PDSS Critical Parameter Development & Mgt. Process Quick Guide

PDSS Critical Parameter Development & Mgt. Process Quick Guide 12 Steps for a Critical Parameter Development Project (incl. 6 Check Points) Step 1: Create a CP Project Charter - establish goal, objectives, team members, roles & responsibilities, time line & scope Scope of CP define clear, specific & measurable CP project results Activity Step 2: Create a cross-functional team of experts to help ID a thorough set of CPs - make sure they are well balanced, right mix of people (experience & judgment) good at mistake-proofing the list of parameters (methods for mistake-proofing) Step 3: Generate / Assess Requirement Clarity, Classification & Flow-down - define Critical System level functional Reqts & their tolerance limits (USL & LSL) define NUD (Critical) & ECO (non-Critical) requirements as they flow down to subsystems, subassemblies, parts, materials and mfg. /assy./ packaging processes Step 4: Generate I-O-C Diagrams, P – Diagram, Noise Diagrams & the Boundary Diagram - identify high level mass, energy & information flows into and out of the system, subsystems & subassemblies - identify candidate Critical functions, inputs, outputs, controllable parameters & noises - Define leading and lagging indicators & their units of measure - identify unit-to-unit, external / environmental & deteriorative noise parameters - preliminary documentation of required measurement systems Step 5: Structure a Critical Parameter Flow-down Tree - CP candidate structure & Define the relationships between Y, ys & their controlling xs prioritize for focus areas o Function Trees & Functional Flow Diagrams o Math Models Define macro-relationships aligned with Critical noise parameters; which are NUD? Plan to separate which xs dominate & control the mean & which control σ for each Y & sub-y Conduct Potential Problem Prevention & Impact Mitigation Analysis (P3IMA Table aka FMEA) o Laws of Unintended Consequences (unwanted functions) Step 6: Identify unique sub-areas of focus; lean out, rank & prioritize the areas to work on - Group prioritized CP flows with the biggest impact on the reqts; apply 6 Step Prevention Process Select the appropriate groups of flows that matter the most; again – which are NUD? Align critical noise parameters with the appropriate sub-groups Step 7: Prove measurement systems are capable - MSA & Gage R&R Studies for Critical Ys, sub-ys & controlling Xs (for both leading & lagging indicators) Step 8: Design & conduct experiments (problem ID & prevention!) - screening experiments (separate signal from random noise) modeling experiments (linear & non-linear effects plus interactivity) noise parameter strength experiments (what shifts the mean or spreads the variance?) robustness experiments tolerance sensitivity experiments Step 9: Analyze data using ANOVA & other statistical methods that identify sensitivities & level of capability define statistical significance (p values) Facts database of CPs & MSparameter / MStotal their relationships - Cp & Cpk values Capability Growth Indices (CGI maturation by development process phase) Step 10: Establish & Verify tolerance ranges & % contribution to variation of critical Ys & sub-ys - USL & LSL for both nominal conditions & stressful conditions (robust tolerances) establish variance role-up model (s2 total = s21 + s22 + …. + s2n) verify & validate final design & processing set points Documented CP set points Step 11: Mfg. & Production Implementation Plan for Critical Parameters - Establish production & assembly data requirements & data utilization plan o Agreement on what constitutes a production or assembly CP � In-process CPs on the process itself � Within-process or post-process CPs (on parts, sub-assy, sub-system or system during mfg., assembly, packaging or upon receipt) o Requirements/Specifications to measure production & assembly CPs against o SPC & Cp/Cpk Study requirements & procedures � Frequency of measurements & action based upon data � Critical Cpk>>>Cp Adjustment parameters (mean shifters) o Measurement system requirements & acceptable signal/noise resolution o Contingency & Corrective Action plans � Alternative action plan � Process specific LSS-based corrective action process plan • Kaizen event or 6σ Project? Conduct CP summary reviews & make Cpk>>>Cp adjustments as needed during steady state mfg. Step 12: Evaluate Quality and Implement Changes in a Control plan - Develop and submit alternate acceptance plans that maintain or improve functional quality with reduced acceptance costs o Select acceptance plan that meets overall program needs o Verify performance of selected plan o Implement changes as supported by data per the control plan Change Implementation and Document Ongoing Control Plan CP Step Step 1: Create a CP Project Charter Enabling Tools, Methods or Best Practices Project Planning tools (MS Project); Monte Carlo Simulations of Critical the Path of Task Flows (@Risk Software Tool; Palisades), Cost Estimation, SMART Problem & Goal Identification, Intro. to CP Module Step 2: Create Specific Experience, Technical Expertise & Judgment, Prefer a crossDFLSS trained individuals on the IPT; can be trained and functional team mentored JIT as required of experts to help ID a thorough set of CPs Step 3: Customer/Stakeholder ID, Interviewing Methods, KJ Method, Generate / NUD vs. ECO Classification, Kano Method, Quality Function Assess Deployment (QFD) & the Houses of Quality, DOORS Reqts. Requirement Software Tool, CP Reqt. Database Documentation, CP Clarity, Reqts. Worksheets Classification & Flow-down Documentation Step 4: I-O-C Diagramming, P-Diagramming, Noise Diagramming, Generate I-O­ System Noise Mapping, Boundary & Interface Diagramming, C Diagrams, P- 1st Principles Modeling & Simulations Diagrams, Noise Diagrams, Boundary Diagrams & Math Models Step 5: Functional Diagramming, FAST Diagramming, Tree Structure a Diagramming, Flow Diagramming, CocCPit CPM Software Critical Tool (Cognition), CP Data Base Construction Module, CP Characteristics Score Card Structuring Module, CP Reqts. & Measured Y Flow-down Worksheets Tree Step 6: Identify NUD vs. ECO Classification, Kano Classification, Pareto unique sub­ Process, QFD Reqts. Ranking & Prioritization, Function areas of focus; Trees, Noise Diagrams, FMEAs lean out, rank & prioritize the areas to work Step 7: Prove Measurement System Analysis, Gage R&R Studies measurement systems are capable Step 8: Design & conduct experiments Step 9: Analyze data using ANOVA & other statistical methods that identify sensitivities & level of capability Step 10: Establish & Verify tolerance ranges & % contribution to variation of critical Ys & sub-ys Steps 11-12: Mfg. & Production Implementation Plan SPC Studies, Capability Studies, Sample-size Determination, Sample Data Parameter & Distribution Characterization Studies, Multi-vari Correlation Studies, t-Tests for 2 Way Comparisons, DOE Methods: Full & Fractional Factorial Designs, Screening Experiments, Modeling Experiments, Optimization Experiments, Mixture Experiments, Robustness