See also : 5Y's."]
["Standard Deviation","A statistical index of variability which describes the spread. See also Variance.\nNB: S/N may be a better measure of spread/noise, especially where there is a response changes proportionally with any of the factors."]
["Standard Operating Procedure","See SOP"]
["Standard Order","A standard pattern of Low/High factor levels in a 2K Factorial DOE. \n-1 and 1 are used to represent the low and high levels of the factors. e.g. for 1 replication:\n-1 -1   \n 1 -1\n-1  1\n 1  1  \nThe converse of Standard Order is Randomized order"]
["Standard WIP","??? Part of Kaizen and an enabler for Kan Ban Lean Manufacturing method. \nSee also Takt Time, Cycle Time, Work Sequence."]
["Standardize","Part of 5S. To maintain the workplace at a level which uncovers and makes problems obvious. To continuously improve our plant by continuous assessment & actions. To sustain sorting, storage and shining activities every day. See also Sorting, Storage, Shining, Sustain."]
["Standardized Work","The “one best way” to perform each operation identified and agreed upon through general consensus (not majority rules). This becomes the “Standard” work procedure. The affected employees should understand that once they have defined the standard, they will be expected to perform the job according to that standard.\nSee also Lean Manufacturing=5S>Visual Factory>Standardized Work>Kaizen>KanBan."]
["Star Points","Enhancing design for a Response Surface Method by adding additional Star Points to a standard Cube/Square+Centre Point design in a circluar/spherical pattern. These will be points with factor levels of +/1 root 2 (1.414)"]
["Static System","A System where there is a fixed Signal. E.g. ignition switch. In contrast to a Dynamic System which has a variable Signal. E.g. Accelerator."]
["STATISTICAL CONTROL","A quantitative condition which describes a process that is free of assignable/special causes of variation, e.g., variation in the central tendency and variance.  Such a condition is most often evidenced on a control chart, i.e., a control chart which displays an absence of non-random variation."]
["STATISTICAL PROCESS CONTROL","The application of statistical methods and procedures relative to a process and a given set of standards. See SPC."]
["Std Dev","See Standard Deviation"]
["Stepwise Regression","A Regression method for selecting most significant factors on a stepwise basis taking the most significant and then adding others ..."]
["Storage","Arrangement of  items needed straight up the work place. Part of 5S. \nTo arrange all necessary items. To have a designated place for everything. A place for everything and everything in its place. Visually shows what is required or is out of place. More efficient to find items/ documents (silhouettes/labels). Saves time, not having to search for items. Shorter travel distances. See also Sorting, Shining, Standardise, Sustain."]
["Storming","Stage 2 in Tuckman's model of team development. The struggle as the team realises the scale of the challenges ahead. Highlights the need for collaboration, for adherence to process & to focus energy on the purpose. Often expressed in impatience. Conflict resolution is a key team skill in this stage."]
["Stratified Sample","Sampling by diving the population into homogenous groups and drawing a sample from each group. I.e. where some correlation/dependance is expected between variables and cluster."]
["Stress Coefficient","A parameter of the probability function for a Probit Reliability analysis. \nNoemclature: b1."]
["Stress/Strength Interference","Mapping the potential interference between the distribtution of the customer stress on the product with the product strength - where stress & strength can represent any CTQ characteristics. Overlap between the two represents Useful Life failures where stress exceeds strength."]
["Structured Inventive Thinking","Structured Inventive Thinking (SIT) is a creative structured problem solving technique. The SIT methodology deals with conceptual solutions to technological problems. Its purpose is to focus the problem solver on the essence of the problem, to overcome psychological barriers to creative thinking, to enable the discovery of inventive solutions.\nUses Closed World Method or Particles Method to derive uniqueness from selected objects. Dimensionality, Pluralization & Redistribution are then used to define solution concepts for input to Generification. For origins see TRIZ."]
["Study Tolerance","Measurement system study variation (sMS) divided by total variation (sTOTAL). \nAssesses data relative to total sample data spread irrespective of spec limits.\nRange: <10% = good, 10-20% = Acceptable, 20-30% = Marginal. \nAlso known as: P/TV, %R&R, %Study Tolerance, %SV, Study Variation, Measruement Capability Index."]
["Study Variation","Measurement system study variation (sMS) divided by total variation (sTOTAL). \nAssesses data relative to total sample data spread irrespective of spec limits.\nRange: <10% = good, 10-20% = Acceptable, 20-30% = Marginal. \nAlso known as: P/TV, %R&R, %Study Tolerance, %SV, Study Variation, Measruement Capability Index."]
["SUBGROUP","A logical grouping of objects or events which displays only random event-to-event variations, e.g., the objects or events are grouped to create homogenous groups free of assignable or special causes.  By virtue of the minimum within group variability, any change in the central tendency or variance of the universe will be reflected in the “subgroup-to-subgroup” variability."]
["Success","An observation that has the characteristic of interest. NB. The characteristic of interest may well be real world failures! See also Failure"]
["Success Tree","The converse of a Fault Tree. See Fault Tree Analysis."]
["Survival","Endurance of capability prioir to failure. See Survival Function."]
["Survival Function","Plot of probability of survival vs time. R(t) = Successes/Total Units. Where MTBF is constant this = e-t/MTBF .\nWhere t=time period. See also Hazard Function."]
["Sustaining","Training & Disciplined Culture. Part of 5S. To maintain our discipline, we need to practice and repeat until it becomes a way of life. To build 5-S’s into our every day process. See also Sorting, Storage, Shining, Standardise."]
["SV","See Study Variation"]
["SV/Toler","Measurement System variation (5.15*sMS) divided by Tolerance. \nAlso known as: %Tolerance, P/T, SV/Toler, Tolerance Ratio."]
["SYMPTOM","That which serves as evidence of something not seen."]
["Symptom","A dependant variable. See Y's"]
["SYSTEM","A set of connected things or parts; an organized body. That which is connected according to a scheme. A regularly interacting or interdependent group of items forming a unified whole, a network serving a common purpose.\nAn Engineered System Model may be represented in a P Diagram.\nA Viable System is one able to maintain a separate existence, surviving in a given environment.\nSee Static System, Dynamic System, Purposive System, Viable System Model."]
["System In Focus","The Viable System which is the focus of an organizational or system study. There will be other chains of systems above and below this."]
["System Model","1. See Engineered System Model, P Diagram.\n2. See Viable System Model."]
["Systematic Sample","Sampling by select every nth object from a selected starting point."]
["SYSTEMATIC VARIABLES","A pattern which displays predictable tendencies."]
["Systems Engineering","The core FPDS process of engineering systems with all their interfaces and dependancies so as to optimse vehicle design by cascading requirements from Vehicle to System to Sub-System to Component levels and then verification of those back up the levels. Each stage of systems engineering iterations are made of the three bubbles: Requirements Analysis, Design Synthesis, Verification."]
["Systems V","The FPDS Systems Engineering process shown graphically as: `\\_/`\nRepresented on the FPDS gateway timeline as: <KO>`<SI>\\<PA>_<AA>/<PR>`<J1>&<FS>.\nThe downward slope represents the target cascade systems engineering process.\nThe upward slop is the verification process."]
["t","t Value. See stats tables for values by sample size. E.g. sample size 10 t=2.26, for 100 t=1.98 for 1000 t=1.96. See t-Distribution"]
["T","1. See Target. \n2. T-Statistic. A measure of impact in ANOVA used in pareto & normal charts.\n3. T-Bar. In Parameter design used for grand average of responses (y-Double Bar) for S/N & b only.\n4. Minitab Error messages may use a 'T' as an arrow to show the point in the input command string which is in error. This has nothing to do with 'T' itself, or any reference to 'T' in the command string! Confusing eh!!\nSee also t (t-Value), t, (Treatment)."]
["t ","Greek letter Tau. See Treatment"]
["T3","Train-The-Trainer. Training for those who will be delivering e.g. Green Belt 6-Sigma training. Also called TTT."]
["Taguchi","Dr. Genichi taguchi. Japanese exponent and developer of Quality processes. E.g. Parameter Design, measurement of Energy Transformation, experimental design improvements measured from current state (1,2 level values), Use of S/N as preferred measurement of noise."]
["Takt Time","A geman acronym for: Time available to produce a unit. I.e. Time available/period divided by no. units needed to meet customer requirements. Part of Kaizen Lean Manufacturing method. See also Cycle Time, Work Sequence, Standard WIP."]
["Tally","Count of subtotals of each (text) value of a factor. See also Cross Tabulation."]
["Tally Point","Measurement / Inspection point in a process"]
["TAR","Time Adjusted Return"]
["Target","Desired value - Sits between USL & LSL."]
["Target DPMO","The process target DPMO at the completion of the project. See DPMO"]
["Targets","Attribute targets set in the Systems Engineering VDS, SDS & CDS specifications. Under FPDS, Targets become Objectives when they have been verified through the Systems Engineering target cascade process."]
["Targets Cascade","The FPDS process of cascading targets from Systems to sub-systems to components before they are converted from targets to objectives. Part of the Systems V timing."]
["Tau","Greek Letter t."]
["T-Bar","In Parameter design used for grand average of responses (y-Double Bar) for S/N & b only. Nomenclature: `Tb, `TS/N."]
["TCM","Total Cost Management"]
["tc's","Treatment Combinations - Number of DOE runs with different permutations of factors."]
["t-Distribution","A series of bell shaped normal distributions. The higher the sample size, the higher and narrower the bell. The t value used in calcuating this is also used in calculating Confidence Intervals"]
["TE Supplement","Tooling & Equipment Supplement to QS-9000. Also known as TE-9000."]
["TE-9000","Tooling & Equipment Supplement to QS-9000. Also known as TE Supplement."]
["Team Process Review","A process for improving team effectiveness:\n1. How do you feel?   \n2. What did you notice (facts)\n3. What would you like to change?   \n4. Who did / will do what? (Roles & Responsibilities, actions)"]
["Team Roles","Key team roles include:\nLeader, Scribe, Time Keeper, Facilitator, team member."]
["TED","Things Engineers Do - See Engineering Activity Matrix."]
["Test Of Significance","A procedure to determine whether a quantity subjected to random variation differs from a postulated value by an amount greater than that due to random variation alone."]
["Test Sensitivity ","The magnitude or size of the difference being tested.  Nomenclature: d/s"]
["Test to Bogey","See Bogey Testing."]
["TGR","Things Gone Right - Measured in J.D. Powers APEAL survey. Defined as achieving 9/10 on an individual product attribute. See also TGW."]
["TGW","Things Gone Wrong - Customer surveyed product problems - Recorded in GQRS. Usually quoted in TGW/1000 except for J.D. Powers which quotes TGW/100"]
["THEORY","A plausible or scientifically acceptable general principle offered to explain phenomena."]
["Time Series Chart","Provides a basic plot of Measures of problem vs. time etc. Use Run Chart where more detailed analysis is required. See also Trend Chart."]
["TNI","Trouble Not Identified - Failure cause not found due to non-representative tests, destruction of information, intermittent failures etc. See also NFF."]
["Tolerance","USL - LSL. See Tolerance Ratio, %Tolerance, Study Tolerance, Tolerance Design, Tolerancing."]
["Tolerance Design","Top down design to set parameters to optimise performance according to what the customer will tolerate. \nSteps: Team; Identify Function/Response; Identify Factors; \n   Set + & - values as nominal +/- 1s,\n   Measure response (DOE) gives sensitivity of response to the variability of this factor,\n   Calculate Cost/Benefit.\nAdjusting tolerances and upgrading materials to improve Quality. Tolerance Design should be pursued after implementing effective concept design, experimental design and Parameter Design.\nSee also Tolerancing, Quality Loss Function, L, L-Bar."]
["Tolerance ratio","Measurement System variation (5.15*sMS) divided by Tolerance. \nAlso known as: %Tolerance, P/T, SV/Toler, Tolerance Ratio."]
["Tolerancing","Bottom up assessment of Geometric tolerance design. Cf. Customer Tolerance Design - top down design based on what response the customer will tolerate."]
["Tollgates","Project management points where Go/No-Go decisions are made against a set of deliverables. Tollgate meetings may be held at the start or end of each DMAIC project phase."]
["Tool","An object used in the manufacturing process in conjunction with a jig or fixture, e.g. cutting tools, hand tools / power tools.  See also Jig, Fixture, Setting Aid, Gauge."]
["TOPS 8D","The 8 Discipline problem solving methodology which predated Ford Global 8D."]
["Total Mean","Accuracy / mean of the product plus that of the measurement system. Nomenclature mtotal. See also Measurement System Mean, Product Mean"]
["Total Variance","Precision / variance of the product plus that of the people and machines. \nTotal Variance = MS Variance + True Variance. See also Product, Repeatability & Reproducability Variances"]
["Total Variation","Total Variation -TV  (5.15*sTotal) "]
["Toyota Production System","Toyota's production quality system achieving world leading consistant quality through the following elements, each building on each other: 5S, Visual Factory, Standardized Work, Kaizen, Kan Ban."]
["TPC","Technical, Political & Cultural change factors"]
["TPS","See Toyota Production System."]
["TQM","Total Quality Management - Previous Ford/Jaguar quality / process improvement methodology"]
["Tr Mean","Trimmed Mean. The mean of the sample values excluding outlaying values. The 5% Tr Mean is the mean of the sample excluding the highest and lowest 5% of values."]
["Traceability","Linking of requirements to sub-requirements. All little x's and y's are traceable back to high level customer CTQ's."]
["Trade-Off Study","Systems Engineering process of assessing the capability of alternative solutions to meet the system requirements."]
["Transfer Function","Y=f(x). Identifying the material relationship between measurable inputs and customer critical to satisfaction (CTS) key charactyeristics."]
["Transition Curve","Stages in the management of change. See Change Management."]
["Treatment","Changes to factors to improve outputs. Nomenclature:t. See also Treatment Combinations, tc's."]
["Treatment Combinations","Number of DOE runs with different permutations of factors. Also called tc's. See Treatment."]
["Trend Chart","A chart mapping values of criteria varying over time. Often referred to as a Run Chart or Time Series Plot or I-Chart."]
["TRIZ","Russian acronym for \"Theory of Inventive Problem Solving\". \nThe forerunner of SIT (Structured Inventive Thinking). Developed in the 1950's by Henry Altshuller in the  Soviet Union. Revised & simplified in Israel, enabling swifter learning with less reliance on external databases. See SIT. "]
["True Value","The target value of the CTQ"]
["True Variance","True Variance. The variance of the defect. Excludes measurement system variance. Same as Product Variance"]
["TRV","Target Reference Vehicle - target baseline vehicle design."]
["TS16949","The quality process standard. Supercedes QS-9000 / ISO 9000."]
["t-Test","Method for analyzing the difference between a sample mean and a target value\nMethods for analyzing the difference between means obtained from two samples\nMethod for determining whether significant differences in variance exist between two or more samples"]
["TTT","Train-The-Trainer. Training for those who will be delivering e.g. Green Belt 6-Sigma training. Also called T3."]
["Tuckman's Model","Tuckman's model of team development stages. \nSee Forming, Storming, Norming, Performing. Ref: Leadership 2 PG_TM…ppt"]
["TV","See Total Variance"]
["TWO-SIDED ALTERNATIVE","The values of a parameter which designate an upper and lower bound."]
["Type 1 Gauge R&R","A first level Gage R&R propounded by Jaguar SPC department. Determines the capability of a measuring system to be used by a single operator repeatably measure a single part. \nType 1 gauge capability Cg = TotalTolerance*0.15 / 6*Sgauge.\nFor acceptance Cg must be <=15%.\nCg is equivalent to %Tolerance in a Type 2 Gauge R&R."]
["Type 2 Gauge R&R","A full scale Gage R&R which determines the capability of a measuring system to measure a variety of parts with good repeatability, when measured by a variety of operators (reproducability). Nomenclature: Known generically within 6-Sigma processes as a Gage R&R. Identified as Type 2 by Jaguar SPC department so as to differentiate it from a Type 1 R&R."]
["Type I","See Type I Error or Type 1 Gauge R&R."]
["TYPE I ERROR","See ALPHA RISK."]
["Type II","See Type II Error or Type 2 Gauge R&R."]
["TYPE II ERROR","See BETA RISK."]
["TYPE III ERROR","Answering the wrong question (!)"]
["Type of Experiment","DOE types in descending order of effectiveness and resource required to execute:\n- Response Surface Methods\n- Full Factorials with Replication\n- Full Factorials with Repetition\n- Full Factorials without Replication or Repetition\n- Screening or Fractional Designs\n- One Factor At a Time (OFAT)"]
["u","1. Proportion of defectives - See u Chart.\n2. English letter \"u\". Sometimes used in lieu of Greek letter Mu (m) for Mean"]
["u Chart","An attribute Run Chart for measuring number of Defects per Unit. A simple chart used to track the number of defects per unit sampled (not the percent defective). U charts can be used when the sample size is either constant or not constant. See also c Chart."]
["UB","Upper Bound. Same as the Upper Control Limit."]
["UCL","Upper Control Limit"]
["Uncoded","Representative real life actual factor levels e.g. in a 2K Factorials DOE. \nIn contrast to Coded values."]
["Uncontrollable Variable","Noise (N):  things you cannot control due to cost or difficulty (ambient temperature or humidity, operator training). See X's"]
["Unimodal","A statistical distribution which has single dominant peaks. E.g. The Normal Distribution. See also Bi-Modal"]
["Uniqueness","An Structured Inventive Thinking solution technique. Follows use of the Closed World Method or the Particles Method. Used with the other SIT solution techniques of  Dimensionality, Pluralization & Redistribution."]
["Universe","Same as Population"]
["UNNATURAL PATTERN","Any pattern in which a significant number of the measurements do not group themselves around a centre line; when the pattern is unnatural, it means that outside disturbances are present and are affecting the process."]
["UPPER CONTROL LIMIT","A horizontal line on a control chart (usually dotted) which represents the upper limits of process capability. See Control Limit"]
["Use Cases","Process mapping of usage cases. See RWUP."]
["Useful Life Failures","Failures after initial introduction and before end of design life. Generally random effects of stress / strength / usage extremes outside of design usage specification.\nSee also Early Life Failures, Wear Out Failures, Bathtub Curve."]
["USL","Upper Specification Limit. The lower limit of customer acceptability of CTQ."]
["V","1. Resolution V.  A Fractional factorial distinguishing main effects and 1st & 2nd order interactions.\n2. The Verify phase of a DfSS 6-Sigma project."]
["VA","1. Value Added. A Value Added process step.\n2. Value Analysis."]
["VA/VE","Value Analysis / Value Engineering."]
["Validity","Ensuring Validity of DOE. See Internal Validity, External Validity."]
["Value added step","A Process Map step which is an operation which transforms the product in a way that is meaningful to the customer. A process step for which the customer would be willing to pay."]
["Value Analysis","Assessing the value functions of a system or component design vs. their costs. Used to support Value Engineering."]
["Value Engineering","Using the output from Value Analysis to determine the optimal cost / function effective engineered solution for a component or system."]
["Value Ordering","Selecting which order to sort data in. e.g. Alphabetical, Numeric, Days, Months etc."]
["Value Stream Mapping","High level process mapping."]
["VarComp","MiniTab report of components of Total Variance"]
["Variable","A characteristic which may take different values and is measurable - as opposed to an attribute which has only a binary value. See also X's, Y's, Parameter."]
["Variable R & R","The process for measuring the repeatability & reproducability of the value of a variable"]
["VARIABLES DATA","Numerical measurements made at the interval or ratio level; quantitative data, e.g., ohms, voltage, diameter; subdivisions of the measurement scale are conceptually meaningful, e.g., 1.6478 volts."]
["Variance","The average squared deviation of each individual data point from the mean. Nomenclature: s2 or s2. See also Standard Deviation. NB: S/N may be a better measure of spread/noise, especially where there is a response changes proportionally with any of the factors."]
["Variance Inflation Factor","A measure of the magnitude of multicollinearity. VIF quantifies how much the variance of an estimated regression coefficient increases if the predictors are correlated. Regression coefficients are copnsidered poorly estimated if VIF exceeds 5 or 10. Collect additional data or use alternative predictors to overcome multicollinearity. For variable i: \nVIFi = 1 / (1-R21,2,...i) where R is the correlation matrix of input variables."]
["Variation","Any quantifiable difference between individual measurements, such differences can be classified as being due to common causes (random) or special causes (assignable).\nSee also Study Variation, Total Variation, %Variation."]
["VARIATION RESEARCH","Procedures, techniques, and methods used to isolate one type of variation from another (for example, separating product variation from test variation)."]
["VE","Value Engineering."]
["Veh Comp","Vehicles Completed. Number of vehicles despatched to a market which have reached a given time in service (MIS) in a LR warranty report."]
["Verify","The Verify of a DfSS 6-Sigma project. In this step we\nAssess actual performance, reliability, and manufacturing capability\nDemonstrate customer-correlated performance over product life"]
["Versatility Matrix","Maps present competencies, skills & knowledge of production operators."]
["Viable","A system able to maintain a separate existence. An organization is viable if it can survive in a given environment.\nPart of the Viable System Model (VSM) developed by Stafford Beer. \nSee also Recursion, Self-Reference, Homeostasis, Invariant, P Diagram."]
["Viable System Model","Viable System Model developed by Stafford Beer. \nSee also Viable, Recursion, Self-Reference, Homeostasis, Invariant, P Diagram."]
["VIF","See Variance Inflation Factor."]
["Visual Factory","Processes to ensure we can readily identify: Downtime issues; Scrap issues; Changeover problems; Line balancing problems; Excessive Inventory Levels; Extraneous Tools & Supplies; If you CANNOT readily identify any of these opportunities through a visual glance of the area, then the team should seek to establish a means of immediate identification.\nSee also Lean Manufacturing=5S>Visual Factory>Standardized Work>Kaizen>KanBan."]
["Vital Few","See Critical X's"]
["VOC","Voice Of the Customer - Ensuring that customer requirements drive process & product improvement. CTS driven definition of the big Y's. See also VOP."]
["VOCS","Value Of Customer Satisfaction improvement. See VCSI."]
["VOP","Voice of the Process. CTQ/CTC/CTD driven view of the Critical X's. See also VOC."]
["VRT","Variability Reduction Team"]
["VSM","Viable System Model developed by Stafford Beer. \nSee also Viable, Recursion, Self-Reference, Homeostasis, Invariant, P Diagram."]
["Waste","There are 7 elements of Waste, they are Waste of: Correction, Overproduction, Processing, Conveyance, Inventory, Motion, Waiting. Elimination of Waste is a key objective of Lean Manufacturing. Also known as the 7 Sinds of Muda."]
["WBS","Work Breakdown Structure. Breaking large project phases down into smaller tasks and sub-tasks in order to efficiently manage the project to successful completion."]
["WCP","See World Class Process."]
["WCR","Worldwide Customer Requirements"]
["Wear Out Failures","Late in life product failures due to end of life. Generally determined by design life expectancy. NB: These can be identified by increasing frequency with time. This may occur before the target life expectancy of the part/product, thus representing premature wear. See also Early Life Failures, Useful Life Failures, Bathtub Curve."]
["WeiBayes","The application of Bayes Analysis to Weibull Distribution data."]
["Weibull Analysis","A tool for predicting population behaviour from small samples. A Log-Log representation of the Cumulative Distribution Function (CDF). Process involves: Rank sample failure times/cycles; calculate Median Rankings; Produce Weibull Plot of Ln time vs. Ln-Ln Median Rank. Determine failure type from Weibull Slope (b): <1 Early Life, =1 Useful Life, >1 EOL; Get Weibull figure for mean value proportion according to slope/shape. Measure this from Weibull plot to get MTBF. Use 5% & 95% figures to get MTBF CI. See also Weibull Distribution."]
["Weibull Distribution","A non-normal data distribution. Process capability analysis of the Weibull distribution may be performed as an alternative to transforming data to a normal distribution. \nWeibull values are determined by the Shape (b), and the Scale (h).\nSee also Extreme Value Distribution, Box Cox Transformation, Normal Distribution, Weibull Analysis."]
["Weight","An input to Multiple Response Optimizer and Response Surface Method. \n   The weight defines the shape for each response.  Select a weight (from 0.1 to 10) to emphasize or de-emphasize the target. A weight <1 places less emphasis on the target; =1 places equal importance on the target and the bounds; > 1 places more emphasis on the target\n   The weight determines how the desirability is distributed over the interval between the lower (or upper) bound and the target. It determines the shape of the desirability function that is used to translate the response scale to the zero-to-one desirability scale to determine the individual desirability of a response. You can think of a weight of one as a neutral setting. Increasing the weight requires the response to move closer to the target to achieve a given desirability. Decreasing the weight has the opposite effect. \n   See also Multiple Response Optimizer, Desirability, Importance."]
["Whisker","The first of last quartile of the data on a box plot."]
["Wilcoxon Test","See 1 Sample Wilcoxon Test."]
["Within Capability","Same as Short Term Process Capability.\nBest Potential process capability within subgroups. \nI.e. Capability within the one sampled sub-group.\nAlso know as Potential or Short Term Capability. \nSee also Between Capability, Overall Capability."]
["Work Sequence","??? Part of Kaizen Lean Manufacturing method. See also Takt Time, Cycle Time, Standard WIP."]
["World Build","The number of vehicles despatched to all markets regardless of whether they have been sold. See also Market Build."]
["WR","Warranty Returns"]
["X & R CHARTS","A control chart combining an X-Bar Chart and an R Chart, which is a representation of process capability over time; displays a variability in the process average and range across time. Use an R or S Chart before performing an X-Bar Chart. Also known as a Shewhart Control Chart. See also X-Bar Chart, R Chart, S Chart."]
["X-Bar","The Sample Mean. Nomenclature:`X  i.e. X with a line above it. \nSee also X & R Chart, X-Double Bar."]
["X-Bar & R Chart","A control chart combining an X-Bar Chart and an R Chart, which is a representation of process capability over time; displays a variability in the process average and range across time. Use an R or S Chart before performing an X-Bar Chart. Also known as a Shewhart Control Chart. Often referred to simply as an X & R Chart. See also X-Bar Chart, R Chart, S Chart."]
["X-Bar Chart","A Control Chart which measures variability between subgroups. Use an R Chart or S Chart first. See X & R Charts."]
["XDbar","Same as X-Double Bar."]
["X-Double Bar","The Mean of a set of sample means (X-Bar's). Nomenclature: X with two lines above it. "]
["X's","Process Inputs, Causes, Problems. Also known as Inputs, KPIV's, Parameters or Independent Variables. X's may be categorised as Controllable Variables, Uncontrollable Variables (Noise) and Standard Operating Procedures (SOP's). See also Critical X's"]
["y=f(x)","The CT Matrix. The transfer functions (f) which translate of customer needs (Y's) into product requirements (X's)"]
["y50","The Estimate of the point where the average customer returns for repair. \ni.e. where the customer can no longer tolerate the fault.\n(NB. Not the point where 50% of the customers complain.)"]
["Yamazumi Board","Cycle Time mapping process to identify Value added and non-value added steps. \nPart of Kaizen Lean Manufacturing method."]
["YC","Yes, Critical Charateristic. See also YS, OS, HI."]
["Y-Double Bar","The grand average of the avergaes of the responses for different settings of the parameters (X's) "]
["YFINAL","Yield of the final step in the process"]
["YFT","1. First Time Yield. \n2. First Time Yield of a process step. i.e. (input-scrap-rework)/input\n3. Further ambiguously may be used as Final Test Yield YFINAL"]
["Yield","% defect free \n    Yield = e-DPU (Using Poisson Approximation - accurate only for small DPU e.g. 0.1DPU=0.9Yield but 0.9DPU¬=0.4Yield)\nOr Yield = (1-DPO) # of Opportunities\n- See also Final Yield, First Pass Yield & Rolled Throughput Yield/First Time Through, Normalised Yield, DPO, DPU"]
["YIS","Years In Service"]
["Yn","Yield of the nth step in the process"]
["YNORM","See Normalised Yield"]
["Yrt","Nomenclature for Rolled Throughput Yield - See RTY. Note:Yrt = e-dpu"]
["YS","Yes, Significant Charateristic. See also YC, OS, HI."]
["Y's","Outputs, Effects, Dependent variables, Responses, Symptoms. Also known as Outputs or KPOV's. See also X's"]
["Z","Same as Sigma, but for Sample rather than Population. See also Z-Score, Zbench, ZLT, ZST."]
["Za","Z-Alpha. The alpha value of the Z-Distribution"]
["ZBench","Process Capability Sigma Level. May refer to either:\n1. General Sigma Level in which case it may be qualified as short or long term value.\n2. Specifically the sigma level of the sample rather than the population. \n    If so Zbench = Short Term (ZST) "]
["Z-Distribution","Another statistical distribution of data. Values may be used in hypothesis testing or the determining of sample sizes."]
["Zee","American pronounciation of the letter \"Z\". Same as Sigma"]
["Zee Score","Same as Process Sigma Level. Typically reported as Short Term Sigma Level"]
["Zee Shift","Expected shift in Process Sigma Level over the long term. Typically 1.5"]
["Zee Transformation","See Z-Transformation"]
["Zero Bias","The ideal measurement system which will produce “true” measurements every time it is used. i.e. Measurement System Variance = zero. Also known as Zero Variance"]
["Zero Variance","The ideal measurement system which will produce “true” measurements every time it is used. i.e. Measurement System Variance = zero. Also known as Zero Bias"]
["Zindividual","Individual data values transformed using the Z-Transformation."]
["ZLT","Nomencalture for Long Term Process Sigma Level of the sample."]
["Z-Score","Same as Process Sigma Level of a sample. Typically reported as Short Term Sigma Level. Nothing to do with Z-Value."]
["Z-Shift","Expected shift in Process Sigma Level over the long term. Typically 1.5"]
["ZST","Same as Short Term Process Sigma Level of the sample & ZBENCH"]
["Z-Transformation","This “transform” converts any  normal distribution (given a sample mean and sample sigma) to a standard normal distribution which always has a mean=0 and  sigma=1.\n"]
["Z-Value","The Z-value is the normal approximation test statistic used in the one-proportion testing procedure. Nothing to do with Z-Score."]
[null,"The some sum of the products of a set of values. See Balanced Array."]

