Prioritizing Process Improvements

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Prioritizing Process Improvements

  1. 1. Prioritizing Process Improvements Govind Ramu JDS Uniphase Corporation ASQ World Conference on Quality and Improvement W07
  2. 2. Group Interaction (3 to 5 minutes) How effective is your engineering meeting? - Are you discussing the appropriate topics? - Do you have the right audience? - Do you have actionable discussions? - Do you leave the meeting knowing exactly what you need to do? - Would you consider your time spent productive and worthwhile?
  3. 3. Meetings - Are they productive? • Overwhelming information. • Tons of presentations. • Information unrelated to agenda. • Attendees not well prepared. • Delegated attendees. • Too many side conversations. • Blackberry/laptop usage during discussions. • Working on next meeting while listening. • Engaging in arguments on “how to.” • No clear conclusions as to “what to do.” • No clear “actionable” discussions.
  4. 4. Productivity Survey • People work an average of 45 hours a week; they consider about 17 of those hours to be unproductive (U.S.: 45 hours a week; 16 hours considered unproductive). • People spend 5.6 hours each week in meetings; 69 percent feel meetings aren't productive (U.S.: 5.5 hours; 71 percent feel meetings aren't productive). • The most common productivity pitfalls are unclear objectives, lack of team communication and ineffective meetings – chosen by 32 percent of respondents overall (U.S.: procrastination, 42 percent; lack of team communication, 39 percent; ineffective meetings, 34 percent). Source: The Microsoft Office Personal Productivity Challenge (PPC) Responses from more than 38,000 people in 200 countries http://www.microsoft.com/presspass/press/2005/mar05/03-15ThreeProductiveDaysPR.mspx
  5. 5. Typical Engineering Meeting Product yield by part # Product volume by year by part # Product volume and yield Product by volume Any actions?
  6. 6. What really matters at the engineering meeting? Engineers want to know: • How the process health is doing. • What the risks are for internal/external customers. • What process parameters to fix. • What actionable intelligence exists.
  7. 7. The eight-step model for process improvement: • Step 1: Identify CTQs and CTPs. • Step 2: Create a CTQ-CTP relationship matrix. • Step 3: Conduct a process FMEA. • Step 4: Develop a control plan. • Step 5: Conduct gage R&R studies. • Step 6: Set up statistical process control. • Step 7: Use process capability & gage R&R to identify improvements. • Step 8: Prioritize improvement efforts.
  8. 8. Step 1: Identify CTQs and CTPs Need Drivers CTQs CTPs Customer With right participants, Needs engineering meetings to focus heavily here for Customer improved effectiveness Drivers I want… VOC Product CTQs CTP CTQ CTP need Business Drivers CTQ CTP CTQ Business Needs Currently CTP engineering meetings may be CTP Primary Needs spending more time at this level CTQ Secondary Needs CTQ Tertiary Needs General Specific Hard to measure Easy to measure
  9. 9. Some Definitions • Customer drivers – quality, cost, delivery, response. • Business drivers – first-pass/rolled throughput yields, work in progress material cost, inventory cost, cycle time, etc. • Primary, secondary and tertiary needs – customer needs from abstract to tactical. • CTQ – critical to quality characteristics of products that customer expects from the product or service. • CTP – critical process parameters that have cause and effect relationship to one more CTQ.
  10. 10. Step 1: Identify CTQs and CTPs Need Drivers CTQs/ CTCs CTPs Oven process Control I want… tempr. X1 deg +/- 5 deg F VOC Taste Raw material aging (days) Product Vegetable aging (days) quality Oven process control time. X2 min +/- 2 min need Average Delivery Order processing time order-delivery Order handling time I want tasty pizza time I want hot pizza Cost (By type & volume) Order delivery time Material cost I want my pizza to be crispy I want my pizza to have Processing cost fresh toppings Selling price Service Yield % I want my pizza to be quicker quality Margin % Every time I get either the Quantity & wrong pizza or wrong right product Order check toppings! Customer Not so expensive Complaint handling Response time recovery I want to get a replacement time Replacement time for the mistake General Specific Hard to measure Easy to measure
  11. 11. Step 2: Create a CTQ-CTP Relationship Matrix CTP Vs CTQ Screening Engineering Judgment DOE
  12. 12. Step 2: Create a CTQ-CTP Relationship Matrix Explore Interactions CTP Vs CTQ Interrelationships Similar idea referenced by Mikel Harry : http://www.isixsigma.com/forum/ask_dr_harry.asp?ToDo=view&questId=82&catId=11
  13. 13. Step 3: Conduct a Process FMEA FMEA Establish severity, occurrence Creating customized severity, occurrence, and detection scales detection scales for the nature of your business or industry can make a difference – one size does not fit all! Identify Identify the critical process flow of the product line process steps and hold a team brainstorming session to identify all to perform FMEA probable failure modes, causes, and interim and end effects. Identify failure Document current controls as you would in standard modes, causes, effects, FMEA practice. current controls, & risks Further develop the FMEA to include the severity, occurrence, and detection ratings from your customized Identify scales. critical process variables to Calculate risk priority numbers (RPNs) by multiplying monitor, assign the severity, occurrence, and detection ratings. RPN Prioritize risks based on RPN values. Develop control (See more detailed flow next slide) plan (CTQ,CTP) Reference: http://www.qualitytrainingportal.com/resources/fmea/index.htm
  14. 14. Brainstorming all potential causes for failure modes. Inputs: Process flow charts, manufacturing work instructions, historical process defect Pareto, lessons learned, etc. Reference: March 2009 QP article, “FMEA Minus the Headache.”