Development Experiments, System Integration Sensitivity Experiments, Tolerance Balancing Experiments, ALT, HALT & HAST, Duane Plotting Descriptive, Graphical & Inferential Statistical Data Analysis Methods, Confidence Interval Analysis, ANOVA, Regression, Main Effects & Interaction Plotting, CP Documentation & CP Scorecards Screening DOEs (Plackett-Burman Arrays), ANOVA, Taguchi Loss Function, Additive Variance Modeling, SPC & Capability Studies, CP Documentation & CP Scorecards Control Planning, Quality Planning, SPC Studies, Capability Studies, CP Allocation for Production & Assembly Processes, CP Documentation & CP Scorecards, CP Deployment in Production & Supply Chain Environments Module Recommendation for linking NUD requirements to CPs for Capability tracking Cp = USL − LSL Cp = 6σ What is Required? Customer Level (USL – LSL) System Level (USL – LSL) Subsystem Level (USL – LSL) Subassembly Level (USL – LSL) Component Level (USL – LSL) Mfg. Process Level (USL – LSL) USL − LSL σ What is6Measured? Customer Level (Avg & σ) System Level (Avg & σ) Subsystem Level (Avg & σ) Subassembly Level (Avg & σ) Component Level (Avg & σ) Mfg. Process Level (Avg & σ) From this comparison we can document performance Capability USL − LSL Cp = 6σ  X − LSL USL − X Cpk = Min , 3 σ 3σ  P ro c e ss C a p a b ilit y A n a ly s is f o r C2 P ro c e ss D a ta 1 2 .0 00 0 U SL T a r ge t L SL M e an S am ple N S tD ev ( W ith in ) S tD ev ( O ve ra ll) L SL US L W it hi n * Ov e ral l 8 .0 00 0 9 .9 76 6 10 0 0 .4 4 7 13 4 0 .4 5 8 18 6 P o te nt ial (W ith in) C a pa b ility Cp 1 .4 9 C PU C PL 1 .5 1 1 .4 7 C pk 1 .4 7 C pm * 8 9 O v er a ll C a pa b ility 10 O b s e rv ed P er fo r m a n ce 11 12 E xp . " W ith in " P e r fo rm a nc e E xp . "O v er a ll" P e r fo rm an c e Pp P PU 1 .4 6 1 .4 7 PPM < L SL P P M > US L 0 .0 0 0 .0 0 PP M < L SL P P M > US L 4. 92 3. 02 PPM < L SL P P M > US L P PL P pk 1 .4 4 1 .4 4 P P M T o t al 0 .0 0 P P M T ot al 7. 94 P P M T ot al 8 .0 1 5 .0 3 1 3 .0 4 P ro c e ss C a p a b ilit y A n a ly s is f o r C2    L SL P ro c e ss D a ta U SL T a r ge t L SL M e an US L 1 2 .0 00 0 * W it hi n 8 .0 00 0 9 .9 76 6 S am ple N S tD ev ( W ith in ) 10 0 0 .4 4 7 13 4 S tD ev ( O ve ra ll) 0 .4 5 8 18 6 Ov e ral l P o te nt ial (W ith in) C a pa b ility Cp C PU 1 .4 9 1 .5 1 C PL C pk 1 .4 7 1 .4 7 C pm * O v er a ll C a pa b ility 8 9 10 O b s e rv ed P er fo r m a n ce 11 E xp . " W ith in " Pe r fo rm a nc e 12 E xp . "O v er a ll" Pe r fo rm an c e Pp P PU 1 .4 6 1 .4 7 PPM < L SL PPM > US L 0 .0 0 0 .0 0 PP M < L SL PP M > US L 4. 92 3. 02 PPM < L SL PPM > US L P PL P pk 1 .4 4 1 .4 4 PPM T o t al 0 .0 0 PP M T ot al 7. 94 PPM T ot al 8 .0 1 5 .0 3 1 3 .0 4 Summary of Critical CPM Actions: CPM Actions Reliability Actions Metrics: scalars & vectors; continuous variables Y=f(x) physics –based models Metrics: time-based failures; discrete events Additive / Product Functions; serial / parallel Time-To-Failure models Reliability Block Diagrams FMEA, FMECA & Fault Tree Analysis Duane Reliability Growth Plots Normal Reliability Tests Accelerated Reliability Tests HALT, HASS & HAST evaluations P-Diagrams Noise Diagrams Function Trees Functional Flow Diagrams Form Hypotheses & prioritize evaluations Screening & Modeling DOEs under nominal conditions Robustness experiments under stressful FRACAS conditions Tolerance balancing experiments under nominal & stressful conditions Problem Prevention Steps: The 6 Steps for Problem Prevention: Plan & Rank Tasks Define Potential Problems Evaluate Causes