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[null,null,"Process","Sign off EN9000 procedure & despatch hard copy to BU's"]
[null,null,"Team","Quality Department"]
[null,null,"Date","2009-12-01"]
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[null,null,1,"Review latest draft procedure",null,60]
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[null,null,2,"Review draft procedure with manager & update where required",20,60]
[null,null,null,null,null,null,null,10]
[null,null,3,"Collect updated procedure from printer",null,5]
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[null,null,4,"Collate updated procedure at desk",null,null,15]
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[null,null,5,"Obtain manager sign-off on updated procedure (master version)",10]
[null,null,null,null,null,null,null,10]
[null,null,6,"Photocopy master procedure",null,15]
[null,null,null,null,null,null,null,20]
[null,null,7,"Collate master procedure copies, place in envelopes & address",null,20]
[null,null,null,null,null,null,null,20]
[null,null,8,"Post copies of master procedures to BU Quality Managers",null,20]
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[]
[null,null,null,"Time (mins)",{"$f":"SUM(E9:E32)","v":30},{"$f":"SUM(F9:F32)","v":180},{"$f":"SUM(G9:G32)","v":15},{"$f":"SUM(H10:H31)","v":90}]

{"sheet":"ControlPlan","index":8,"n_rows":7,"n_cols":17,"rows_kept":7,"cut":false,"hidden":true,"merged":[[0,0,1,0],[0,16,1,16],[3,16,5,16],[3,15,5,15],[0,15,1,15],[3,2,3,3],[3,6,3,8],[2,6,2,8],[5,4,6,4],[5,5,6,5],[5,6,6,6],[5,8,6,8],[5,9,6,9],[5,10,5,11],[4,1,6,1],[4,2,6,2],[0,1,0,13],[1,6,1,8],[2,10,2,13],[4,3,6,3],[4,4,4,6],[4,7,6,7],[4,8,4,12],[4,13,6,13],[3,10,3,13],[2,4,2,5],[3,4,3,5],[1,4,1,5],[5,12,6,12]]}
["Control Plan\nTemplate","Control Plan",null,null,null,null,null,null,null,null,null,null,null,null,null,"Key","A product characteristics for which reasonably aniticpated variation is likely to significantly affect customer satisfaction with a product such a form, fit or function."]
[null,"Control Plan No ",null,"Prototype / Production","Customer",null,null,null,null,"Date (originated)",null,null,"Date (revision)"]
[null,"Part Number / Revision",null,null,"Customer Contact",null,null,null,null,"Customer Eng. Approval (if required)"]
[null,"Part Name / Description ",null,null,"Plan Prepared By",null,null,null,null,"Customer Quality Approval (if required)",null,null,null,null,null,"Critical","A product characteristics for which reasonably aniticpated variation is likely to significantly affect the products safety or complinace with regulation."]
[null,"Part / Process No","Process Name / Operation Description","Tool/Jig","Characteristic",null,null,"Characteristic Class","Methods",null,null,null,null,"Reaction Plan"]
[null,null,null,null,"No.","Product","Process",null,"Product / Process / Specification / Tolerence","Evaluation  Measurements Technique","Sample",null,"Control method"]
[null,null,null,null,null,null,null,null,null,null,"Size","Frequency"]

