C USTOMER & C OMPETITIVE I NTELLIGENCE

1. CUSTOMER&COMPETITIVEINTELLIGENCE S S IX IGMA FOR SYSTEMSINNOVATION&DESIGN DEPARTMENT OFSTATISTICS REDGEMAN@UIDAHO.EDU OFFICE: +1-208-885-4410 Client, Enterprise & Competitive Intelligence for Product, Process & Systems Innovation Dr. Rick L. Edgeman, University of Idaho DR. RICK EDGEMAN, PROFESSOR& CHAIR– SIX SIGMA BLACK BELT

2. S S IX IGMA DMAIC: The Measure Phase DEPARTMENT OFSTATISTICS Client, Enterprise & Competitive Intelligence for Product, Process & Systems Innovation Dr. Rick L. Edgeman, University of Idaho

3. S S IX IGMA a highly structured strategy for acquiring, assessing, and applying customer, competitor, and enterprise intelligence for the purposes of product, system or enterprise innovation and design. DEPARTMENT OFSTATISTICS Client, Enterprise & Competitive Intelligence for Product, Process & Systems Innovation Dr. Rick L. Edgeman, University of Idaho

4. Definethe problem and customer requirements. Measure defect rates and document the process in its current incarnation. Analyzeprocess data and determine the capability of the process. Improvethe process and remove defect causes. Controlprocess performance and ensure that defects do not recur. Define Control Measure Improve Analyze SixSigmaInnovation& theDMAICAlgorithm Client, Enterprise & Competitive Intelligence for Product, Process & Systems Innovation Dr. Rick L. Edgeman, University of Idaho

5. Measure: • What Measurements are Important and • What Tools Should be Used? • Select Customer Critical to Quality (CTQ) Characteristics; • Define Performance Standards (Numbers & Units); • Establish the Data Collection Plan, • Validate the Measurement System, • and Collect the Necessary Data. Client, Enterprise & Competitive Intelligence for Product, Process & Systems Innovation Dr. Rick L. Edgeman, University of Idaho

6. Measure:1. Select Customer Critical to Quality (CTQ) Characteristics. Among useful quality tools in the MEASURE phase are: Quality Function Deployment(QFD) which relates CTQs to measurable internal sub-processes or product characteristics. Process Mapscreate a shared view of the process, reveals redundant or Unnecessary steps, and compares the “actual” process to the ideal one. Fishbone Diagramsprovide a structure for revealing causes of the effect. Pareto Analysisprovides a useful quantitative means of separating the vital few causes of the effect from the trivial many, but require valid historical data. Failure Modes and Effects Analysis(FMEA) identifies ways that a sub- process or product can fail and develops plans to prevent those failures. FMEA is especially useful with high-risk projects. Client, Enterprise & Competitive Intelligence for Product, Process & Systems Innovation Dr. Rick L. Edgeman, University of Idaho

7. Measure: 1. Select Customer Critical to Quality (CTQ) Characteristics. FAILURE MODES AND EFFECTS ANALYSIS (FMEA) Failure Modes and Effects Analysis (FMEA) Process is a structured approach that has the goal of linking the FAILURE MODES to an EFFECT over time for the purpose of prevention. The structure of FMEA is as follows: Preparation FMEA Process  Improvement a. Select the team b. Develop the process map and steps c. List key process outputs to satisfy internal and external customer requirements d. Define the relationships between outputs and process variables e. Rank inputs according to importance. Client, Enterprise & Competitive Intelligence for Product, Process & Systems Innovation Dr. Rick L. Edgeman, University of Idaho

8. Measure:1. Select Customer Critical to Quality (CTQ) Characteristics. FAILURE MODES AND EFFECTS ANALYSIS (FMEA) Preparation  FMEA Process  Improvement a. Identify the ways in which process inputs can vary (causes) and identify associated FAILURE MODES. These are ways that critical customer requirements might not be met. b. Assign severity, occurrence and detection ratings to each cause and calculate the RISK PRIORITY NUMBERS (RPNs). c. Determine recommended actions to reduce RPNs. d. Estimate time frames for corrective actions. e. Take actions and put controls in place. f. Recalculate all RPNs. FAILUREMODE: How a part or process can fail to meet specifications. CAUSE: A deficiency that results in a failure mode  sources of variation EFFECT: Impact on customer if the failure mode is not prevented or corrected. Client, Enterprise & Competitive Intelligence for Product, Process & Systems Innovation Dr. Rick L. Edgeman, University of Idaho