Define Preventive Actions Define Contingent Actions Re-Use Learning • Design a Diagram of detailed functions in their serial & parallel flows • These are value-added functions that possess inherent robustness • Rank & prioritize the value of each function relative to one another • Define potential problems that can occur within & between the functions • A statement of the mistakes or errors that characterize the problem • Identification of Leading Indicators that measure the on-set of a problem • Evaluate & document the root causes of the potential problems • Understand the mechanisms behind all impending problems • Define preventive actions for effectiveness & value • Finalize, measure & track leading indicators – be proactively efficient • Define contingency plans if the problem actually occurs • Finalize, Measure & track leading & lagging indicators – be reactively effective after indicators reach a trigger point. • Re-use the lessons learned for future use & reactivity • Continuously improve the problem prevention process The P3IMA Table to help with the Problem Prevention Steps The Potential Problem Prevention & Impact Mitigation Analysis (P3IMA) (a derivative of FMEA) Critical Function Potential Problem Likely Causes Probability of Occurrence Preventive Action Ability to Detect Onset Severity of Impact Contingency Action What do I keep track of? & how do I do it? 7 Things to prove you are ok! Measure Measurability Stability Tunability Sensitivity & Stat. Significance Interactivity Capability Robustness How? MSA: Gage R&R Study SPC Chart: I & MR DOE, RSM, Regression Y=F(CAPs) DOE, p-Value, ANOVA:DY/DX DOE, ANOVA: Xa * Xb Capability Indices: Cp & Cpk DOE, S/n: COV, std. deviation If these items are problematic and not in control then these are NUD parameters that are very good candidates for Critical Parameter status… until proven under control and able to be re-classified as Easy, Common & Old. Measurability: G age name : D ate of s tudy: R eported by : To lera nc e: Mis c : G ag e R &R (ANO VA) f or Thic kn ess C om ponents of Variation B y Part Percent 1 00 8 .3 %C o nt rib utio n %S tu dy Var %To le ra nce 8 .2 8 .1 50 8 .0 7 .9 0 Ga ge R& R R ep ea t R e p ro d Pa rt P art-t o-P art 1 2 3 R C hart by O perator Fre d Sample Range 0 .2 5 6 7 8 9 10 8 .3 M ary 8 .2 U C L =0 .1 4 5 9 0 .1 8 .1 R = 0 .0 5 66 7 0 .0 8 .0 LCL=0 7 .9 Op e ra tor Fre d 0 Xbar Chart by Operator J oe M ary Operator*Part Interac tion Jo e 8 .1 U C L =8 .1 0 2 M e a n= 8. 0 44 8 .0 O p e ra tor 8 .2 M ary Average Fre d 8 .2 Sample Mean 4 By Operator Jo e L C L = 7. 98 6 7 .9 Fre d J oe M a ry 8 .1 8 .0 7 .9 Pa rt 0 1 2 3 4 5 6 7 8 9 10 Stability: Individual Value I and MR Chart for C2 UCL=5.917 5 0 Mean=-0.03816 -5 LCL=-5.994 Subgroup 0 50 100 Moving Range 10 UCL=7.316 5 R=2.239 0 LCL=0 Tunability: Main Effects Plot - Data Means for Y plus Noise ANOVA: Y plus Noise versus A, B, C Factor A B C Type Levels Values fixed 2 -1 fixed 2 -1 fixed 2 -1 1 1 1 A B Surface Plot of Cool Tim C 30 Analysis of Variance for Y plus N DF 1 1 1 1 1 1 1 8 15 SS 69.82 401.70 1620.35 27.82 110.85 0.00 0.00 2.85 2233.39 MS F 69.82 196.14 401.70 1128.38 1620.35 4551.60 27.82 78.14 110.85 311.38 0.00 0.00 0.00 0.00 0.36 P 0.000 0.000 0.000 0.000 0.000 0.969 0.967 Y plus Noise 25 Source A B C A*B A*C B*C A*B*C Error Total 20 15 10 -1 1 -1 1 1 -1 6 5 4 Interaction Plot (data means) for Y plus Noise -1 1 -1 Cool Time (sec) 1 A 30 1 Effect Sum of Squares epsilon2 %epsilon2 A 69.82 69.82/2233.39=0.031 3.1% B 401.70 401.70/2233.39=0.18 18% -1 C 