{"sheet":"GageRNR","index":9,"n_rows":256,"n_cols":21,"rows_kept":256,"cut":false,"hidden":true,"merged":[[61,2,61,5],[62,2,62,5],[61,6,61,11],[57,10,57,11],[58,10,58,11],[0,0,1,0],[8,0,11,0],[12,0,14,0],[0,1,0,12],[0,15,1,15],[5,6,5,10],[5,14,6,15],[6,6,6,10],[7,6,7,10],[7,14,8,15],[8,6,8,8],[9,6,9,8],[9,14,10,15],[10,6,10,8],[11,14,13,15],[0,16,0,19],[2,6,2,7],[3,6,3,10],[3,14,4,15],[4,6,4,10],[69,6,69,11],[210,13,210,14],[69,2,69,5],[52,2,52,7],[53,2,53,6],[54,2,54,6],[55,2,55,6],[62,6,62,11],[52,10,52,11],[53,10,53,11],[54,10,54,11],[55,10,55,11],[56,10,56,11],[56,2,56,6],[57,2,57,6],[58,2,58,6]]}
["Variable \nGage R&R","Variables Study Gage R & R"]
[null,"Table 1",null,null,"Set Up"]
[null,null,null,null,null,"Date:"]
[null,"# Parts",null,null,null,"Test Description:"]
[null,"# Trials",null,null,null,"Product Description:"]
[null,"# Appraisers",null,null,null,"Part No / Name:"]
[null,"# Tolerance",null,null,null,"Gage Description:"]
[null,null,null,null,null,"Special Conditions:"]
[null,null,null,null,null,"Appraiser #1:"]
[null,null,null,null,null,"Appraiser #2:"]
[null,null,null,null,null,"Appraiser #3:"]
[null,"Table 2"]
[]
[null,"Appraiser 1"]
[null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,{"$f":"(SUM(D15:M15))/$C$4","v":{"$error":"#DIV/0!"}}]
[null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,{"$f":"(SUM(D16:M16))/$C$4","v":{"$error":"#DIV/0!"}}]
[null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,{"$f":"(SUM(D17:M17))/$C$4","v":{"$error":"#DIV/0!"}}]
[null,null,"Average",{"$f":"IF($C$5=2,AVERAGE(D15:D16),AVERAGE(D15:D17))","v":{"$error":"#DIV/0!"}},{"$f":"IF($C$5=2,AVERAGE(E15:E16),AVERAGE(E15:E17))","v":{"$error":"#DIV/0!"}},{"$error":"#DIV/0!"},{"$error":"#DIV/0!"},{"$error":"#DIV/0!"},{"$error":"#DIV/0!"},{"$error":"#DIV/0!"},{"$error":"#DIV/0!"},{"$error":"#DIV/0!"},{"$error":"#DIV/0!"},null,"Xa",null,null,null,null,null,{"$f":"(SUM(U15:U17))/$C$5","v":{"$error":"#DIV/0!"}}]
[null,null,"Range",{"$f":"IF($C$5=2,MAX(D15,D16)-MIN(D15,D16),MAX(D15,D16,D17)-MIN(D15,D16,D17))","v":0},{"$f":"IF($C$5=2,MAX(E15,E16)-MIN(E15,E16),MAX(E15,E16,E17)-MIN(E15,E16,E17))","v":0},0,0,0,0,0,0,0,0,null,"Ra",null,null,null,null,null,{"$f":"(SUM(D19:M19))/($C$4)","v":{"$error":"#DIV/0!"}}]
[null,null,"sum",{"$f":"SUM(D15:D17)","v":0},{"$f":"SUM(E15:E17)","v":0},0,0,0,0,0,0,0,0]
[]
[null,"Appraiser 2"]
[null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,{"$f":"(SUM(D23:M23))/$C$4","v":{"$error":"#DIV/0!"}}]
[null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,{"$f":"(SUM(D24:M24))/$C$4","v":{"$error":"#DIV/0!"}}]
[null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,{"$f":"(SUM(D25:M25))/$C$4","v":{"$error":"#DIV/0!"}}]
[null,null,"Average",{"$f":"IF($C$5=2,AVERAGE(D23:D24),AVERAGE(D23:D25))","v":{"$error":"#DIV/0!"}},{"$f":"IF($C$5=2,AVERAGE(E23:E24),AVERAGE(E23:E25))","v":{"$error":"#DIV/0!"}},{"$error":"#DIV/0!"},{"$error":"#DIV/0!"},{"$error":"#DIV/0!"},{"$error":"#DIV/0!"},{"$error":"#DIV/0!"},{"$error":"#DIV/0!"},{"$error":"#DIV/0!"},{"$error":"#DIV/0!"},null,"Xb",null,null,null,null,null,{"$f":"(SUM(U23:U25))/$C$5","v":{"$error":"#DIV/0!"}}]
[null,null,"Range",{"$f":"IF($C$5=2,MAX(D23,D24)-MIN(D23,D24),MAX(D23,D24,D25)-MIN(D23,D24,D25))","v":0},{"$f":"IF($C$5=2,MAX(E23,E24)-MIN(E23,E24),MAX(E23,E24,E25)-MIN(E23,E24,E25))","v":0},0,0,0,0,0,0,0,0,null,"Rb",null,null,null,null,null,{"$f":"(SUM(D27:M27))/($C$4)","v":{"$error":"#DIV/0!"}}]
[null,null,"Sum",{"$f":"SUM(D23:D25)","v":0},{"$f":"SUM(E23:E25)","v":0},0,0,0,0,0,0,0,0]
[null,"Appraiser 3"]
[null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,{"$f":"(SUM(D30:M30))/$C$4","v":{"$error":"#DIV/0!"}}]
[null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,{"$f":"(SUM(D31:M31))/$C$4","v":{"$error":"#DIV/0!"}}]
[null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,{"$f":"(SUM(D32:M32))/$C$4","v":{"$error":"#DIV/0!"}}]
[null,null,"Average",{"$f":"IF($C$5=2,AVERAGE(D30:D31),AVERAGE(D30:D32))","v":{"$error":"#DIV/0!"}},{"$f":"IF($C$5=2,AVERAGE(E30:E31),AVERAGE(E30:E32))","v":{"$error":"#DIV/0!"}},{"$error":"#DIV/0!"},{"$error":"#DIV/0!"},{"$error":"#DIV/0!"},{"$error":"#DIV/0!"},{"$error":"#DIV/0!"},{"$error":"#DIV/0!"},{"$error":"#DIV/0!"},{"$error":"#DIV/0!"},{"$f":"(SUM(U30:U32))/$C$5","v":{"$error":"#DIV/0!"}},"Xc"]
[null,null,"Range",{"$f":"IF($C$5=2,MAX(D30,D31)-MIN(D30,D31),MAX(D30,D31,D32)-MIN(D30,D31,D32))","v":0},{"$f":"IF($C$5=2,MAX(E30,E31)-MIN(E30,E31),MAX(E30,E31,E32)-MIN(E30,E31,E32))","v":0},0,0,0,0,0,0,0,0,{"$f":"(SUM(D34:M34))/($C$4)","v":{"$error":"#DIV/0!"}},"Rc"]
[null,null,"sum",{"$f":"SUM(D30:D32)","v":0},{"$f":"SUM(E30:E32)","v":0},0,0,0,0,0,0,0,0]
[null,null,"Total sum",{"$f":"D20+D28+D35","v":0},{"$f":"E20+E28+E35","v":0},0,0,0,0,0,0,0,0]
[]
[null,null,"Part Average",{"$f":"(D36/($C$5*$C$6))","v":{"$error":"#DIV/0!"}},{"$f":"(E36/($C$5*$C$6))","v":{"$error":"#DIV/0!"}},{"$error":"#DIV/0!"},{"$error":"#DIV/0!"},{"$error":"#DIV/0!"},{"$error":"#DIV/0!"},{"$error":"#DIV/0!"},{"$error":"#DIV/0!"},{"$error":"#DIV/0!"},{"$error":"#DIV/0!"}]
[]
[]
[null,"R=",{"$f":"(U19+U27+N34)/C6","v":{"$error":"#DIV/0!"}},null,"K1",{"$f":"IF($C$5=2,0.8862,0.5908)","v":0.59079999999999999},null,"EV^2",{"$f":"B65^2","v":{"$error":"#DIV/0!"}},null,"parts*apprs",{"$f":"C4*C6","v":0}]
[null,"UCL=",{"$f":"C41*(IF($C$5=2,3.27,2.58))","v":{"$error":"#DIV/0!"}},null,"K2",{"$f":"IF($C$6=2,0.7071,0.5231)","v":0.52310000000000001},null,"AV^2",{"$f":"B66^2","v":{"$error":"#DIV/0!"}},null,"r/STD",{"$f":"ABS((C41/C46))","v":{"$error":"#DIV/0!"}}]
[null,"LCL=",0,null,"parts*trials",{"$f":"C4*C5","v":0},null,"R&R^2",{"$f":"B67^2","v":{"$error":"#DIV/0!"}}]
[null,"Xdiff=",{"$f":"IF($C$6=2,MAX(U18,U26)-MIN(U18,U26),MAX(U18,U26,N33)-MIN(U18,U26,N33))","v":{"$error":"#DIV/0!"}},null,"Xdiff * K2",{"$f":"C44*F42","v":{"$error":"#DIV/0!"}},null,"PV^2",{"$f":"B68^2","v":{"$error":"#DIV/0!"}}]
[null,null,null,null,"Xdiff*K2^2",{"$f":"F44^2","v":{"$error":"#DIV/0!"}},null,"F46",{"$f":"SQRT((ABS(((C44/IF(C6=2,1.41,1.91)*5.15)^2)-((5.15*L42)^2/F43))))","v":{"$error":"#DIV/0!"}}]
[null,"std",{"$f":"IF(AND(C5=2,L41=10),1.16,IF(AND(C5=2,L41<=15),1.15,IF(AND(C5=2,L41>15),1.128,IF(AND(C5=3,L41=10),1.72,IF(AND(C5=3,L41<=15),1.71,IF(AND(C6=3,L41>15),1.693))))))","v":false}]
[]
[]
[]
[]
[]
[]
[null,null,null,null,null,null,null,null,"% \nStudy Variation","%\nTolerance\nVariation","%\nContribution"]
[null,null,"Total Repeatability & Reproducibility",null,null,null,null,"R&R =",{"$f":"B67/B69","v":{"$error":"#DIV/0!"}},{"$f":"IF(C7>0,(D67/C7),\"\")","v":""},{"$f":"G67/G69","v":{"$error":"#DIV/0!"}}]
[null,null,"Repeatability - Equipment Variation",null,null,null,null,"EV =",{"$f":"(B65/B69)","v":{"$error":"#DIV/0!"}},{"$f":"IF(C7>0,D65/C7,\"\")","v":""},{"$f":"G65/G69","v":{"$error":"#DIV/0!"}}]
[null,null,"Reproducibility - Appraiser Variation",null,null,null,null,"AV = ",{"$f":"B66/B69","v":{"$error":"#DIV/0!"}},{"$f":"IF(C7>0,(I45/C7),\"\")","v":""},{"$f":"G66/G69","v":{"$error":"#DIV/0!"}}]
[null,null,"Part to Part Variation",null,null,null,null,"PV = ",{"$f":"B68/B69","v":{"$error":"#DIV/0!"}},{"$f":"IF(C7>0,D68/C7,\"\")","v":""},{"$f":"G68/G69","v":{"$error":"#DIV/0!"}}]
[null,null,"Total Variation",null,null,null,null,"TV = ",{"$f":"B69/B69","v":{"$error":"#DIV/0!"}},{"$f":"IF(C7>0,D69/C7,\"\")","v":""},{"$f":"G69/G69","v":{"$error":"#DIV/0!"}}]
[null,null,"Distinct Categories",null,null,null,null,null,{"$f":"(D68/D67)*1.41","v":{"$error":"#DIV/0!"}}]
[]
[null,null,"RESULTS"]
[null,null,"Study Variation",null,null,null,{"$f":"IF(I54=\"\",\"\",IF(I54<0.1,\"Gage is OK\",IF(I54<=0.3,\"Gage may be acceptable based upon importance of application and cost factor\",IF(I54>0.3,\"Gage is unacceptable\",\"\"))))","v":{"$error":"#DIV/0!"}}]
[null,null,"Tolerance Variation",null,null,null,{"$f":"IF(J54=\"\",\"\",IF(J54<0.1,\"Gage is OK\",IF(J54<=0.3,\"Gage may be acceptable based upon importance of application and cost factor\",IF(J54>0.3,\"Gage is unacceptable\",\"\"))))","v":""}]
[null,"Unit Analysis",null,"unit * 5.15",null,null,"Variance"]
[null,{"$f":"C41*F41","v":{"$error":"#DIV/0!"}},"EV",{"$f":"B65*5.15","v":{"$error":"#DIV/0!"}},null,null,{"$f":"(D65/5.15)^2","v":{"$error":"#DIV/0!"}}]
[null,{"$f":"(SQRT(ABS(F45-(I41/F43))))","v":{"$error":"#DIV/0!"}},"AV",{"$f":"B66*5.15","v":{"$error":"#DIV/0!"}},null,null,{"$f":"(D66/5.15)^2","v":{"$error":"#DIV/0!"}}]
[null,{"$f":"(SQRT((I41+I42)))","v":{"$error":"#DIV/0!"}},"R&R",{"$f":"B67*5.15","v":{"$error":"#DIV/0!"}},null,null,{"$f":"(D67/5.15)^2","v":{"$error":"#DIV/0!"}}]
[null,{"$f":"IF(C4=5,(MAX(D38:H38)-MIN(D38:H38))*0.403,IF(C4=6,(MAX(D38:I38)-MIN(D38:I38))*0.3742,IF(C4=7,(MAX(D38:J38)-MIN(D38:J38))*0.3534,IF(C4=8,(MAX(D38:K38)-MIN(D38:K38))*0.3375,IF(C4=9,(MAX(D38:L38)-MIN(D38:L38))*0.3249,(MAX(D38:M38)-MIN(D38:M38))*0.3146)))))","v":{"$error":"#DIV/0!"}},"PV",{"$f":"B68*5.15","v":{"$error":"#DIV/0!"}},null,null,{"$f":"(D68/5.15)^2","v":{"$error":"#DIV/0!"}}]
[null,{"$f":"SQRT(I43+I44)","v":{"$error":"#DIV/0!"}},"TV",{"$f":"B69*5.15","v":{"$error":"#DIV/0!"}},null,null,{"$f":"(D69/5.15)^2","v":{"$error":"#DIV/0!"}}]
[null,null,"Distinct Categories",null,null,null,{"$f":"IF(I59<4.999999999,\"Gage resolution is not acceptable, increase discrimination of measurement system\",\"Gage is OK\")","v":{"$error":"#DIV/0!"}}]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[null,"Demo 1"]
[null,10]
[null,2]
[null,3]
[null,5]
[]
[]
[null,0.65,1,0.85,0.85,0.55000000000000004,1,0.95,0.85,1,0.6]
[null,0.6,1,0.8,0.95,0.45,1,0.95,0.8,1,0.7]
[]
[]
[null,0.55000000000000004,1.05,0.8,0.8,0.4,1,0.95,0.75,1,0.55000000000000004]
[null,0.55000000000000004,0.95,0.75,0.75,0.4,1.05,0.9,0.7,0.95,0.5]
[]
[]
[null,0.5,1.05,0.8,0.8,0.45,1,0.95,0.8,1.05,0.85]
[null,0.55000000000000004,1,0.8,0.8,0.5,1.05,0.95,0.8,1.05,0.8]
[]
[]
[null,"Demo 2"]
[null,10]
[null,3]
[null,3]
[]
[]
[]
[null,1.2,2.5,1.9,2.4,3.1,2.5,1.8,2.5,2.2000000000000002,2.5]
[null,1.4,2.8,1.5,2.4,3.4,2.7,1.6,2.5,1.9,2.2000000000000002]
[null,1.9,2.2000000000000002,1.5,2.1,3.3,2.7,1.3,2.4,1.8,2.5]
[]
[null,1.1000000000000001,2.6,1.8,2.5,3.2,2.4,1.9,2.5,2.1,2.4]
[null,1.6,2.5,1.6,2.2999999999999998,3.1,2.8,1.5,2.6,1.8,2.1]
[null,1.5,2.2999999999999998,1.5,2.8,3.4,2.6,1.2,2.5,1.7,2.6]
[]
[null,1,2.7,1.7,2.6,3.3,2.6,1.7,2.5,2,2.2999999999999998]
[null,1.8,2.6,1.4,2.2000000000000002,3.2,2.9,1.4,2.7,1.7,2.4]
[null,4,2.5,1.7,2.7,3.5,2.5,1.1000000000000001,2.5,1.6,2.4]
[]
[]
[]
[null,"Demo 3"]
[null,5]
[null,2]
[null,2]
[]
[]
[]
[null,5,1,4.5,7.1,5]
[null,5.3,1.2,4.4000000000000004,7.3,5.2]
[]
[]
[null,5.0999999999999996,1.1000000000000001,4.5999999999999996,7.2,5.0999999999999996]
[null,5.2,1.3,4.3,7.4,5.3]
[]
[]
[]
[null,"Demo 4"]
[null,10]
[null,3]
[null,3]
[]
[]
[]
[null,0.28999999999999998,-0.56000000000000005,1.34,0.47,-0.8,0.02,0.59,-0.31,2.2599999999999998,-1.36]
[null,0.41,-0.68,1.17,0.5,-0.92,-0.11,0.75,-0.2,1.99,-1.25]
[null,0.64,-0.57999999999999996,1.27,0.64,-0.84,-0.21,0.66,-0.17,2.0099999999999998,-1.31]
[]
[null,0.08,-0.47,1.19,0.01,-0.56000000000000005,-0.2,0.47,-0.63,1.8,-1.68]
[null,0.25,-1.22,0.94,1.03,-1.2,0.22,0.55000000000000004,0.08,2.12,-1.62]
[null,7.0000000000000007E-2,-0.68,1.34,0.2,-1.28,0.06,0.83,-0.34,2.19,-1.5]
[]
[null,0.04,-1.38,0.88,0.14000000000000001,-1.46,-0.28999999999999998,0.02,-0.46,1.77,-1.49]
[null,-0.11,-1.1299999999999999,1.0900000000000001,0.2,-1.07,-0.67,0.01,-0.56000000000000005,1.45,-1.77]
[null,-0.15,-0.96,0.67,0.11,-1.45,-0.49,0.21,-0.49,1.87,-2.16]