  15. 15. Populating the FMEA table with discussion outputs. Reference: March 2009 QP article, “FMEA Minus the Headache.”
  16. 16. Step 4: Develop a Control Plan Measurement system that may require R&R – ensure if adequate before proceeding with SPC. Inputs from Step 1 (Identify CTQs and CTPs) and FMEA recommended actions corresponding to CTQ, CTP merge here.
  17. 17. Step 5: Conduct Gage R&R Studies Back to Basics • Let us refresh our memory on some basic definitions: – Repeatability: Variation in measurements obtained with one measuring instrument when used several times by an appraiser (operator) while measuring the identical characteristic on the same part. – Reproducibility: Variation in the average of the measurements made by different appraisers (operators) using the same gage when measuring a characteristic on one part. – Process capability compares the output of an in-control process to the specification limits by using capability indices (Cp, Cpk). The comparison is made by forming the ratio of the spread between the process specifications (the specification quot;widthquot;) to the spread of the process values, as measured by 6 process standard deviation units (the process quot;widthquot;). – Process performance indices (Pp, Ppk) basically try to verify if the sample generated from the process is capable to meet customer CTQs (requirements). Process performance differs from process capability and is only used when process control cannot be evaluated.
  18. 18. Step 5: Conduct Gage R&R Studies Measurement Systems Analysis (MSA) n ∑ f (X ) 2 X Definition of standard i i − X i=1 deviation: S = n -1 SGage Standard deviation of gage (one appraiser) Width 0.5% 0.5% 5.15 SGage Gage 99% of measurements fall repeatability in the gage repeatability range Standard deviation of gage (more than one appraiser) Appraiser 1 Appraiser 3 Appraiser 2 (Back to Basics)
  19. 19. Step 5: Conduct Gage R&R Studies Sources of Variation 100.00% (Back to Basics) Reproducibility σ 2 Total = σ 2Part to Part + σ 2 Repeatabil ity + σ 2 Operator + σ 2 OperatorBy Part Overall Product Repeatability Operator Operator by Part Reference Minitab Help: GR & R Study (Crossed)- ANOVA Method
  20. 20. Step 5: Conduct Gage R&R Studies How can a measurement system contribute in accepting BAD product and rejecting GOOD product ? True measurement of the product True measurement of the product Operator variation (E.g., reading from analog panel) +/- 10 Deg F Oven instrument variation +/- 5 deg F LSL USL Accepting BAD product Rejecting GOOD product (Back to Basics)
  21. 21. Step 5: Conduct Gage R&R Studies Effects of Sources of Variation Overall production variation What was Product produced Operator variation—reading from analog panel Oven instrument variation What was observed Target Process adjustment (Back to Basics)
  22. 22. Step 6: Statistical Process Control (SPC) Assign Unique Man ID XXX + Machine Material Method Environment + Extended free text about Special cause Inputs from FMEA OCAP Brainstorming Data base
  23. 23. Step 6: Statistical Process Control (SPC) – Calculate Cp, Cpk if the process is stable. Short-term Vs Long-term Capability Over long term conditions, a “typical” process will “Short-term shift and drift by capability” (Cp, Cpk) approximately 1.5 standard deviations*. Time 1 Time 2 Time 3 Time 4 “Long-term performance” that includes changes to material, multiple shifts, Operators, environmental changes (Pp, Ppk) Target (Back to Basics) LSL USL
  24. 24. Step 7: Using Process Capability and GR&R to Identify Improvements Baseline GR&R Baseline Capability Pp= 0.8 Ppk= 0.6 Pp= 1 Ppk= 0.93 Pp= 1 Ppk= 1 Oven temperature Order processing time Order handling time Pp= 0.8 Ppk= 0.7 Pp= 0.8 Ppk= 0.8 Pp= 0.6 Ppk= 0.6 Oven time Order handling time Order response time Raw material aging Vegetable aging Order replacement time *Pp= 0.9 Ppk= 0.7 *Pp= 1.2 Ppk= 1.1 *Pp= 1.3 Ppk= 1.2 * Data transformed
  25. 25. Step 7: Using Process Capability and GR&R to Identify Improvements Capability & GR&R Grid High >24% % GR& R* Low <24% Low High Cpk/Ppk* <1.1 >1.1 * GR&R 24%, Cp, Cpk 1.1 are an example. Decide what is acceptable for your organization.