1620.35 1620.35/2233.39=0.72 72% 30 1 27.82 27.82/2233.39=0.012 1.2% AB 110.85 110.85/2233.39=0.05 5% Total AC 2233.39 -1 Speed 10 C 2 1 0 -2 20 -1 2 0 10 B 3 1 20 Hold values: Vol: 0.0 -1 0 1 -2 2 Temp Interactivity: Main Effects Plot - Data Means for Y plus Noise ANOVA: Y plus Noise versus A, B, C Factor A B C Type Levels Values fixed 2 -1 fixed 2 -1 fixed 2 -1 1 1 1 A B C 30 Analysis of Variance for Y plus N DF 1 1 1 1 1 1 1 8 15 SS 69.82 401.70 1620.35 27.82 110.85 0.00 0.00 2.85 2233.39 MS F 69.82 196.14 401.70 1128.38 1620.35 4551.60 27.82 78.14 110.85 311.38 0.00 0.00 0.00 0.00 0.36 Y plus Noise 25 Source A B C A*B A*C B*C A*B*C Error Total P 0.000 0.000 0.000 0.000 0.000 0.969 0.967 20 15 10 1 -1 1 -1 1 -1 Interaction Plot (data means) for Y plus Noise -1 1 -1 1 A 30 1 20 -1 10 B Sum of Squares epsilon2 %epsilon2 A 69.82 69.82/2233. 39=0.031 3. 1% B 401. 70 401.70/2233.39=0.18 C 1620.35 1620. 35/2233.39=0.72 72% AC 27.82 27.82/2233. 39=0.012 1. 2% AB 110. 85 110.85/2233.39=0.05 5% Total 2233.39 Effect 30 1 20 -1 10 C 18% Sensitivity & Statistical Significance: [36,960/105,227] x [100] = 35% [29,843/105,227] x [100] = 28% [14,945 /105,227] x [100]= 14% [10,764/105,227] x [100]= 10% [6,281/105,227] x [100]= 6% [3,335/105,227] x [100]= 3% [1,580/105,227] x [100]= 1.5% [716/105,227] x [100]= 0.68% 40 25 20 Res A 1 3335 3335 3335 24.88 0.002 10 Res B 1 6281 6281 6281 46.85 0.000 5 1 10764 10764 10764 80.29 0.000 Res D 1 14945 14945 14945 111.48 0.000 Trans F 1 716 716 716 5.34 0.054 Res G 1 36960 36960 36960 275.69 0.000 Res I 1 29843 29843 29843 222.60 0.000 Trans K 1 1580 1580 1580 11.79 0.011 Error 7 938 938 134 Total 15 105361 105227 Total MS Capability: P ro c e s s C a p a b ilit y A n a ly s is fo r C 2 LSL P roc es s D at a U SL T ar get US L 12. 00 00 * LS L 8. 00 00 Me an 9. 97 66 S am ple N S tD ev ( W ith in) 1 00 0 .4 471 34 S tD ev ( O ve rall) 0 .4 581 86 W ith in O v e ra ll Po te nt ial (W it hin) C apa bilit y Cp 1. 49 CP U 1. 51 CP L Cpk 1. 47 1. 47 Cpm * O v era ll C apa bilit y 8 9 10 O bs e rv ed P erf or m a nc e 11 E xp . "W it hin " Pe rf orm an c e 12 Ex p. "O v era ll" Pe rf orm an c e Pp P PU 1. 46 1. 47 PP M < L SL PP M > U S L 0 .0 0 0 .0 0 P P M < L SL P P M > US L 4 .9 2 3 .0 2 PP M < L SL PP M > U S L P PL 1. 44 PP M T ot al 0 .0 0 P P M T ot al 7 .9 4 PP M T ot al P pk 1. 44 8. 01 5. 03 13. 04 Robustness: Yavg A1 Sensitivity to Noise! Critical Functional Robustness Parameter (CFRP) A2 Robustness N1 N2 Compounded Noise Factors 10.26 5.99 3.18 1.5 0.68 0.39 0 Trans. F Cap C 14.24 15 Voltage L P Trans. K F Resistor A Adj MS Cap. C Adj SS Resistor B Seq SS Re sistor I DF 28.44 30 % Contribution Source 35.23 35 Resistor D Resistor G Resistor I Resistor D Capacitor C Resistor B Resistor A Transistor C Transistor F Resistor G • • • • • • • • Example of getting Functions right and driving your CP development methodology: If I have 5 Functions, then I have at least 5 measurable ys. Each y variable is dependent on one or more X variables. First, we define all 5 functions verbally using verb-noun pairs on Post It notes. This results in vertical branches of lists of functions called Function Trees. The top of the tree is a big Y, which is your business or VOC based requirement. This is usually not stated in physics terms but rather quality, financial or some other non-physics form of units of measure. We MUST state our functions in fundamental units of physical measure. You can have multiple branches of functions coming from multiple Voice of Business (VOB) goals or Voice