{"sheet":"GageAttributes","index":10,"n_rows":55,"n_cols":18,"rows_kept":55,"cut":false,"hidden":true,"merged":[[8,4,8,7],[8,10,8,12],[0,0,2,0],[0,2,0,17],[3,2,3,3],[3,4,3,7],[3,9,3,12],[4,4,4,7],[5,4,5,7],[6,10,6,12],[7,2,7,3],[7,4,7,7],[7,10,7,12],[9,2,9,3],[9,4,9,7],[9,10,9,12],[11,2,11,3],[11,4,11,5],[11,6,11,7],[12,2,12,3],[12,4,12,5],[12,6,12,7],[13,2,13,3],[13,4,13,5],[13,6,13,7],[49,2,49,3],[14,2,14,3],[14,4,14,5],[14,6,14,7],[16,2,16,3],[16,4,16,5],[16,6,16,7],[16,8,16,9],[16,12,16,13],[16,14,16,15],[16,16,16,17],[48,2,48,3],[57,2,57,17],[51,4,51,5],[51,6,51,7],[51,8,51,9],[54,3,54,17],[55,2,55,17],[56,2,56,17]]}
["Gage R & R \nAttribute",null,"Gage R&R attribute"]
[]
[]
[null,null,"Observation",null,null,null,null,null,null,"Attribute"]
[]
[]
[null,null,null,null,null,null,null,null,null,"A","Approved"]
[null,null,"Gage name",null,null,null,null,null,null,"D","Defect"]
[null,null,null,null,null,null,null,null,null,"G","Go"]
[null,null,"Gage ",null,null,null,null,null,null,"S","Stop",null,null,null,"Total Gage R&R =",null,null,{"$f":"K49","v":0}]
[]
[null,null,null,null,"Name",null,"Date"]
[null,null,"Operator A"]
[null,null,"Operator B"]
[null,null,"Operator C"]
[]
[null,null,"Known attribute",null,"Operator A",null,"Operator B",null,"Operator C",null,null,null,"Operator A",null,"Operator B",null,"Operator C"]
[null,null,"Nr.","Attribute","Trial 1","Trial 2","Trial 1","Trial 2","Trial 1","Trial 2",null,null,"Trial 1","Trial 2","Trial 2","Trial 2","Trial 2","Trial 2"]
[null,null,1,null,null,null,null,null,null,null,null,null,{"$f":"IF(AND($D19<>0,E19<>0),IF($D19=E19,1,0),\"\")","v":""},"","","","",""]
[null,null,2,null,null,null,null,null,null,null,null,null,"","","","","",""]
[null,null,3,null,null,null,null,null,null,null,null,null,"","","","","",""]
[null,null,4,null,null,null,null,null,null,null,null,null,"","","","","",""]
[null,null,5,null,null,null,null,null,null,null,null,null,"","","","","",""]
[null,null,6,null,null,null,null,null,null,null,null,null,"","","","","",""]
[null,null,7,null,null,null,null,null,null,null,null,null,"","","","","",""]
[null,null,8,null,null,null,null,null,null,null,null,null,"","","","","",""]
[null,null,9,null,null,null,null,null,null,null,null,null,"","","","","",""]
[null,null,10,null,null,null,null,null,null,null,null,null,"","","","","",""]
[null,null,11,null,null,null,null,null,null,null,null,null,"","","","","",""]
[null,null,12,null,null,null,null,null,null,null,null,null,"","","","","",""]
[null,null,13,null,null,null,null,null,null,null,null,null,"","","","","",""]
[null,null,14,null,null,null,null,null,null,null,null,null,"","","","","",""]
[null,null,15,null,null,null,null,null,null,null,null,null,"","","","","",""]
[null,null,16,null,null,null,null,null,null,null,null,null,"","","","","",""]
[null,null,17,null,null,null,null,null,null,null,null,null,"","","","","",""]
[null,null,18,null,null,null,null,null,null,null,null,null,"","","","","",""]
[null,null,19,null,null,null,null,null,null,null,null,null,"","","","","",""]
[null,null,20,null,null,null,null,null,null,null,null,null,"","","","","",""]
[null,null,21,null,null,null,null,null,null,null,null,null,"","","","","",""]
[null,null,22,null,null,null,null,null,null,null,null,null,"","","","","",""]
[null,null,23,null,null,null,null,null,null,null,null,null,"","","","","",""]
[null,null,24,null,null,null,null,null,null,null,null,null,"","","","","",""]
[null,null,25,null,null,null,null,null,null,null,null,null,"","","","","",""]
[null,null,26,null,null,null,null,null,null,null,null,null,"","","","","",""]
[null,null,27,null,null,null,null,null,null,null,null,null,"","","","","",""]
[null,null,28,null,null,null,null,null,null,null,null,null,"","","","","",""]
[null,null,29,null,null,null,null,null,null,null,null,null,"","","","","",""]
[null,null,30,null,null,null,null,null,null,null,null,null,"","","","","",""]
[null,null,"Result",null,{"$f":"IF(30-COUNTBLANK(M19:M48)<>0,SUM(M19:M48)/(30-COUNTBLANK(M19:M48)),0)","v":0},0,0,0,0,0,{"$f":"F50*H50*J50","v":0}]
[null,null,"Result per operator",null,null,{"$f":"IF(60-COUNTBLANK(M19:N48)<>0,SUM(M19:N48)/(60-COUNTBLANK(M19:N48)),0)","v":0},null,{"$f":"IF(60-COUNTBLANK(O19:P48)<>0,SUM(O19:P48)/(60-COUNTBLANK(O19:P48)),0)","v":0},null,{"$f":"IF(60-COUNTBLANK(Q19:R48)<>0,SUM(Q19:R48)/(60-COUNTBLANK(Q19:R48)),0)","v":0}]
[null,null,null,null,"Trial 1","Trial 2","Trial 1","Trial 2","Trial 1","Trial 2"]
[null,null,null,null,"Operator A",null,"Operator B",null,"Operator C"]
[]
[]
[null,null,"Notes:"," "]

{"sheet":"CpCpk","index":11,"n_rows":74,"n_cols":9,"rows_kept":74,"cut":false,"hidden":true,"merged":[[7,8,7,9],[11,5,13,10],[0,1,0,15],[2,2,2,5],[2,9,2,11],[3,2,3,7],[3,9,3,11],[6,8,6,9]]}
["Cp & Cpk\nCalculation","Cp & Cpk Calculation"]
[]
[null,"Process No. or unique ID"]
[null,"Process Description"]
[null,"Number of Subgroups"]
[null,"Upper Specification Limit"]
[null,"Lower Specification Limit",null,null,null,null,null,null,"First Subgroup"]
[null,"Process Average (Xbar)",null,null,null,null,null,null,"Last Subgroup"]
[]
[]
[]
[null,null,null,null,null,"This can be obtained using statistical software e.g. Minitab, Excel, or using the estimator using Rbar/D2"]
[null,"Process Potential (Cp):-","Std dev"]
[]
[null,null,"Cp Value:",null,{"$f":"IF(OR(C70=\"\",C71=\"\"),\"\",C70/C71)","v":""}]
[]
[]
[]
[]
[null,"Process Capability (Cpk):-","Cpk Upper",null,{"$f":"IF(E13=\"\",\"\",(C6-C8)/(3*E13))","v":""}]
[]
[null,null,"Cpk Lower",null,{"$f":"IF(C8=\"\",\"\",(C8-C7)/(3*E13))","v":""}]
[]
[null,null,"Cpk Value:",null,{"$f":"IF(E20=\"\",\"\",MIN(E20,E22))","v":""}]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[null,null,{"$f":"IF(E13=\"\",22,IF(C6=\"\",\"N/A\",IF(C7=\"\",\"N/A\",C6-C7)))","v":22}," ",3]
[null,null,{"$f":"IF(C70=\"N/A\",\"N/A\",IF(E13=\"\",\"\",E13*6))","v":""},null,34]
[null,null,null,null,24]
[null,null,null,null,30]
[null,null,null,null,1.36]

{"sheet":"CauseEffectMatrix","index":12,"n_rows":37,"n_cols":21,"rows_kept":37,"cut":false,"hidden":true,"merged":[[38,4,38,20],[39,4,39,20],[40,4,40,20],[41,4,41,20],[4,10,4,11],[37,4,37,20],[7,3,7,4],[0,0,3,0],[3,10,3,19],[0,2,0,20],[31,2,31,4],[36,4,36,20]]}
["Cause & Effect\nMatrix",null,"Cause & Effect Matrix"]
[]
[]
[null,null,null,"Date",null,null,null,null,null,"Process : "]
[null,null,null,"Dept.",null,null,null,null,null,"Score",{"$f":"MAX(U12:U32)","v":0}]
[null,null,null,"Resp."]
[]
[null,null,null,"Rating of importance to customer"]
[null,null,null,null,null,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15]
[null,null," "]
[null,null,null,"Process step","Process Input",null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"Total"]
[null,null,1,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,{"$f":"SUM(F$8*F12,G$8*G12,H$8*H12,I$8*I12,J$8*J12,K$8*K12,L$8*L12,M$8*M12,N$8*N12,O$8*O12,P$8*P12,Q$8*Q12,R$8*R12,S$8*S12,T$8*T12)","v":0}]
[null,null,2,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,{"$f":"SUM(F$8*F13,G$8*G13,H$8*H13,I$8*I13,J$8*J13,K$8*K13,L$8*L13,M$8*M13,N$8*N13,O$8*O13,P$8*P13,Q$8*Q13,R$8*R13,S$8*S13,T$8*T13)","v":0}]
[null,null,3,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,0]
[null,null,4,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,0]
[null,null,5,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,0]
[null,null,6,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,0]
[null,null,7,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,0]
[null,null,8,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,0]
[null,null,9,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,0]
[null,null,10,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,0]
[null,null,11,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,0]
[null,null,12,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,0]
[null,null,13,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,0]
[null,null,14,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,0]
[null,null,15,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,0]
[null,null,16,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,0]
[null,null,17,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,0]
[null,null,18,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,{"$f":"SUM(F$8*F29,G$8*G29,H$8*H29,I$8*I29,J$8*J29,K$8*K29,L$8*L29,M$8*M29,N$8*N29,O$8*O29,P$8*P29,Q$8*Q29,R$8*R29,S$8*S29,T$8*T29)","v":0}]
[null,null,19,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,{"$f":"SUM(F$8*F30,G$8*G30,H$8*H30,I$8*I30,J$8*J30,K$8*K30,L$8*L30,M$8*M30,N$8*N30,O$8*O30,P$8*P30,Q$8*Q30,R$8*R30,S$8*S30,T$8*T30)","v":0}]
[null,null,20,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,{"$f":"SUM(F$8*F31,G$8*G31,H$8*H31,I$8*I31,J$8*J31,K$8*K31,L$8*L31,M$8*M31,N$8*N31,O$8*O31,P$8*P31,Q$8*Q31,R$8*R31,S$8*S31,T$8*T31)","v":0}]
[null,null,"Total",null,null,{"$f":"SUM(F8*F12,F8*F13,F8*F14,F8*F15,F8*F16,F8*F17,F8*F18,F8*F19,F8*F20,F8*F21,F8*F22,F8*F23,F8*F24,F8*F25,F8*F26,F8*F27,F8*F28,F8*F29,F8*F30,F8*F31)","v":0},0,0,0,0,0,0,0,0,0,0,0,0,0,0]
[null,null,null,null,"LSL"]
[null,null,null,null,"Target"]
[null,null,null,null,"OSL"]
[]
[null,null,"Notes"]

{"sheet":"FishboneData","index":13,"n_rows":21,"n_cols":8,"rows_kept":21,"cut":false,"hidden":true,"merged":[[2,4,2,6],[0,0,1,0],[0,1,0,7]]}
["Fishbone\nwhy, why","Fishbone"]
[]
[null,null,"                Defect description"]
[]
[null,null,"Main cause","Why?","Why?","Why?","Why?","Why?"]
[null,"People"]
[]
[]
[null,"Machine"]
[]
[]
[null,"Method"]
[]
[]
[null,"Material"]
[]
[]
[null,"Measuring"]
[]
[]
[null,"Environment"]

{"sheet":"FishboneModel","index":14,"n_rows":33,"n_cols":36,"rows_kept":33,"cut":false,"hidden":true,"merged":[[0,0,2,0],[0,2,0,37]]}
["Fishbone",null,{"$f":"FishboneData!E3","v":0}]
[]
[null,null,null,null,{"$f":"FishboneData!B12","v":"Method"},null,null,null,null,null,null,null,null,null,null,null,{"$f":"FishboneData!B9","v":"Machine"},null,null,null,null,null,null,null,null,null,null,null,{"$f":"FishboneData!B6","v":"People"}]
[]
[null,null,null,{"$f":"FishboneData!C12","v":0},null,null,null,null,null,null,null,null,null,null,null,{"$f":"FishboneData!C9","v":0},null,null,null,null,null,null,null,null,null,null,null,{"$f":"FishboneData!C6","v":0}]
[null,null,{"$f":"FishboneData!D12","v":0},null,null,null,null,null,null,null,null,null,null,null,{"$f":"FishboneData!D9","v":0},null,null,null,null,null,null,null,null,null,null,null,{"$f":"FishboneData!D6","v":0}]
[null,null,{"$f":"FishboneData!E12","v":0},null,null,null,null,{"$f":"FishboneData!C13","v":0},null,null,null,null,null,null,{"$f":"FishboneData!E9","v":0},null,null,null,null,{"$f":"FishboneData!C10","v":0},null,null,null,null,null,null,{"$f":"FishboneData!E6","v":0},null,null,null,null,{"$f":"FishboneData!C7","v":0}]
[null,null,{"$f":"FishboneData!F12","v":0},null,null,null,null,null,{"$f":"FishboneData!D13","v":0},null,null,null,null,null,{"$f":"FishboneData!F9","v":0},null,null,null,null,null,{"$f":"FishboneData!D10","v":0},null,null,null,null,null,{"$f":"FishboneData!F6","v":0},null,null,null,null,null,{"$f":"FishboneData!D7","v":0}]
[null,null,{"$f":"FishboneData!G12","v":0},null,null,null,null,null,{"$f":"FishboneData!E13","v":0},null,null,null,null,null,{"$f":"FishboneData!G9","v":0},null,null,null,null,null,{"$f":"FishboneData!E10","v":0},null,null,null,null,null,{"$f":"FishboneData!G6","v":0},null,null,null,null,null,{"$f":"FishboneData!E7","v":0}]
[null,null,{"$f":"FishboneData!H12","v":0},null,null,null,null,null,{"$f":"FishboneData!F13","v":0},null,null,null,null,null,{"$f":"FishboneData!H9","v":0},null,null,null,null,null,{"$f":"FishboneData!F10","v":0},null,null,null,null,null,{"$f":"FishboneData!H6","v":0},null,null,null,null,null,{"$f":"FishboneData!F7","v":0}]
[null,null,null,null,null,{"$f":"FishboneData!C14","v":0},null,null,{"$f":"FishboneData!G13","v":0},null,null,null,null,null,null,null,null,{"$f":"FishboneData!C11","v":0},null,null,{"$f":"FishboneData!G10","v":0},null,null,null,null,null,null,null,null,{"$f":"FishboneData!C8","v":0},null,null,{"$f":"FishboneData!G7","v":0}]
[null,null,null,null,{"$f":"FishboneData!D14","v":0},null,null,null,{"$f":"FishboneData!H13","v":0},null,null,null,null,null,null,null,{"$f":"FishboneData!D11","v":0},null,null,null,{"$f":"FishboneData!H10","v":0},null,null,null,null,null,null,null,{"$f":"FishboneData!D8","v":0},null,null,null,{"$f":"FishboneData!H7","v":0}]
[null,null,null,null,{"$f":"FishboneData!E14","v":0},null,null,null,null,null,null,null,null,null,null,null,{"$f":"FishboneData!E11","v":0},null,null,null,null,null,null,null,null,null,null,null,{"$f":"FishboneData!E8","v":0}]
[null,null,null,null,{"$f":"FishboneData!F14","v":0},null,null,null,null,null,null,null,null,null,null,null,{"$f":"FishboneData!F11","v":0},null,null,null,null,null,null,null,null,null,null,null,{"$f":"FishboneData!F8","v":0}]
[null,null,null,null,{"$f":"FishboneData!G14","v":0},null,null,null,null,null,null,null,null,null,null,null,{"$f":"FishboneData!G11","v":0},null,null,null,null,null,null,null,null,null,null,null,{"$f":"FishboneData!G8","v":0}]
[null,null,null,null,{"$f":"FishboneData!H14","v":0},null,null,null,null,null,null,null,null,null,null,null,{"$f":"FishboneData!H11","v":0},null,null,null,null,null,null,null,null,null,null,null,{"$f":"FishboneData!H8","v":0}]
[]
[]
[]
[null,null,null,null,null,null,null,null,null,{"$f":"FishboneData!C22","v":0},null,null,null,null,null,null,null,null,null,null,null,null,{"$f":"FishboneData!C19","v":0},null,null,null,null,null,null,null,null,null,null,null,{"$f":"FishboneData!C16","v":0}]
[null,null,null,null,null,null,null,null,null,null,{"$f":"FishboneData!D22","v":0},null,null,null,null,null,null,null,null,null,null,null,null,{"$f":"FishboneData!D19","v":0},null,null,null,null,null,null,null,null,null,null,null,{"$f":"FishboneData!D16","v":0}]
[null,null,null,null,null,null,{"$f":"FishboneData!C21","v":0},null,null,null,{"$f":"FishboneData!E22","v":0},null,null,null,null,null,null,null,{"$f":"FishboneData!C18","v":0},null,null,null,null,{"$f":"FishboneData!E19","v":0},null,null,null,null,null,null,{"$f":"FishboneData!C15","v":0},null,null,null,null,{"$f":"FishboneData!E16","v":0}]
[null,null,null,null,{"$f":"FishboneData!D21","v":0},null,null,null,null,null,{"$f":"FishboneData!F22","v":0},null,null,null,null,null,{"$f":"FishboneData!D18","v":0},null,null,null,null,null,null,{"$f":"FishboneData!F19","v":0},null,null,null,null,null,{"$f":"FishboneData!D15","v":0},null,null,null,null,null,{"$f":"FishboneData!F16","v":0}]
[null,null,null,null,{"$f":"FishboneData!E21","v":0},null,null,null,null,null,{"$f":"FishboneData!G22","v":0},null,null,null,null,null,{"$f":"FishboneData!E18","v":0},null,null,null,null,null,null,{"$f":"FishboneData!G19","v":0},null,null,null,null,null,{"$f":"FishboneData!E15","v":0},null,null,null,null,null,{"$f":"FishboneData!G16","v":0}]
[null,null,null,null,{"$f":"FishboneData!F21","v":0},null,null,null,null,null,{"$f":"FishboneData!H22","v":0},null,null,null,null,null,{"$f":"FishboneData!F18","v":0},null,null,null,null,null,null,{"$f":"FishboneData!H19","v":0},null,null,null,null,null,{"$f":"FishboneData!F15","v":0},null,null,null,null,null,{"$f":"FishboneData!H16","v":0}]
[null,null,null,null,{"$f":"FishboneData!G21","v":0},null,null,null,null,null,null,null,null,null,null,null,{"$f":"FishboneData!G18","v":0},null,null,null,null,null,null,null,null,null,null,null,null,{"$f":"FishboneData!G15","v":0}]
[null,null,null,null,{"$f":"FishboneData!H21","v":0},null,null,{"$f":"FishboneData!C23","v":0},null,null,null,null,null,null,null,null,{"$f":"FishboneData!H18","v":0},null,null,{"$f":"FishboneData!C20","v":0},null,null,null,null,null,null,null,null,null,{"$f":"FishboneData!H15","v":0},null,{"$f":"FishboneData!C17","v":0}]
[null,null,null,null,null,null,null,null,{"$f":"FishboneData!D22","v":0},null,null,null,null,null,null,null,null,null,null,null,{"$f":"FishboneData!D20","v":0},null,null,null,null,null,null,null,null,null,null,null,{"$f":"FishboneData!D17","v":0}]
[null,null,null,null,null,null,null,null,{"$f":"FishboneData!E23","v":0},null,null,null,null,null,null,null,null,null,null,null,{"$f":"FishboneData!E20","v":0},null,null,null,null,null,null,null,null,null,null,null,{"$f":"FishboneData!E17","v":0}]
[null,null,null,null,null,null,null,null,{"$f":"FishboneData!F23","v":0},null,null,null,null,null,null,null,null,null,null,null,{"$f":"FishboneData!F20","v":0},null,null,null,null,null,null,null,null,null,null,null,{"$f":"FishboneData!F17","v":0}]
[null,null,null,null,null,null,null,null,{"$f":"FishboneData!G23","v":0},null,null,null,null,null,null,null,null,null,null,null,{"$f":"FishboneData!G20","v":0},null,null,null,null,null,null,null,null,null,null,null,{"$f":"FishboneData!G17","v":0}]
[null,null,null,null,null,null,null,null,{"$f":"FishboneData!H23","v":0},null,null,null,null,null,null,null,null,null,null,null,{"$f":"FishboneData!H20","v":0},null,null,null,null,null,null,null,null,null,null,null,{"$f":"FishboneData!H17","v":0}]
[null,null,null,{"$f":"FishboneData!B21","v":"Environment"},null,null,null,null,null,null,null,null,null,null,null,{"$f":"FishboneData!B18","v":"Measuring"},null,null,null,null,null,null,null,null,null,null,null,null,{"$f":"FishboneData!B15","v":"Material"}]