  26. 26. High % GR& R Low Scenario — High GR&R + Low Cp/Pp & Cpk/Ppk Low High True value Cpk/Ppk True value Process of the part of the part shift Operator variation Operator variation reproducibility reproducibility Instrument variation Instrument variation repeatability repeatability LSL USL Accepting BAD product Rejecting GOOD product (Back to Basics)
  27. 27. High % GR& R Low Scenario — Low GR&R + Low Cp/Pp & Cpk/Ppk Low High Cpk/Ppk True value True value of the part Process of the part Shift Operator variation reproducibility Operator variation reproducibility Instrument variation Instrument variation repeatability repeatability LSL USL Accepting BAD product Rejecting GOOD product (Back to Basics)
  28. 28. High % GR& R Low Scenario — High GR&R + High Cp/High Cpk Low High True value Cpk/Ppk True value of the part of the part Operator variation Operator variation reproducibility reproducibility Instrument variation Instrument variation repeatability repeatability LSL USL Accepting BAD product Rejecting GOOD product (Back to Basics)
  29. 29. High % GR& R Low Scenario — Low GR&R + High Cp/Pp & Cpk/Ppk Low High Cpk/Ppk True value True value of the part of the part Operator variation reproducibility Operator variation reproducibility Instrument variation Instrument variation repeatability repeatability LSL USL Accepting BAD product Rejecting GOOD product (Back to Basics)
  30. 30. Step 7: Using Process Capability and GR&R to Identify Improvements Capability & GR&R Grid CTQ1 CTQ3 High CTP3 % GR&R CTQ2 CTP1 Low CTP2 Low High Cpk/Ppk Now that we know the baseline data of performance indices/capability & GR&R of our pizza-making CTQ & CTP, let us place them in the appropriate quadrants.
  31. 31. Step 8 - Prioritizing Improvement Efforts (Process Health Card) CTQ 1 2 3 CTP 1 2 Date GRR/ CL Date CL Alpha/Beta Next due 3 GRR* GRR% ESTB. LCL UCL Stability** Cp/Pp Cpk/Ppk Risk% Date 01/07 37% 01/07 20 24 NO 0.8 0.6 07/07 CTQ1 Product 01/07 8% 01/07 1.30 1.80 YES 0.9 0.88 07/07 CTQ2 01/07 25% 01/07 15 18 NO 1.2 1.15 07/07 CTQ3 CTP1 01/07 7% 01/07 200 208 YES 1.3 1.25 07/07 Process 01/07 12% 01/07 1.5 1.7 YES 1.00 0.92 07/07 CTP2 CTP3 01/07 25% 01/07 1.7 2.0 NO 0.95 0.82 07/07 Relationship Significant Moderate Weak •If new test station/equipment added, operator changed, equipment overhauled, new GR&R study is required. If no changes, 6 months frequency of GR&R monitoring is a good practice. ** If there has been sudden change in process variation (for good or bad), extended period of lack of stability, a new study has to be conducted and control limits recalculated. If no changes, 6 months frequency of review of control limits is a good practice. CTQ: critical to quality characteristics. CTP: critical to process parameters. Relationship between CTP and CTQ to be established up front. Alpha/Beta errors can be obtained from statistical software misclassification feature, or by using simulation software.