of Customer (VOC) requirements… Y = Business Goal 1 (VOB) or Customer Reqt. 1 (VOC) F1 F2 Function Tree Diagram F3 F4 F5 Next, we rearrange the Post It notes containing the Functions into their horizontal flow relationships over time. This will result in a serial-parallel flow diagram of exactly how the functions occur over time. F2 F1 F3 F4 F5 Functional Flow Diagram time Each function is quantified as a y variable and is stated in its physics-based units of measure (a scalar or vector). We add the y variables and their units of physical measure to the Post It notes. F1…y1 Next we must define each X variable that controls or influences each y… X variables come in 4 varieties: X mean shifter = controllable parameter that has a strong ability to move the mean of y X std. dev. shifter = controllable parameter that has a strong ability to move the value of σ X COV shifter = controllable parameter that has a strong ability to move both the mean of y and the value of σ (a coupled affect on both statistical parameters! Called the Coefficient of Variation: COV = (σ/mean) X noise inducer = an uncontrollable parameter that has a strong ability to either move the mean of y, the value of σ or the COV. They come from either External, Unit-Unit or Deteriorative sources. So for each X we can create a Post It note to align with each y… Xa = … Xb = … Xn = … To define the candidate critical parameter ys & Xs; we lay out the functional relationships F1…y1 Xa = … Xb = … Xn = … For each function you develop a Y = f(X…) model. This is a set of “hypotheses” that must be proven to be complete and true. DOEs will answer the following questions… y1 = f(Xa, Xb, …) y2 = f(Xa, Xb, …) y3 = f(Xa, Xb, …) y4 = f(Xa, Xb, …) y5 = f(Xa, Xb, …) Δy1 = f(Δ ΔXa, ΔXb, …); Δy2 = f(Δ ΔXa, ΔXb, …); Δy3 = f(Δ ΔXa, ΔXb, …); Δy4 = f(Δ ΔXa, ΔXb, …); Δy5 = f(Δ ΔXa, ΔXb, …); σ2 of y1 = f(σ σ2 from ΔXa, σ2 from ΔXb,…) 2 σ of y2 = f(σ σ2 from ΔXa, σ2 from ΔXb,…) σ2 of y3 = f(σ σ2 from ΔXa, σ2 from ΔXb,…) σ2 of y4 = f(σ σ2 from ΔXa, σ2 from ΔXb,…) 2 σ of y5 = f(σ σ2 from ΔXa, σ2 from ΔXb,…) We will know the model is complete by developing both analytical & empirical models. If our correlation coefficient (R2) for the empirical model is high, then we know very little of the data is attributable to missing Xs and in fact the error on the model is due to random effects and not missed X parameters. If random error is small, then our model is good and our data acquisition system is too. If random error is high and our GR&R is over 10-20% we have a measurement system problem to correct! We will know the model is true because the terms, coefficients, linearity or curvature in the analytical and empirical models are in agreement and that the X parameters are all statistically significant – a parsimonius or efficient model! Assumptions will have been proven or corrected. What about Noise and its affect of the Functions (ys) and the relationship of (X * Noise) interactions. The Noise Diagrams: For the Function, what noises cause it to vary? Unit-Unit Noises External Noises Function Δy = f(Xs) Mean of y&σ Deterioration Noises For any X variable that is not a Noise Parameter, what noises cause it to vary? X variations Unit-Unit Noises External Noises ΔXn Mean of y&σ Deterioration Noises We must know what noise parameters really affect our Xs & Ys so we can conduct robust design experiments to assess interactivity between the Xs & the Noises. These noises will also impact k in Cpk assessment because they are able to cause both mean shifts and variance growth.