{"sheet":"FMEA","index":15,"n_rows":43,"n_cols":17,"rows_kept":43,"cut":false,"hidden":true,"merged":[[6,1,6,9],[6,10,6,12],[6,13,6,16],[2,15,5,15],[2,16,5,16],[3,4,3,6],[3,8,3,10],[4,4,4,6],[5,4,5,6],[2,12,5,14],[0,0,2,0],[2,1,5,2],[2,4,2,6],[2,8,2,10],[2,11,5,11],[0,1,1,7],[0,8,1,16]]}
["Failure Mode &\nEffects Analysis","Failure Mode & Effects Analysis",null,null,null,null,null,null,"Failure Mode & Effect Analysis cont."]
[]
[null,null,null,"Process/Product:",null,null,null,"FMEA Date: (original)",null,null,null,"FMEA No:",null,null,null,"Issue: "]
[null,null,null,"FMEA Team:",null,null,null,"(Revised)"]
[null,null,null,"Responsibility:",null,null,null,"Page:",null,"of"]
[null,null,null,"Prepared By:"]
[null,"Analysis",null,null,null,null,null,null,null,null,"Actions",null,null,"Results"]
[null,"Process Step","Potential Failure Mode","Potential Effects of Failure","Severity                         (1-10)","Potential Cause(s) of Failure","Occurrence               (1-10)","Current Controls","Detection                        (1-10)","Risk Priority Number (RPN)","Recommended Action","Responsibility and Target Completion Date","Action Taken","Revised Severity                           (1-10)","Revised Occurrence                          (1-10)","Revised Detection                         (1-10)","Revised Risk Priority Number"]
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["FMEA\nGuidelines","Score","Severity Guidelines"]
[null,null,"AIAG",null,"Six Sigma"]
[null,10,"Hazardous without warning",null,"Injure a customer or employee",null,null,null,null,null,"Bad"]
[null,9,"Hazardous with warning",null,"Be illegal"]
[null,8,"Very High",null,"Render product or service unfit for use"]
[null,7,"High",null,"Cause extreme customer dissatisfaction"]
[null,6,"Moderate",null,"Result in partial malfunction"]
[null,5,"Low",null,"Cause a loss of performance which is likely to result in a complaint"]
[null,4,"Very Low",null,"Cause minor performance loss"]
[null,3,"Minor",null,"Cause a minor nuisance but can be overcome with no performance loss"]
[null,2,"Very Minor",null,"Be unnoticed and have only minor effect on performance"]
[null,1,"None",null,"Be unnoticed and not affect the performance",null,null,null,null,null,"Good"]
[]
[null,"Score","Occurrence Guidelines"]
[null,null,"AIAG",null,null,"Six Sigma"]
[null,10,"Very High: Persistent Failures, Ppk < 0.55",null,null,"More than once per day",null," > 30%",null,null,"Bad"]
[null,9,"Very High: Persistent Failures, Ppk >= 0.55",null,null,"Once every 3-4 days",null,"< 30%"]
[null,8,"High: Frequent Failures, Ppk >= 0.78",null,null,"Once every week",null,"< 5%"]
[null,7,"High: Frequent Failures, Ppk >= 0.86",null,null,"Once per month",null,"< 1%"]
[null,6,"Moderate: Occasional Failures, Ppk >= 0.94",null,null,"Once every 3 months",null,"< 0.03%"]
[null,5,"Moderate: Occasional Failures, Ppk >= 1.00",null,null,"Once every 6 months",null,"< 1 per 10,000"]
[null,4,"Moderate: Occasional Failures, Ppk >= 1.10",null,null,"Once per year",null,"< 6 per 100,000"]
[null,3,"Low: Relatively Few Failures, Ppk >=1.20",null,null,"Once every 1-3 years",null,"< 6 per million"]
[null,2,"Low: Relatively Few Failures, Ppk >=1.30",null,null,"Once every 3-6 years",null,"< 3 per 10 million"]
[null,1,"Remote: Failure is Unlikely, Ppk >=1.67",null,null,"Once every 6-9 years",null,"< 2 per billion",null,null,"Good"]
[]
[null,"Score","Detection Guidelines"]
[null,null,"AIAG",null,null,null,"Six Sigma"]
[null,10,"Almost Impossible: Absolute certainty of non-detection",null,null,null,"Defect caused by failure is not detectable",null,null,null,"Bad"]
[null,9,"Very Remote: Controls will probably not detect",null,null,null,"Occasional units are checked for defects"]
[null,8,"Remote: Controls have poor chance of detection",null,null,null,"Units are systematically sampled and inspected"]
[null,7,"Very Low: Controls have poor chance of detection",null,null,null,"All units are manually inspected"]
[null,6,"Low: Controls may detect",null,null,null,"Manual inspection with mistake-proofing modifications"]
[null,5,"Moderate: Controls may detect",null,null,null,"Process is monitored (SPC) and manually inspected"]
[null,4,"Moderately High: Controls have a good chance to detect",null,null,null,"SPC is used with an immediate reaction to out of control conditions"]
[null,3,"High: Controls have a good chance to detect",null,null,null,"SPC as above, 100% inspection surrounding out of control conditions"]
[null,2,"Very High: Controls almost certain to detect",null,null,null,"All units are automatically inspected"]
[null,1,"Very High: Controls certain to detect",null,null,null,"Defect is obvious and can be kept from affecting the customer",null,null,null,"Good"]

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["Design FMEA\nExample","Design FMEA Example"]
[null,"SEVERITY EVALUATION CRITERIA",null,null,null,null,"OCCURRENCE EVALUATION CRITERIA",null,null,null,null,"DETECTION EVALUATION CRITERIA"]
[null,"Effect","Criteria:  Severity of Effect\n(Customer Effect)","Criteria:  Severity of Effect\n(Manufacturing / Assembly Effect)","Rank",null,"Probability","Likely Failure Rates",null,"Rank",null,"Detection","Criteria","Suggested Range of Detection Methods","Rank"]
[null,"Hazardous -\nwithout warning","Very high severity ranking when a potential failure mode affects safe item operation and / or involves noncompliance with government regulation without warning.","Or may endanger operator (machine or assembly) without warning.",10,null,"Very High:  Persistent Failures",">/ 100 per thousand pieces",null,10,null,"Almost\nImpossible","Absolute certainty of non-detection.","Design Control will not and/or can not detect a potential cause/mechanism and subsequent failure mode; or there is no Design Control.",10]
[null,"Hazardous -\nwith warning","Very high severity ranking when a potential failure mode affects safe process operation and / or involves noncompliance with government regulation with warning.","Or may endanger operator (machine or assembly) with warning.",9,null,null,"50 per thousand pieces",null,9,null,"Very Remote","Controls will probably not detect.","Very remote chance the Design Control will detect a potential cause/mechanism and subsequent failure mode.",9]
[null,"Very High","Item inoperable (loss of primary function).","Or 100% of product may have to be scrapped, or item repaired in repair department with a repair time greater than one hour.",8,null,"High:  Frequent Failures","20 per thousand pieces",null,8,null,"Remote","Controls have poor chance of detection.","Remote chance the Design Control will detect a potential cause/mechanism and subsequent failure mode.",8]
[null,"High","Item operable, but at a reduced level of performance. Customer very dissatisfied.","Or product may have to be sorted and a portion (less than 100%) scrapped, or item repaired in repair department between a half-hour and an hour.",7,null,null,"10 per thousand pieces",null,7,null,"Very Low","Controls have poor chance of detection.","Very low chance the Design Control will detect a potential cause/mechanism and subsequent failure mode.",7]
[null,"Moderate","Item operable, but some comfort / convenience element(s) inoperable.  Customer dissatisfied.","Or a portion (less than 100%) of the product may have to be scrapped with no sorting, or item repaired in repair department with a repair time less than a half-hour.",6,null,"Moderate:  Occasional Failures","5 per thousand pieces",null,6,null,"Low","Controls may detect.","Low chance the Design Control will detect a potential cause/mechanism and subsequent failure mode.",6]
[null,"Low","Item operable, but some comfort / convenience element(s) operable at a reduced level of performance.","Or 100% of product may have to be reworked, or item repaired offline but does not go to repair department.",5,null,null,"2 per thousand pieces",null,5,null,"Moderate","Controls may detect.","Moderate chance the Design Control will detect a potential cause/mechanism and subsequent failure mode.",5]
[null,"Very Low","Fit / Finish / Squeak & Rattle item does not conform. Defect noticed by most customers (greater than 75%).","Or the product may have to be sorted, with no scrap, and a portion (less than 100%) reworked.",4,null,null,"1 per thousand pieces",null,4,null,"Moderately High","Controls have a good chance to detect.","Moderately high chance the Design Control will detect a potential cause/mechanism and subsequent failure mode.",4]
[null,"Minor","Fit / Finish / Squeak & Rattle item does not conform. Defect noticed by 50% of customers.","Or a portion (less than 100%) of the product may have to be reworked, with no scrap, on-line but out-of-station.",3,null,"Low:  Relatively Few Failures","0.5 per thousand pieces",null,3,null,"High","Controls have a good chance to detect.","High chance the Design Control will detect a potential cause/mechanism and subsequent failure mode.",3]
[null,"Very Minor","Fit / Finish / Squeak & Rattle item does not conform. Defect noticed by discriminating customers (less than 25%).","Or a portion (less than 100%) of the product may have to be reworked, with no scrap, on-line but in-station.",2,null,null,"0.1 per thousand pieces",null,2,null,"Very High","Controls almost certain to detect.","Very high chance the Design Control will detect a potential cause/mechanism and subsequent failure mode.",2]
[null,"None","No discernible effect.","Or slight inconvenience to operation or operator, or no effect.",1,null,"Remote:  Failure is Unlikely","</ 0.01 per thousand pieces",null,1,null,"Very High","Controls certain to detect.","Design Control will almost certainly detect a potential cause/mechanism and subsequent failure mode.",1]
[]
[null,null,"RPN = Risk Priority Number",null,"Classifications:",null,null,"= CC",null,"= SC or HIC",null,null,null,"= Blank"]

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["Process FMEA\nExample","Process FMEA Example"]
[null,"SEVERITY EVALUATION CRITERIA",null,null,null,null,"OCCURRENCE EVALUATION CRITERIA",null,null,null,null,"DETECTION EVALUATION CRITERIA"]
[null,"Effect","Criteria:  Severity of Effect\n(Customer Effect)","Criteria:  Severity of Effect\n(Manufacturing / Assembly Effect)","Rank",null,"Probability","Likely Failure Rates",null,"Rank",null,"Detection","Criteria","Inspection Types","Suggested Range of Detection Methods","Rank"]
[null,"Hazardous -\nwithout warning","Very high severity ranking when a potential failure mode affects safe item operation and / or involves noncompliance with government regulation without warning.","Or may endanger operator (machine or assembly) without warning.",10,null,"Very High:  Persistent Failures",">/ 100 per thousand pieces",null,10,null,"Almost\nImpossible","Absolute certainty of non-detection.","Manual Inspection","Cannot detect or is not checked.",10]
[null,"Hazardous -\nwith warning","Very high severity ranking when a potential failure mode affects safe process operation and / or involves noncompliance with government regulation with warning.","Or may endanger operator (machine or assembly) with warning.",9,null,null,"50 per thousand pieces",null,9,null,"Very Remote","Controls will probably not detect.","Manual Inspection","Control is achieved with indirect or random checks only.",9]
[null,"Very High","Item inoperable (loss of primary function).","Or 100% of product may have to be scrapped, or item repaired in repair department with a repair time greater than one hour.",8,null,"High:  Frequent Failures","20 per thousand pieces",null,8,null,"Remote","Controls have poor chance of detection.","Manual Inspection","Control is achieved with visual inspection only.",8]
[null,"High","Item operable, but at a reduced level of performance. Customer very dissatisfied.","Or product may have to be sorted and a portion (less than 100%) scrapped, or item repaired in repair department between a half-hour and an hour.",7,null,null,"10 per thousand pieces",null,7,null,"Very Low","Controls have poor chance of detection.","Manual Inspection","Control is achieved with double visual inspection only.",7]
[null,"Moderate","Item operable, but some comfort / convenience element(s) inoperable.  Customer dissatisfied.","Or a portion (less than 100%) of the product may have to be scrapped with no sorting, or item repaired in repair department with a repair time less than a half-hour.",6,null,"Moderate:  Occasional Failures","5 per thousand pieces",null,6,null,"Low","Controls may detect.","Gauging /\nManual Inspection","Control is achieved with charting methods, such as SPC (Statistical Process Control).",6]
[null,"Low","Item operable, but some comfort / convenience element(s) operable at a reduced level of performance.","Or 100% of product may have to be reworked, or item repaired offline but does not go to repair department.",5,null,null,"2 per thousand pieces",null,5,null,"Moderate","Controls may detect.","Error - Proofed /\nGauging","Control is based on variable gauging after parts have left the station, or GO/NO GO gauging performed on 100% of the parts after parts have left the station.",5]
[null,"Very Low","Fit / Finish / Squeak & Rattle item does not conform. Defect noticed by most customers (greater than 75%).","Or the product may have to be sorted, with no scrap, and a portion (less than 100%) reworked.",4,null,null,"1 per thousand pieces",null,4,null,"Moderately High","Controls have a good chance to detect.","Error - Proofed /\nGauging","Error detection in subsequent operations, or gauging performed on set-up and first piece check (for set-up causes only).",4]
[null,"Minor","Fit / Finish / Squeak & Rattle item does not conform. Defect noticed by 50% of customers.","Or a portion (less than 100%) of the product may have to be reworked, with no scrap, on-line but out-of-station.",3,null,"Low:  Relatively Few Failures","0.5 per thousand pieces",null,3,null,"High","Controls have a good chance to detect.","Error - Proofed /\nGauging","Error detection in-station, or error detection in subsequent operations by multiple layers of acceptance, supply, select, install, verify, cannot accept discrepant part.",3]
[null,"Very Minor","Fit / Finish / Squeak & Rattle item does not conform. Defect noticed by discriminating customers (less than 25%).","Or a portion (less than 100%) of the product may have to be reworked, with no scrap, on-line but in-station.",2,null,null,"0.1 per thousand pieces",null,2,null,"Very High","Controls almost certain to detect.","Error - Proofed /\nGauging","Error detection in-station (automatic gauging with automatic stop feature).  Cannot pass discrepant part.",2]
[null,"None","No discernible effect.","Or slight inconvenience to operation or operator, or no effect.",1,null,"Remote:  Failure is Unlikely","</ 0.01 per thousand pieces",null,1,null,"Very High","Controls certain to detect.","Error - Proofed ","Discrepant parts cannot be made because item has been error-proofed by process / product design.",1]
[]
[null,null,"RPN = Risk Priority Number",null,"Classifications:",null,null,"= CC",null,"= SC or HIC",null,null,null,"= Blank"]