9. FMEA Standardized Rating System 1 < RPN = (Degree of Severity)*(Likelihood of Occurrence)*(Ability to Detect) < 1000 Client, Enterprise & Competitive Intelligence for Product, Process & Systems Innovation Dr. Rick L. Edgeman, University of Idaho

10. Client, Enterprise & Competitive Intelligence for Product, Process & Systems Innovation Dr. Rick L. Edgeman, University of Idaho

11. Measure: 1. Select Customer Critical to Quality (CTQ) Characteristics. FAILURE MODES AND EFFECTS ANALYSIS (FMEA) Preparation FMEAProcess  Improvement Develop and implement plans to reduce RPN’s. Client, Enterprise & Competitive Intelligence for Product, Process & Systems Innovation Dr. Rick L. Edgeman, University of Idaho

12. Measure:2. Define Performance Standards: Numbers & Units At this stage customer needs are translated into clearly defined measurable traits. OPERATIONAL DEFINITION: This is a precise description that removes any ambiguity about a process and provides a clear way to measure that process. An operational definition is a key step towards getting a value for the CTQ that is being measured. TARGET PERFORMANCE: Where a process or product characteristic is “aimed” If there were no variation in the product / process then this is the value that would always occur. SPECIFICATION LIMIT: The amount of variation that the customer is willing to tolerate in a process or product. This is usually shown by the “upper” and “lower” boundary which, if exceeded, will cause the customer to reject the process or product. DEFECT DEFINITION: Any process or product characteristic that deviates outside of specification limits. Client, Enterprise & Competitive Intelligence for Product, Process & Systems Innovation Dr. Rick L. Edgeman, University of Idaho

13. Measure: 3. Establish Data Collection Plan, Validate the Measurement System, and Collect Data. A Good Data Collection Plan: a. Provides clearly documented strategy for collecting reliable data; b. Gives all team members a common reference; c. Helps to ensure that resources are used effectively to collect only critical data. The cost of obtaining new data should be weighed vs. its benefit. There may be viable historical data available. We refer to “actual process variation” and measure “actual output”: a. what is the measurement process used? b. describe that procedure c. what is the precision of the system? d. how was precision determined e. what does the gage supplier state about: f. Do we have results of either a: * Accuracy * Precision * Resolution * Test-Retest Study? * Gage R&R Study? Client, Enterprise & Competitive Intelligence for Product, Process & Systems Innovation Dr. Rick L. Edgeman, University of Idaho

14. Measure: 3.Establish Data Collection Plan, Validate the Measurement System, and Collect Data. Note that our measurement process may also have variation. a.Gage Variability: Precision:Accuracy:Both: b.Operator Variability: Differences between operators related to measurement. c.Other Variability: Many possible sources. Repeatability:Assess effects within ONE unit of your measurement system, e.g., the variation in the measurements of ONE device. Reproducibility:Assesses the effects across the measurement process, e.g., the variation between different operators. Resolution: The incremental aspect of the measurement device. Client, Enterprise & Competitive Intelligence for Product, Process & Systems Innovation Dr. Rick L. Edgeman, University of Idaho

15. Measure:3. Establish Data Collection Plan, Validate the Measurement System, & Collect Data. GAGE R&R (Repeatability & Reproducibility) STUDY: a.Operators – at least 3 recommended; b.Part – the product or process being measured. It is recommended that at least 10 representative (reflects the range of parts possible) parts per study, with each operator measuring thesame parts. c.Trial – each time the item is measured. There should be at least 3 trials per part, per customer. Client, Enterprise & Competitive Intelligence for Product, Process & Systems Innovation Dr. Rick L. Edgeman, University of Idaho

16. S S IX IGMA Endof Session DEPARTMENT OFSTATISTICS Client, Enterprise & Competitive Intelligence for Product, Process & Systems Innovation Dr. Rick L. Edgeman, University of Idaho