  32. 32. FMEA SPC GR&R Establish Identify equipment severity, occurrence Data query/retrieve detection scales (real time – where possible) By process date? Validate data By lot sequence? Identify Plan GR&R sequence By measure date? process steps experiment to perform FMEA Control chart Measure GR&R Measure Identify failure Analyze data capability Cp/Cpk modes, causes, (if stable) effects, current controls Identify & risks special causes (If not stable) Yes Continue monitoring Identify Cp, Cpk acceptable? stability & critical process Estimate process process capability variables to performance indices monitor, assign No RPN Summarize Prioritize improvement efforts the Cp, Cpk, Pp, Develop Ppk, GR&R%, Health control CTQ vs CTP false acceptance, card plan (CTQ,CTP) matrix false reject
  33. 33. Triggering Actionable Discussions • Out of all CTQ and CTP from a given product line, prioritize a vital few for improvement actions. In this example, CTQ1 and CTP3 are prioritized. • By improving CTQ1 and CTP3, we can reduce the producer/consumer risks to a set goal acceptable by customers and manufacturing. • Improve gage R&R to <10%. Improve process capability indices >1.5. • Move items from red, yellow, and blue zones to green based on prioritization.
  34. 34. One might ask… • Why go through these process steps? Why not focus on low process capability to start with? • Answer: This process helps … – Understand whether the CTQs and CTPs that are measured are traceable to customer needs. – Prioritize improvements using the CTQ-CTP relationship matrix. – Review priority for improvement in relationship to measurement capability. (As a containment, organizations would rather risk losing yield than sending nonconforming products to customers. 1% of incorrectly accepted products is 10,000 PPM.)
  35. 35. About Engineering Meetings • Meeting discipline issues narrated in this presentation are common to any organization in general and not targeted on any specific organization. • There is more to engineering meetings than Cpk and Gage R&R: e.g., engineering changes, machine maintenance issues, budget control, etc. • This presentation is targeted to help quality professionals and engineering professionals involved in quality improvement and does not suggest replacing the entire engineering meeting.
  36. 36. Acronyms & Definitions • CTQ: critical to quality (characteristics) • CTC: critical to cost/customer (characteristics) • CTP: critical to process (parameter) • GR&R: gage repeatability & reproducibility • LSL: lower specification limit • USL: upper specification limit • FMEA: failure mode effects analysis • RPN: risk priority number • Alpha risk: probability of rejecting good products • Beta risk: probability of accepting bad products
  37. 37. 55 Acknowledgements, References, & Bibliography • Acknowledgements: – Ms. Noel Wilson, ASQ - Review, feedback, and support. – Mr. Steven Hunt- @ Risk Misclassification & Simulation. – Ms. Cathy Akritas, Minitab Inc- Help with Misclassification macro. – Mr. Ed Russell, Mr. John Noguera, Mr. Andrew Sleeper - Suggestions and guidance for Misclassification Simulation. • References: – Concepts for R&R Studies, ASQ Press, by Larry B. Barrentine. – FMEA Minus the Headache,” QP, April 2009, by Govind Ramu. – Measurement Systems Analysis Manual,AIAG. – MINITAB 15 Help Menu. • Bibliography: – http://www.isixsigma.com/forum/ask_dr_harry.asp?ToDo=view&questId=82&catId=11 – AIAG Statistical Process Control – SPC – http://www.onesixsigma.com/crystalball/Misclassification-Rates-in-Measurement-Systems- Analysis-Gauge-RR-01011970 – http://www.qualitytrainingportal.com/resources/fmea/index.htm
  38. 38. Questions and Answers Thanks:Govind Ramu
  39. 39. 45 Probability of rejecting good products and accepting bad products for Ppk=0.43 and GR&R 30%: E.g., CTQ1 - Red Quadrant Product Distribution Probability of accepting Good Parts 88.08% Probability of rejecting bad parts 8.99% both tails GRR Error Distribution Probability of incorrectly Probability of incorrectly Accepting bad parts rejecting good parts Incorrectly accepted = 12.02% (Beta Risk) Incorrectly rejected = 1.80% (Alpha Risk) Note: The exact percentage of errors was calculated simulating the distribution with 100,000 random data points using @ Risk software and MINITAB Macro. Reference AIAG Manual Measurement System Analysis 3rd Edition- Pages 16-22
  40. 40. Probability of rejecting good products and accepting bad products for Ppk=0.43 and GR&R 10%: E.g., CTQ2 - Yellow Quadrant Product Distribution Probability of accepting Good Parts 89.28% Probability of rejecting bad parts 9.77% both tails GRR Error Distribution Probability of incorrectly Probability of incorrectly Accepting bad parts rejecting good parts Incorrectly accepted = 5.22% (Beta Risk) Incorrectly rejected = 0.68% (Alpha Risk) Note: The exact percentage of errors was calculated simulating the distribution with 100,000 random data points using @ Risk software and MINITAB Macro. Reference AIAG Manual Measurement System Analysis 3rd Edition- Pages 16-22

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