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["Charting \nTools",null,null,null,null,null,null,"Date","Cycle Time","Cycle Time","Cycle Time",null,"Temperature C","Pressure Bar"]
[null,null,null,null,null,null,null,"2010-01-01",10.026759534582958,7.4004258762565485,9.5175811079649542,null,105,104]
[null,null,null,null,null,null,null,"2010-01-02",8.1926934788584536,10.97373803149306,9.3177281362796407,null,105,103]
[null,null,null,null,null,null,null,"2010-01-03",9.7801726880216986,12.098580287179379,11.471454522736279,null,96,96]
[null,null,null,null,null,null,null,"2010-01-04",10.186887272370686,11.428410466716379,8.482279808088812,null,102,105]
[null,null,null,null,null,null,null,"2010-01-05",9.7479743550152484,9.83316261377845,8.5,null,100,99]
[null,null,null,null,null,null,null,"2010-01-06",9.5887688574137151,8.1448297170441553,8.1177452860163477,null,102,101]
[null,null,null,null,null,null,null,"2010-01-07",9.1705292395018105,9.1514306778270686,11.679705189103055,null,102,95]
[null,null,null,null,null,null,null,"2010-01-08",10.073650291227015,7.1818967401883338,12.864685258319071,null,102,96]
[null,null,null,null,null,null,null,"2010-01-09",9.9324139773173705,9.9290397115429609,3.9745990798227471,null,102,106]
[null,null,null,null,null,null,null,"2010-01-10",10.648468591760061,11.475344138648444,13.332246607187628,null,98,100]
[null,null,null,null,null,null,null,"2010-01-11",10.605707550009649,10.710733003564949,0.502719356925315,null,95,98]
[null,null,null,null,null,null,null,"2010-01-12",10.246276436237334,10.651357708723298,4.0376863733595627,null,97,102]
[null,null,null,null,null,null,null,"2010-01-13",9.0369985329430609,9.6933961921993355,13.389342154740607,null,104,108]
[null,null,null,null,null,null,null,"2010-01-14",10.014086382024766,7.1767492835817954,8.2607165094220427,null,102,107]
[null,null,null,null,null,null,null,"2010-01-15",10.581861721128451,6.291569016178526,10.856047672179615,null,101,96]
[null,null,null,null,null,null,null,"2010-01-16",9.7590677967128681,10.567917499363123,9.1186627630447052,null,99,107]
[null,null,null,null,null,null,null,"2010-01-17",9.0828659641866576,12.530503187693053,11.403893620990065,null,103,99]
[null,null,null,null,null,null,null,"2010-01-18",11.1055134257538,9.4152609412953758,3.9100912989842396,null,104,110]
[null,null,null,null,null,null,null,"2010-01-19",8.8009276062747475,10.918938627584524,11.400931471696367,null,107,115]
[null,null,null,null,null,null,null,"2010-01-20",7.9521511704642096,7.1519794825147951,11.122077550539627,null,99,89]
[null,null,null,null,null,null,null,"2010-01-21",11.892526949376746,11.791071731441406,8.0249257724840426,null,101,105]
[null,null,null,null,null,null,null,"2010-01-22",10.593400952316538,10.686247055747943,5.0141578810577538,null,108,106]
[null,null,null,null,null,null,null,"2010-01-23",10.740641640267309,12.583145978258241,10.728414031275653,null,111,123]
[null,null,null,null,null,null,null,"2010-01-24",10.677400023088154,10.546532364389661,10.588637933002355,null,108,118]
[null,null,null,null,null,null,null,"2010-01-25",10.029537289297835,10.82940976556899,10.754636495252047,null,107,112]
[null,null,null,null,null,null,null,"2010-01-26",10.273467159104726,8.0704206435277062,2.7510000265480228]
[null,null,null,null,null,null,null,"2010-01-27",11.006851084718534,12.035570744770048,11.64684135940985]
[null,null,null,null,null,null,null,"2010-01-28",10.681412223101294,7.8972246053914583,6.028126913426699]
[null,null,null,null,null,null,null,"2010-01-29",8.785748580120897,8.3513623827924963,11.517985091971683]
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[null,{"$f":"IF(OR(ArraySheet!A485=\"\",ArraySheet!B485=\"\"),\"\",SUM(ArraySheet!$B$2:B485)/SUM(ArraySheet!$B$2:'ArraySheet'!$B$501))","v":""},{"$f":"IF(ISBLANK(ArraySheet!B485),\"\",IF(B485<=$F$1,ArraySheet!B485,\"\"))","v":""},{"$f":"IF(ISBLANK(ArraySheet!B485),\"\",IF(C485=\"\",ArraySheet!B485,\"\"))","v":""}]
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[null,{"$f":"IF(OR(ArraySheet!A488=\"\",ArraySheet!B488=\"\"),\"\",SUM(ArraySheet!$B$2:B488)/SUM(ArraySheet!$B$2:'ArraySheet'!$B$501))","v":""},{"$f":"IF(ISBLANK(ArraySheet!B488),\"\",IF(B488<=$F$1,ArraySheet!B488,\"\"))","v":""},{"$f":"IF(ISBLANK(ArraySheet!B488),\"\",IF(C488=\"\",ArraySheet!B488,\"\"))","v":""}]
[null,{"$f":"IF(OR(ArraySheet!A489=\"\",ArraySheet!B489=\"\"),\"\",SUM(ArraySheet!$B$2:B489)/SUM(ArraySheet!$B$2:'ArraySheet'!$B$501))","v":""},{"$f":"IF(ISBLANK(ArraySheet!B489),\"\",IF(B489<=$F$1,ArraySheet!B489,\"\"))","v":""},{"$f":"IF(ISBLANK(ArraySheet!B489),\"\",IF(C489=\"\",ArraySheet!B489,\"\"))","v":""}]
[null,{"$f":"IF(OR(ArraySheet!A490=\"\",ArraySheet!B490=\"\"),\"\",SUM(ArraySheet!$B$2:B490)/SUM(ArraySheet!$B$2:'ArraySheet'!$B$501))","v":""},{"$f":"IF(ISBLANK(ArraySheet!B490),\"\",IF(B490<=$F$1,ArraySheet!B490,\"\"))","v":""},{"$f":"IF(ISBLANK(ArraySheet!B490),\"\",IF(C490=\"\",ArraySheet!B490,\"\"))","v":""}]
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[null,{"$f":"IF(OR(ArraySheet!A492=\"\",ArraySheet!B492=\"\"),\"\",SUM(ArraySheet!$B$2:B492)/SUM(ArraySheet!$B$2:'ArraySheet'!$B$501))","v":""},{"$f":"IF(ISBLANK(ArraySheet!B492),\"\",IF(B492<=$F$1,ArraySheet!B492,\"\"))","v":""},{"$f":"IF(ISBLANK(ArraySheet!B492),\"\",IF(C492=\"\",ArraySheet!B492,\"\"))","v":""}]
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[null,{"$f":"IF(OR(ArraySheet!A500=\"\",ArraySheet!B500=\"\"),\"\",SUM(ArraySheet!$B$2:B500)/SUM(ArraySheet!$B$2:'ArraySheet'!$B$501))","v":""},{"$f":"IF(ISBLANK(ArraySheet!B500),\"\",IF(B500<=$F$1,ArraySheet!B500,\"\"))","v":""},{"$f":"IF(ISBLANK(ArraySheet!B500),\"\",IF(C500=\"\",ArraySheet!B500,\"\"))","v":""}]
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{"sheet":"G-Pareto","index":25,"n_rows":0,"n_cols":0,"rows_kept":0,"cut":false,"hidden":true,"merged":[]}

{"sheet":"C-Histogram","index":26,"n_rows":87,"n_cols":13,"rows_kept":87,"cut":false,"hidden":true,"merged":[]}
["Histogram",null,null,"(Bins) Interval","frequency","Cum frequency","Frequency %",null,null,"norm dist",null,"2sample","2 sample norm dist"]
[null,"data max",{"$f":"MAX(ArraySheet!$A$2:$A$501)","v":111},{"$f":"IF(C3>0,FLOOR(C6,0.5),IF(C3<0,FLOOR(C6,-0.5)))","v":94.5},{"$f":"F2","v":0},{"$f":"IF(D2=\"\",\"\",(FREQUENCY(ArraySheet!$A$2:'ArraySheet'!$A$501,D2)))","v":0},{"$f":"IF(C12=0,\"\",E2/$C$12)","v":0},-2,{"$f":"IF($C$8=\"\",\"\",H2*$C$8+$C$9)","v":94.483771941974723},{"$f":"IF($C$8=\"\",\"\",NORMDIST(I2,$C$9,$C$8,FALSE))","v":1.364057885079579E-2},null,{"$f":"IF($C$33=\"\",\"\",H2*$C$33+$C$34)","v":88.52959815217028},{"$f":"IF(L2=\"\",\"\",NORMDIST(L2,$C$34,$C$33,FALSE))","v":6.9799048588724622E-3}]
[null,"data min",{"$f":"MIN(ArraySheet!$A$2:$A$501)","v":95},{"$f":"D2+C5","v":96.3},{"$f":"IF(F3=\"\",\"\",F3-F2)","v":2},{"$f":"IF(D3=\"\",\"\",(FREQUENCY(ArraySheet!$A$2:'ArraySheet'!$A$501,D3)))","v":2},{"$f":"IF(F3=\"\",\"\",E3/$C$12)","v":0.08},-1.75,95.473300449227892,2.1797582938264611E-2,null,{"$f":"IF($C$33=\"\",\"\",IF(H3=\"\",\"\",H3*$C$33+$C$34))","v":90.463398383148984},1.1153856205566484E-2]
[null,"delta",{"$f":"C2-C3","v":16},{"$f":"IF(D3<$C$2,D3+$C$5,\"\")","v":98.1},2,{"$f":"IF(D4=\"\",\"\",(FREQUENCY(ArraySheet!$A$2:'ArraySheet'!$A$501,D4)))","v":4},0.08,-1.5,96.462828956481047,3.2722047600584205E-2,null,92.397198614127703,1.6743921320190026E-2]
[null,"Intervals",{"$f":"ROUND((C2-C3)/9,1)","v":1.8},99.899999999999991,2,{"$f":"IF(D5=\"\",\"\",(FREQUENCY(ArraySheet!$A$2:'ArraySheet'!$A$501,D5)))","v":6},0.08,-1.25,97.452357463734202,4.6145483442422169E-2,null,94.330998845106421,2.3612713772479664E-2]
[null,"Bin Min",{"$f":"C3-(C4/100)","v":94.84},101.69999999999999,3,{"$f":"IF(D6=\"\",\"\",(FREQUENCY(ArraySheet!$A$2:'ArraySheet'!$A$501,D6)))","v":9},0.12,-1,98.441885970987371,6.1132833148696282E-2,null,96.26479907608514,3.1281763318008242E-2]
[null,"delta/100",{"$f":"C4/100","v":0.16},103.49999999999999,7,{"$f":"IF(D7=\"\",\"\",(FREQUENCY(ArraySheet!$A$2:'ArraySheet'!$A$501,D7)))","v":16},0.28000000000000003,-0.75,99.431414478240526,7.6081040098262109E-2,null,98.198599307063859,3.8930783455641112E-2]
[null,"Standard Dev",{"$f":"IF(ArraySheet!A2=\"\",\"\",STDEV(ArraySheet!$A$2:'ArraySheet'!$A$501))","v":3.9581140290126386},105.29999999999998,4,{"$f":"IF(D8=\"\",\"\",(FREQUENCY(ArraySheet!$A$2:'ArraySheet'!$A$501,D8)))","v":20},0.16,-0.5,100.42094298549368,8.8947747382639927E-2,null,100.13239953804256,4.5514697061820548E-2]
[null,"Mean =",{"$f":"IF(ArraySheet!A2=\"\",\"\",AVERAGE(ArraySheet!$A$2:'ArraySheet'!$A$501))","v":102.4},107.09999999999998,2,{"$f":"IF(D9=\"\",\"\",(FREQUENCY(ArraySheet!$A$2:'ArraySheet'!$A$501,D9)))","v":22},0.08,-0.25,101.41047149274685,9.7689989214207823E-2,null,102.06619976902128,4.9988115448609788E-2]
[null,"Mean * 2",{"$f":"IF(C12=0,\"\",C9*2)","v":204.8},108.89999999999998,2,{"$f":"IF(D10=\"\",\"\",(FREQUENCY(ArraySheet!$A$2:'ArraySheet'!$A$501,D10)))","v":24},0.08,0,102.4,0.10079100235041734,null,104,5.1574908567407207E-2]
[null,null,null,110.69999999999997,0,{"$f":"IF(D11=\"\",\"\",(FREQUENCY(ArraySheet!$A$2:'ArraySheet'!$A$501,D11)))","v":24},0,0.25,103.38952850725316,9.7689989214207823E-2,null,105.93380023097872,4.9988115448609788E-2]
[null,"# data points",{"$f":"COUNT(ArraySheet!$A$2:'ArraySheet'!$A$501)","v":25},112.49999999999997,1,{"$f":"IF(D12=\"\",\"\",(FREQUENCY(ArraySheet!$A$2:'ArraySheet'!$A$501,D12)))","v":25},0.04,0.5,104.37905701450633,8.8947747382639927E-2,null,107.86760046195744,4.5514697061820548E-2]
[null,null,null,"","",{"$f":"IF(D13=\"\",\"\",(FREQUENCY(ArraySheet!$A$2:'ArraySheet'!$A$501,D13)))","v":""},"",0.75,105.36858552175948,7.6081040098262109E-2,null,109.80140069293614,3.8930783455641112E-2]
[null,"HistNormMin",{"$f":"D2","v":94.5},"","",{"$f":"IF(D14=\"\",\"\",(FREQUENCY(ArraySheet!$A$2:'ArraySheet'!$A$501,D14)))","v":""},"",1,106.35811402901264,6.1132833148696282E-2,null,111.73520092391486,3.1281763318008242E-2]
[null,"HistNormMax",{"$f":"MAX(D2:D20)","v":112.49999999999997},"","",{"$f":"IF(D15=\"\",\"\",(FREQUENCY(ArraySheet!$A$2:'ArraySheet'!$A$501,D15)))","v":""},"",1.25,107.34764253626581,4.6145483442422169E-2,null,113.66900115489358,2.3612713772479664E-2]
[null,null,null,"","",{"$f":"IF(D16=\"\",\"\",(FREQUENCY(ArraySheet!$A$2:'ArraySheet'!$A$501,D16)))","v":""},"",1.5,108.33717104351896,3.2722047600584205E-2,null,115.6028013858723,1.6743921320190026E-2]
[null,"mean","mean frequency","","",{"$f":"IF(D17=\"\",\"\",(FREQUENCY(ArraySheet!$A$2:'ArraySheet'!$A$501,D17)))","v":""},"",1.75,109.32669955077212,2.1797582938264611E-2,null,117.53660161685102,1.1153856205566484E-2]
[null,{"$f":"C9","v":102.4},{"$f":"IF(C12=0,\"\",MAX($G$2:$G$12)+1)","v":1.28},"","",{"$f":"IF(D18=\"\",\"\",(FREQUENCY(ArraySheet!$A$2:'ArraySheet'!$A$501,D18)))","v":""},"",2,110.31622805802529,1.364057885079579E-2,null,119.47040184782972,6.9799048588724622E-3]
[null,"row count",{"$f":"COUNT(ArraySheet!$A$2:'ArraySheet'!$A$501)","v":25},null,null,null,""]
[null,"Count columns"]
[null,"mean dot y value",0]
[null,"se mean",{"$f":"IF(C12=0,\"\",C8/SQRT(C12))","v":0.79162280580252775}]
[null,"95% confidence",{"$f":"IF(C12=0,\"\",C9-(TINV(0.05,C12-1)*C22))","v":100.76617082975223},{"$f":"IF(C12=0,\"\",C9+(TINV(0.05,C12-1)*C22))","v":104.03382917024778},{"$f":"IF(C23=\"\",\"\",ROUND(C23,3)&\", \"&ROUND(D23,3))","v":"100,766, 104,034"}]
[]
[]
["Two Sample",null,null,"(Bins) Interval","frequency","Cum frequency","Frequency %"]
[null,"data max",{"$f":"MAX(ArraySheet!$B$2:$B$501)","v":123},{"$f":"FLOOR(C31,0.5)","v":88.5},{"$f":"F27","v":0},{"$f":"IF(D27=\"\",\"\",(FREQUENCY(ArraySheet!$B$2:'ArraySheet'!$B$501,D27)))","v":0},{"$f":"IF($C$37=0,\"\",E27/$C$37)","v":0},null,{"$f":"ROUNDDOWN(C39,0.5)","v":88}]
[null,"data min",{"$f":"MIN(ArraySheet!$B$2:$B$501)","v":89},{"$f":"D27+C30","v":92.277777777777771},{"$f":"IF(F28=\"\",\"\",F28-F27)","v":1},{"$f":"IF(D28=\"\",\"\",(FREQUENCY(ArraySheet!$B$2:'ArraySheet'!$B$501,D28)))","v":1},0.04,null,{"$f":"I27+(($I$37-$I$27)/10)","v":90.5}]
[null,"delta",{"$f":"C27-C28","v":34},{"$f":"IF(D28<$C$2,D28+$C$5,\"\")","v":94.077777777777769},0,{"$f":"IF(D29=\"\",\"\",(FREQUENCY(ArraySheet!$B$2:'ArraySheet'!$B$501,D29)))","v":1},0,null,93]
[null,"Intervals",{"$f":"(C27-C28)/9","v":3.7777777777777777},95.877777777777766,1,{"$f":"IF(D30=\"\",\"\",(FREQUENCY(ArraySheet!$B$2:'ArraySheet'!$B$501,D30)))","v":2},0.04,null,95.5]
[null,"Bin Min",{"$f":"C28-(C29/100)","v":88.66},97.677777777777763,3,{"$f":"IF(D31=\"\",\"\",(FREQUENCY(ArraySheet!$B$2:'ArraySheet'!$B$501,D31)))","v":5},0.12,null,98]
[null,"delta/100",{"$f":"C29/100","v":0.34},99.47777777777776,3,{"$f":"IF(D32=\"\",\"\",(FREQUENCY(ArraySheet!$B$2:'ArraySheet'!$B$501,D32)))","v":8},0.12,null,100.5]
[null,"Standard Dev",{"$f":"IF(ArraySheet!$B$2=\"\",\"\",STDEV(ArraySheet!$B$2:'ArraySheet'!$B$501))","v":7.7352009239148618},101.27777777777776,2,{"$f":"IF(D33=\"\",\"\",(FREQUENCY(ArraySheet!$B$2:'ArraySheet'!$B$501,D33)))","v":10},0.08,null,103]
[null,"Mean =",{"$f":"IF(ArraySheet!B2=\"\",\"\",AVERAGE(ArraySheet!$B$2:'ArraySheet'!$B$501))","v":104},103.07777777777775,2,{"$f":"IF(D34=\"\",\"\",(FREQUENCY(ArraySheet!$B$2:'ArraySheet'!$B$501,D34)))","v":12},0.08,null,105.5]
[null,"Mean * 2",{"$f":"IF(OR(C37=0,C12=0),\"\",C34*2)","v":208},104.87777777777775,1,{"$f":"IF(D35=\"\",\"\",(FREQUENCY(ArraySheet!$B$2:'ArraySheet'!$B$501,D35)))","v":13},0.04,null,108]
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[null,null,null,null,null,null,{"$f":"IF($C$37=0,\"\",IF(E38=\"\",\"\",E38/$C$37))","v":""}]
[null,"TwoHistNormMin",{"$f":"MIN(D27,D2)","v":88.5}]
[null,"TwoHistNormMax",{"$f":"MAX(D37,D12)","v":112.49999999999997}]
[]
[null,"mean","mean frequency"]
[null,{"$f":"C34","v":104},{"$f":"IF(G27=\"\",\"\",(MAX($G$27:$G$37)+1))","v":1.1599999999999999}]
[null,"row count",{"$f":"COUNT(ArraySheet!$B$2:'ArraySheet'!$B$501)","v":25}]
[null,"Count columns"]
[null,"mean dot y value",0]
[null,"se mean",{"$f":"IF(C37=0,\"\",C33/SQRT(C37))","v":1.5470401847829724}]
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[null,"95 factor",{"$f":"IF(OR(C37=0,C12=0),\"\",TINV((1-0.95),D56))","v":2.0301079282503438}]
[null,"mean diff est.",{"$f":"IF(OR(C34=\"\",C9=\"\"),\"\",C9-C34)","v":-1.5999999999999943}]
[]
[]
[null," (std A)^2/N1",{"$f":"IF(C12=0,\"\",C8^2/C12)","v":0.62666666666666648}]
[null,"(std B)^2/N2",{"$f":"IF(C33=\"\",\"\",C33^2/C37)","v":2.3933333333333331}]
[null,"c",{"$f":"IF(OR(C37=0,C12=0),\"\",C53/(C53+C54))","v":0.20750551876379689}]
[null,"Deg of freedom",{"$f":"IF(OR(C37=0,C12=0),\"\",((C12-1)*(C37-1))/((C12-1)*(C55)^2+(1-C55)^2*(C12-1)))","v":35.761859465425466},{"$f":"IF(OR(C37=0,C12=0),\"\",INT(C56))","v":35}]
[null,"se mean B^2",{"$f":"IF(C47=\"\",\"\",C47^2)","v":2.3933333333333335}]
[null,"se mean A^2",{"$f":"IF(C22=\"\",\"\",C22^2)","v":0.62666666666666659}]
[null,"sqrt se meanA+se mean B",{"$f":"IF(C57=\"\",\"\",SQRT(C57+C58))","v":1.7378147196982767}]
[null,"T value",{"$f":"IF(C50=\"\",\"\",C50/C59)","v":-0.92069654023749425}]
[null,"P value",{"$f":"IF(OR(C37=0,C12=0),\"\",TDIST(ABS(C60),D56,2))","v":0.36351167088750447}]
[]
["data b","conf interval",{"$f":"IF(C34=\"\",\"\",C34-(TINV(0.05,C37-1)*C47))","v":100.80706598784568},{"$f":"IF(C34=\"\",\"\",C34+(TINV(0.05,C37-1)*C47))","v":107.19293401215432},{"$f":"IF(C63=\"\",\"\",ROUND(C63,3)&\", \"&ROUND(D63,3))","v":"100,807, 107,193"}]
[]
[]
[null,"Hist2AxisMin",{"$f":"ROUNDDOWN(MIN(TwoHistNormMin,C3),0)","v":88}]
[null,"Hist2AxisMax",{"$f":"L79+D72","v":90}]
[]
[]
["two hist bin min",{"$f":"ROUNDDOWN(MIN(C3,C28),1)","v":89}]
["two hist bin max",{"$f":"ROUNDUP(MAX(C27,C2),0.5)","v":123},null,"BINS","SAMPLE 1 FREQUENCY","SAMPLE 1 CUM FREQUENCY","SAMPLE 1 FREQUENCY %","SAMPLE 2 FREQUENCY","SAMPLE 2 CUM FREQUENCY","SAMPLE 2 FREQUENCY %"]
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[]
[]
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[null,null,null,null,null,{"$f":"IF(D85=\"\",\"\",(FREQUENCY(ArraySheet!$A$2:'ArraySheet'!$A$501,D85)))","v":""}]
[null,null,null,null,null,{"$f":"IF(D86=\"\",\"\",(FREQUENCY(ArraySheet!$A$2:'ArraySheet'!$A$501,D86)))","v":""}]
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{"sheet":"G-Histogram","index":27,"n_rows":0,"n_cols":0,"rows_kept":0,"cut":false,"hidden":true,"merged":[]}

{"sheet":"C-Scatter","index":28,"n_rows":5,"n_cols":9,"rows_kept":5,"cut":false,"hidden":true,"merged":[]}
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[null,null,null,null,"min AU",{"$f":"MIN(ArraySheet!$A$2:$A$501)","v":95},null,"correl min",-1]
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[null,null,null,null,"x axis min",{"$f":"IF(F4<0.1,ROUND(F4,1),ROUND(F4,0))","v":89}]

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{"sheet":"C-Time","index":30,"n_rows":501,"n_cols":5,"rows_kept":501,"cut":false,"hidden":true,"merged":[]}
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[null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"Test 1 r",{"$f":"'C-IMR'!CO2","v":"Show"}]
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[null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"Test 2r",{"$f":"'C-IMR'!CO4","v":"Hide"}]
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[null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"Test 3r",{"$f":"'C-IMR'!CO6","v":"Hide"}]
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[null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"Test 4r",{"$f":"'C-IMR'!CO8","v":"Hide"}]
[null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"Test 5",{"$f":"'C-IMR'!CO9","v":"Show"},{"$f":"AR12","v":"Show"}]
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["x"]

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["Box Plots"]
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[null,"Median",{"$f":"IF(ISBLANK(ArraySheet!A1),\"\",MEDIAN(ArraySheet!A:A))","v":102},{"$f":"IF(ISBLANK(ArraySheet!B2),\"\",MEDIAN(ArraySheet!B:B))","v":104},{"$f":"IF(ISBLANK(ArraySheet!C1),\"\",MEDIAN(ArraySheet!C:C))","v":""},{"$f":"IF(ISBLANK(ArraySheet!D1),\"\",MEDIAN(ArraySheet!D:D))","v":""},{"$f":"IF(ISBLANK(ArraySheet!E1),\"\",MEDIAN(ArraySheet!E:E))","v":""},{"$f":"IF(ISBLANK(ArraySheet!F1),\"\",MEDIAN(ArraySheet!F:F))","v":""},{"$f":"IF(ISBLANK(ArraySheet!G1),\"\",MEDIAN(ArraySheet!G:G))","v":""},{"$f":"IF(ISBLANK(ArraySheet!H1),\"\",MEDIAN(ArraySheet!H:H))","v":""},{"$f":"IF(ISBLANK(ArraySheet!I1),\"\",MEDIAN(ArraySheet!I:I))","v":""},{"$f":"IF(ISBLANK(ArraySheet!K1),\"\",MEDIAN(ArraySheet!K:K))","v":""}]
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[null,"Max",{"$f":"IF(ISBLANK(ArraySheet!A1),\"\",MAX(ArraySheet!A:A))","v":111},{"$f":"IF(ISBLANK(ArraySheet!B2),\"\",MAX(ArraySheet!B:B))","v":123},{"$f":"IF(ISBLANK(ArraySheet!C1),\"\",MAX(ArraySheet!C:C))","v":""},{"$f":"IF(ISBLANK(ArraySheet!D1),\"\",MAX(ArraySheet!D:D))","v":""},{"$f":"IF(ISBLANK(ArraySheet!E1),\"\",MAX(ArraySheet!E:E))","v":""},{"$f":"IF(ISBLANK(ArraySheet!F1),\"\",MAX(ArraySheet!F:F))","v":""},{"$f":"IF(ISBLANK(ArraySheet!G1),\"\",MAX(ArraySheet!G:G))","v":""},{"$f":"IF(ISBLANK(ArraySheet!H1),\"\",MAX(ArraySheet!H:H))","v":""},{"$f":"IF(ISBLANK(ArraySheet!I1),\"\",MAX(ArraySheet!I:I))","v":""},{"$f":"IF(ISBLANK(ArraySheet!K1),\"\",MAX(ArraySheet!K:K))","v":""}]
[null,"IQR",{"$f":"IF(ISBLANK(ArraySheet!A1),\"\",C5-C3)","v":5},{"$f":"IF(ISBLANK(ArraySheet!B2),\"\",D5-D3)","v":8},{"$f":"IF(ISBLANK(ArraySheet!C1),\"\",E5-E3)","v":""},{"$f":"IF(ISBLANK(ArraySheet!D1),\"\",F5-F3)","v":""},{"$f":"IF(ISBLANK(ArraySheet!E1),\"\",G5-G3)","v":""},{"$f":"IF(ISBLANK(ArraySheet!F1),\"\",H5-H3)","v":""},{"$f":"IF(ISBLANK(ArraySheet!G1),\"\",I5-I3)","v":""},{"$f":"IF(ISBLANK(ArraySheet!H1),\"\",J5-J3)","v":""},{"$f":"IF(ISBLANK(ArraySheet!I1),\"\",K5-K3)","v":""},{"$f":"IF(ISBLANK(ArraySheet!K1),\"\",L5-L3)","v":""}]
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[null,"Lower Outliers",{"$f":"IF(ISBLANK(ArraySheet!A1),\"\",IF(COUNTIF(ArraySheet!A:A,\"<\"&C15)=0,0,COUNTIF(ArraySheet!A:A,\"<\"&C15)))","v":0},{"$f":"IF(ISBLANK(ArraySheet!B1),\"\",IF(COUNTIF(ArraySheet!B:B,\"<\"&D15)=0,0,COUNTIF(ArraySheet!B:B,\"<\"&D15)))","v":0},{"$f":"IF(ISBLANK(ArraySheet!C1),\"\",IF(COUNTIF(ArraySheet!C:C,\"<\"&E15)=0,0,COUNTIF(ArraySheet!C:C,\"<\"&E15)))","v":""},{"$f":"IF(ISBLANK(ArraySheet!D1),\"\",IF(COUNTIF(ArraySheet!D:D,\"<\"&F15)=0,0,COUNTIF(ArraySheet!D:D,\"<\"&F15)))","v":""},{"$f":"IF(ISBLANK(ArraySheet!E1),\"\",IF(COUNTIF(ArraySheet!E:E,\"<\"&G15)=0,0,COUNTIF(ArraySheet!E:E,\"<\"&G15)))","v":""},{"$f":"IF(ISBLANK(ArraySheet!F1),\"\",IF(COUNTIF(ArraySheet!F:F,\"<\"&H15)=0,0,COUNTIF(ArraySheet!F:F,\"<\"&H15)))","v":""},{"$f":"IF(ISBLANK(ArraySheet!G1),\"\",IF(COUNTIF(ArraySheet!G:G,\"<\"&I15)=0,0,COUNTIF(ArraySheet!G:G,\"<\"&I15)))","v":""},{"$f":"IF(ISBLANK(ArraySheet!H1),\"\",IF(COUNTIF(ArraySheet!H:H,\"<\"&J15)=0,0,COUNTIF(ArraySheet!H:H,\"<\"&J15)))","v":""},{"$f":"IF(ISBLANK(ArraySheet!I1),\"\",IF(COUNTIF(ArraySheet!I:I,\"<\"&K15)=0,0,COUNTIF(ArraySheet!I:I,\"<\"&K15)))","v":""},{"$f":"IF(ISBLANK(ArraySheet!J1),\"\",IF(COUNTIF(ArraySheet!J:J,\"<\"&L15)=0,0,COUNTIF(ArraySheet!J:J,\"<\"&L15)))","v":""},{"$f":"IF(ISBLANK(ArraySheet!K1),\"\",IF(COUNTIF(ArraySheet!K:K,\"<\"&M15)=0,0,COUNTIF(ArraySheet!K:K,\"<\"&M15)))","v":""}]
[]
[null,"Q2-Q1",{"$f":"IF(C4=\"\",\"\",C4-C3)","v":2},5,"","","","","","","",""]
[null,"Q3-Q2",{"$f":"IF(C5=\"\",\"\",C5-C4)","v":3},3,"","","","","","","",""]
[]
[null,"Q3+1.5*IQR",{"$f":"IF(C5=\"\",\"\",C5+1.5*C7)","v":112.5},119,"","","","","","","",""]
[null,"Q1-1.5*IQR",{"$f":"IF(C3=\"\",\"\",C3-1.5*C7)","v":92.5},87,"","","","","","","",""]
[null,"Upper Whisker",{"$f":"IF(C6=\"\",\"\",MIN(C6,C14))","v":111},119,"","","","","","","",""]
[null,"Lower Whisker",{"$f":"IF(C2=\"\",\"\",MAX(C2,C15))","v":95},"","","","","","","","",""]
[null,"Upper Whisker - Q3",{"$f":"IF(C5=\"\",\"\",C16-C5)","v":6},12,"","","","","","","",""]
[null,"Lower Whisker - Q1",{"$f":"IF(C3=\"\",\"\",(C3-C17))","v":5},{"$error":"#VALUE!"},"","","","","","","",""]
[]
[null,"Outliers Max",{"$f":"IF(C6=\"\",\"\",IF(C8>0,C6,NA()))","v":{"$error":"#N/A"}},123,"","","","","","","","",""]
[null,"Outliers Min",{"$f":"IF(C2=\"\",\"\",IF(C9>0,C2,NA()))","v":{"$error":"#N/A"}},"","","","","","","","","",""]
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[null,"LO"]
["HistBox"]
[null,"min",{"$f":"IF(ISBLANK(ArraySheet!W2),\"\",MIN(ArraySheet!W:W))","v":""}]
[null,"Quartile 1",{"$f":"IF(ISBLANK(ArraySheet!W2),\"\",PERCENTILE(ArraySheet!W:W,0.25))","v":""},"series c"]
[null,"Median",{"$f":"IF(ISBLANK(ArraySheet!W2),\"\",MEDIAN(ArraySheet!W:W))","v":""}]
[null,"Quartile 3",{"$f":"IF(ISBLANK(ArraySheet!W2),\"\",PERCENTILE(ArraySheet!W:W,0.75))","v":""}]
[null,"Max",{"$f":"IF(ISBLANK(ArraySheet!W2),\"\",MAX(ArraySheet!W:W))","v":""}]
[null,"IQR",{"$f":"IF(ISBLANK(ArraySheet!W2),\"\",C29-C27)","v":""}]
[null,"Upper Outliers",{"$f":"IF(ISBLANK(ArraySheet!W2),\"\",COUNTIF(ArraySheet!W2:W501,\">\"&C38))","v":""}]
[null,"Lower Outliers",{"$f":"IF(ISBLANK(ArraySheet!W2),\"\",COUNTIF(ArraySheet!2:2,\"<\"&C39))","v":""}]
[]
[null,"Q2-Q1",{"$f":"IF(C28=\"\",\"\",C28-C27)","v":""},"series b"]
[null,"Q3-Q2",{"$f":"IF(C29=\"\",\"\",C29-C28)","v":""},"series a"]
[]
[null,"Q3+1.5*IQR",{"$f":"IF(C29=\"\",\"\",C29+1.5*C31)","v":""}]
[null,"Q1-1.5*IQR",{"$f":"IF(C27=\"\",\"\",C27-1.5*C31)","v":""}]
[null,"Upper Whisker",{"$f":"IF(C30=\"\",\"\",MIN(C30,C38))","v":""},"upperA"]
[null,"Lower Whisker",{"$f":"IF(C26=\"\",\"\",MAX(C26,C39))","v":""},"lowerA"]
[null,"Upper Whisker - Q3",{"$f":"IF(C29=\"\",\"\",C40-C29)","v":""},"upperB"]
[null,"Lower Whisker - Q1",{"$f":"IF(C27=\"\",\"\",(C27-C41))","v":""},"lowerB"]
[]
[null,"Outliers Max",{"$f":"IF(C30=\"\",\"\",IF(C32>0,C30,\"\"))","v":""}]
[null,"Outliers Min",{"$f":"IF(C26=\"\",\"\",IF(C33>0,C26,\"\"))","v":""}]

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["box"]

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{"sheet":"G-Normality","index":47,"n_rows":0,"n_cols":0,"rows_kept":0,"cut":false,"hidden":true,"merged":[]}

{"sheet":"Examples","index":48,"n_rows":17,"n_cols":13,"rows_kept":17,"cut":false,"hidden":true,"merged":[]}
["Examples",null,null,null,null,null,null,null,null,null,null,"Causes","# Defects"]
[null,null,null,null,null,null,null,null,null,null,null,"Cause # 1",8]
[null,null,null,null,null,null,null,null,null,null,null,"Cause # 2",9]
[null,null,null,null,null,null,null,null,null,null,null,"Cause # 3",30]
[null,null,null,null,null,null,null,null,null,null,null,"Cause # 4",6]
[null,null,null,null,null,null,null,null,null,null,null,"Cause # 5",40]
[null,null,null,null,null,null,null,null,null,null,null,"Cause # 6",7]
[null,null,null,null,null,null,null,null,null,null,null,"Cause # 7",5]
[null,null,null,null,null,null,null,null,null,null,null,"Cause # 8",89]
[null,null,null,null,null,null,null,null,null,null,null,"Cause # 9",56]
[null,null,null,null,null,null,null,null,null,null,null,"Cause # 10",2]
[null,null,null,null,null,null,null,null,null,null,null,"Cause # 11",1]
[null,null,null,null,null,null,null,null,null,null,null,"Cause # 12",6]
[null,null,null,null,null,null,null,null,null,null,null,"Cause # 13",4]
[null,null,null,null,null,null,null,null,null,null,null,"Cause # 14",3]
[null,null,null,null,null,null,null,null,null,null,null,"Cause # 15",5]
[null,null,null,null,null,null,null,null,null,null,null,"Cause # 16",4]

{"sheet":"BensMatrix","index":49,"n_rows":31,"n_cols":10,"rows_kept":31,"cut":false,"hidden":true,"merged":[[37,3,37,9],[30,3,30,9],[32,3,32,9],[33,3,33,9],[34,3,34,9],[35,3,35,9],[36,3,36,9],[0,1,2,10],[0,0,3,0],[27,6,28,7],[27,4,28,4],[27,9,28,9],[11,3,11,6],[12,3,12,6],[8,3,8,6],[9,3,9,6],[10,3,10,6],[6,3,6,8],[4,3,4,4],[19,2,22,3]]}
["Effort &\nBenefits Matrix","Effort & Benefits Matrix"]
[]
[]
[]
[null,null,"Date"]
[]
[null,null,"Team"]
[]
[null,null,"Names"]
[]
[]
[]
[]
[]
[]
[null,null,null,"High"]
[]
[]
[]
[null,null,"Benefits"]
[]
[]
[]
[]
[]
[]
[null,null,null,"Low"]
[null,null,null,null,"Low",null,"Effort",null,null,"High"]
[]
[]
[null,null,"Notes"]

{"sheet":"CriteriaMatrix","index":50,"n_rows":33,"n_cols":8,"rows_kept":33,"cut":false,"hidden":true,"merged":[[177,2,177,4],[37,2,37,7],[38,2,38,7],[39,2,39,7],[40,2,40,7],[41,2,41,7],[159,4,159,5],[159,6,159,7],[36,2,36,7],[0,0,3,0],[11,4,11,5],[11,6,11,7],[4,3,4,4],[5,3,5,7],[6,3,6,7],[7,3,7,7],[8,3,8,7],[29,2,29,4],[0,1,2,7],[33,2,33,7],[34,2,34,7],[35,2,35,7]]}
["Criteria \nMatrix","Criteria Matrix"]
[]
[]
[]
[null,null,"Date"]
[null,null,"Team"]
[null,null,"Description"]
[]
[]
[]
[]
[null,null,null,null,"A",null,"B"]
[null,null,null,null,"Alternative A =",null,"Alternative B = "]
[null,null,"Desirable Criteria","Weight","Score","Wtd Score","Score","Wtd Score"]
[null,null,null,null,null,{"$f":"E15*D15","v":0},null,{"$f":"G15*D15","v":0}]
[null,null,null,null,null,{"$f":"E16*D16","v":0},null,{"$f":"G16*D16","v":0}]
[null,null,null,null,null,0,null,0]
[null,null,null,null,null,0,null,0]
[null,null,null,null,null,0,null,0]
[null,null,null,null,null,0,null,0]
[null,null,null,null,null,0,null,0]
[null,null,null,null,null,0,null,0]
[null,null,null,null,null,0,null,0]
[null,null,null,null,null,0,null,0]
[null,null,null,null,null,0,null,0]
[null,null,null,null,null,0,null,0]
[null,null,null,null,null,0,null,0]
[null,null,null,null,null,0,null,0]
[null,null,null,null,null,0,null,0]
[null,null,"Total Score",null,null,{"$f":"SUM(F15:F29)","v":0},null,{"$f":"SUM(H15:H29)","v":0}]
[]
[]
[null,null,"Notes"]

{"sheet":"Driver1","index":51,"n_rows":1,"n_cols":2,"rows_kept":1,"cut":false,"hidden":true,"merged":[[0,1,0,10]]}
["DRIVER","DRIVER"]

{"sheet":"expics","index":52,"n_rows":501,"n_cols":101,"rows_kept":501,"cut":false,"hidden":true,"merged":[]}
[null,null,null,null,null,null,null,null,"Causes","# Defects",null,null,null,null,null,null,null,null,null,null,null,"Stage 1 Delivery Time",null,null,null,null,null,null,null,null,null,"Stage 5 Delivery Time","Total Delivery Time",null,null,null,null,null,null,null,null,null,null,"Stage 4 Delivery Time",null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"Stage 1 Delivery Time","Stage 2 Delivery Time","Stage 3 Delivery Time","Stage 4 Delivery Time","Stage 5 Delivery Time",null,null,null,null,null,null,null,null,null,null,"Motors Ordered","Motors Delivered Late (> 300 days)",null,null,null,null,null,null,null,null,"Pos Skew",null,null,null,null,null,null,null,null,null,null,"Stage 4 Delivery Time","Stage 5 Delivery Time"]
[null,null,null,null,null,null,null,null,"Cause # 1",8,null,null,null,null,null,null,null,null,null,null,null,15.1319,null,null,null,null,null,null,null,null,null,3.1770800000000001,38.769399999999997,null,null,null,null,null,null,null,null,null,null,3.7736100000000001,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,15.1319,8.0868000000000002,8.6,3.7736100000000001,3.1770800000000001,null,null,null,null,null,null,null,null,null,null,41,4,null,null,null,null,null,null,null,null,63.70599365234375,null,null,null,null,null,null,null,null,null,null,3.7736100000000001,3.1770800000000001]
[null,null,null,null,null,null,null,null,"Cause # 2",9,null,null,null,null,null,null,null,null,null,null,null,15.8924,null,null,null,null,null,null,null,null,null,1.86042,41.046500000000002,null,null,null,null,null,null,null,null,null,null,7.6048600000000004,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,15.8924,7.9673999999999996,7.7214999999999998,7.6048600000000004,1.86042,null,null,null,null,null,null,null,null,null,null,39,3,null,null,null,null,null,null,null,null,65.795341491699219,null,null,null,null,null,null,null,null,null,null,7.6048600000000004,1.86042]
[null,null,null,null,null,null,null,null,"Cause # 3",30,null,null,null,null,null,null,null,null,null,null,null,15.338900000000001,null,null,null,null,null,null,null,null,null,0.41249999999999998,41.557600000000001,null,null,null,null,null,null,null,null,null,null,7.5159700000000003,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,15.338900000000001,12.050700000000001,6.2396000000000003,7.5159700000000003,0.41249999999999998,null,null,null,null,null,null,null,null,null,null,40,2,null,null,null,null,null,null,null,null,66.51202392578125,null,null,null,null,null,null,null,null,null,null,7.5159700000000003,0.41249999999999998]
[null,null,null,null,null,null,null,null,"Cause # 4",6,null,null,null,null,null,null,null,null,null,null,null,17.2681,null,null,null,null,null,null,null,null,null,1.31667,38.265999999999998,null,null,null,null,null,null,null,null,null,null,7.8006900000000003,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,17.2681,5.9451000000000001,5.9353999999999996,7.8006900000000003,1.31667,null,null,null,null,null,null,null,null,null,null,41,1,null,null,null,null,null,null,null,null,64.910659790039063,null,null,null,null,null,null,null,null,null,null,7.8006900000000003,1.31667]
[null,null,null,null,null,null,null,null,"Cause # 5",40,null,null,null,null,null,null,null,null,null,null,null,16.268699999999999,null,null,null,null,null,null,null,null,null,2.40347,36.6785,null,null,null,null,null,null,null,null,null,null,5.1798599999999997,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,16.268699999999999,4.2714999999999996,8.5548999999999999,5.1798599999999997,2.40347,null,null,null,null,null,null,null,null,null,null,43,2,null,null,null,null,null,null,null,null,67.237747192382813,null,null,null,null,null,null,null,null,null,null,5.1798599999999997,2.40347]
[null,null,null,null,null,null,null,null,"Cause # 6",7,null,null,null,null,null,null,null,null,null,null,null,16.622199999999999,null,null,null,null,null,null,null,null,null,1.8631899999999999,45.293799999999997,null,null,null,null,null,null,null,null,null,null,6.4312500000000004,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,16.622199999999999,12.6313,7.7458,6.4312500000000004,1.8631899999999999,null,null,null,null,null,null,null,null,null,null,36,1,null,null,null,null,null,null,null,null,62.929141998291016,null,null,null,null,null,null,null,null,null,null,6.4312500000000004,1.8631899999999999]
[null,null,null,null,null,null,null,null,"Cause # 7",5,null,null,null,null,null,null,null,null,null,null,null,15.95,null,null,null,null,null,null,null,null,null,1.6187499999999999,38.208300000000001,null,null,null,null,null,null,null,null,null,null,5.7062499999999998,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,15.95,6.7680999999999996,8.1653000000000002,5.7062499999999998,1.6187499999999999,null,null,null,null,null,null,null,null,null,null,40,2,null,null,null,null,null,null,null,null,63.645626068115234,null,null,null,null,null,null,null,null,null,null,5.7062499999999998,1.6187499999999999]
[null,null,null,null,null,null,null,null,"Cause # 8",89,null,null,null,null,null,null,null,null,null,null,null,17.359000000000002,null,null,null,null,null,null,null,null,null,1.5972200000000001,39.676400000000001,null,null,null,null,null,null,null,null,null,null,6.7743099999999998,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,17.359000000000002,5.8186999999999998,8.1271000000000004,6.7743099999999998,1.5972200000000001,null,null,null,null,null,null,null,null,null,null,45,1,null,null,null,null,null,null,null,null,63.990306854248047,null,null,null,null,null,null,null,null,null,null,6.7743099999999998,1.5972200000000001]
[null,null,null,null,null,null,null,null,"Cause # 9",56,null,null,null,null,null,null,null,null,null,null,null,18.5382,null,null,null,null,null,null,null,null,null,1.4145799999999999,39.9771,null,null,null,null,null,null,null,null,null,null,5.5180600000000002,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,18.5382,6.8410000000000002,7.6653000000000002,5.5180600000000002,1.4145799999999999,null,null,null,null,null,null,null,null,null,null,39,2,null,null,null,null,null,null,null,null,116.00341033935547,null,null,null,null,null,null,null,null,null,null,5.5180600000000002,1.4145799999999999]
[null,null,null,null,null,null,null,null,"Cause # 10",2,null,null,null,null,null,null,null,null,null,null,null,19.25,null,null,null,null,null,null,null,null,null,1.79375,45.261099999999999,null,null,null,null,null,null,null,null,null,null,4.6923599999999999,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,19.25,14.3132,5.2118000000000002,4.6923599999999999,1.79375,null,null,null,null,null,null,null,null,null,null,41,1,null,null,null,null,null,null,null,null,79.095306396484375,null,null,null,null,null,null,null,null,null,null,4.6923599999999999,1.79375]
[null,null,null,null,null,null,null,null,"Cause # 11",1,null,null,null,null,null,null,null,null,null,null,null,16.213899999999999,null,null,null,null,null,null,null,null,null,3.0006900000000001,46.593800000000002,null,null,null,null,null,null,null,null,null,null,5.40069,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,16.213899999999999,17.802099999999999,4.1764000000000001,5.40069,3.0006900000000001,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,71.894416809082031,null,null,null,null,null,null,null,null,null,null,5.40069,3.0006900000000001]
[null,null,null,null,null,null,null,null,"Cause # 12",6,null,null,null,null,null,null,null,null,null,null,null,16.785399999999999,null,null,null,null,null,null,null,null,null,2.14236,47.161799999999999,null,null,null,null,null,null,null,null,null,null,5.6694399999999998,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,16.785399999999999,16.398599999999998,6.1660000000000004,5.6694399999999998,2.14236,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,62.927444458007812,null,null,null,null,null,null,null,null,null,null,5.6694399999999998,2.14236]
[null,null,null,null,null,null,null,null,"Cause # 13",4,null,null,null,null,null,null,null,null,null,null,null,16.136099999999999,null,null,null,null,null,null,null,null,null,3.39167,40.325000000000003,null,null,null,null,null,null,null,null,null,null,4.4923599999999997,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,16.136099999999999,8.3556000000000008,7.9493,4.4923599999999997,3.39167,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,68.355422973632812,null,null,null,null,null,null,null,null,null,null,4.4923599999999997,3.39167]
[null,null,null,null,null,null,null,null,"Cause # 14",3,null,null,null,null,null,null,null,null,null,null,null,15.527100000000001,null,null,null,null,null,null,null,null,null,2.04236,44.652799999999999,null,null,null,null,null,null,null,null,null,null,5.4368100000000004,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,15.527100000000001,15.9917,5.6548999999999996,5.4368100000000004,2.04236,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,65.962547302246094,null,null,null,null,null,null,null,null,null,null,5.4368100000000004,2.04236]
[null,null,null,null,null,null,null,null,"Cause # 15",5,null,null,null,null,null,null,null,null,null,null,null,15.2722,null,null,null,null,null,null,null,null,null,0.625,39.915999999999997,null,null,null,null,null,null,null,null,null,null,4.7659700000000003,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,15.2722,13.5458,5.7069000000000001,4.7659700000000003,0.625,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,63.058265686035156,null,null,null,null,null,null,null,null,null,null,4.7659700000000003,0.625]
[null,null,null,null,null,null,null,null,"Cause # 16",4,null,null,null,null,null,null,null,null,null,null,null,17.752099999999999,null,null,null,null,null,null,null,null,null,3.5034700000000001,39.220799999999997,null,null,null,null,null,null,null,null,null,null,4.9194399999999998,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,17.752099999999999,5.3624999999999998,7.6833,4.9194399999999998,3.5034700000000001,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,63.279747009277344,null,null,null,null,null,null,null,null,null,null,4.9194399999999998,3.5034700000000001]
[null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,19.5181,null,null,null,null,null,null,null,null,null,2.7173600000000002,39.540999999999997,null,null,null,null,null,null,null,null,null,null,4.6965300000000001,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,19.5181,5.5388999999999999,7.0701000000000001,4.6965300000000001,2.7173600000000002,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,62.957565307617188,null,null,null,null,null,null,null,null,null,null,4.6965300000000001,2.7173600000000002]
[null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,17.5396,null,null,null,null,null,null,null,null,null,4.0409699999999997,40.3812,null,null,null,null,null,null,null,null,null,null,3.17639,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,17.5396,10.2569,5.3673999999999999,3.17639,4.0409699999999997,null,null,null,null,null,null,null,null,null,null,"Motors Delivered Late (> 300 days)","Days Late Total",null,null,null,null,null,null,null,null,64.240631103515625,null,null,null,null,null,null,null,null,null,null,3.17639,4.0409699999999997]
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