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10 statistical quality control
10 statistical quality control
10 statistical quality control
10 statistical quality control
10 statistical quality control
10 statistical quality control
10 statistical quality control
10 statistical quality control
10 statistical quality control
10 statistical quality control
10 statistical quality control
10 statistical quality control
10 statistical quality control
10 statistical quality control
10 statistical quality control
10 statistical quality control
10 statistical quality control
10 statistical quality control
10 statistical quality control
10 statistical quality control
10 statistical quality control
10 statistical quality control
10 statistical quality control
10 statistical quality control
10 statistical quality control
10 statistical quality control
10 statistical quality control
10 statistical quality control
10 statistical quality control
10 statistical quality control
10 statistical quality control
10 statistical quality control
10 statistical quality control
10 statistical quality control
10 statistical quality control
10 statistical quality control
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10 statistical quality control

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  • Relevant pointers to text: Page 109-110
  • Metrics is a very important topic in its own right from project management perspective. What we are seeing here is in the context of quality.
    Relevant pointers to text: Page 56-58
  • Relevant pointers to text: Page 59-81
  • Relevant pointers to text: Page 59-61
  • Relevant pointers to text: Page 63
  • Relevant pointers to text: Page 63-64
  • Relevant pointers to text: Page 63-64
  • Relevant pointers to text: Page 64-65
  • Relevant pointers to text: Page 67-72
  • Relevant pointers to text: Page 72-73
  • Relevant pointers to text: Page 63-64
  • Even in an outsourced environment, the concepts of metrics and the pitfalls listed above would almost surely apply! Hence worth getting a deep appreciation of these
    Relevant pointers to text: Page 75-77
  • Transcript

    • 1. (c) Gopalaswamy Ramesh No part of this presentation can be duplicated without express written consent from the author Statistical Quality Control G Ramesh Gopalaswamy_Ramesh@Yahoo.Com
    • 2. SQC – Session Objectives  During this session you would get to:  Characterize what is “statistical” QC, taking from QA vs QC  Briefly revise the basic concepts of statistics  Appreciate the attributes of an effective measurement system / metrics (essential part of SQC)  Know some of the common pitfalls to watch out for while establishing a Metrics Program  Look at some of the common metrics for different project types  Look at the SQC Tools 2
    • 3. References  MGSP – Chapter 5  Statistics for Management – Levin and Rubin; PHI EEE – Chapter 10 3
    • 4. QA, QC and SQC 4
    • 5. Quality Assurance Vs Quality Control  Quality Assurance  Process Oriented  Prevention Oriented  Proactive  Everybody’s Responsibility  Quality Control  Pertains to Product  Detection Oriented  Reactive  “Tester/Reviewer’s” Responsibility 5
    • 6. Statistical Quality Control  Basic Premises:  Variability is the opposite of quality  Perfect non-variability is not possible causing “randomness”)  Variability (“randomness) has to be “under control”  Variation is of two types  Random variation caused by “general causes”  Systemic variation caused by “special causes”  Variation characterized by a random variable having two attributes  Mean  Standard Deviation 6
    • 7. Metrics “ When you can measure what you are speaking about, you know something about it; but when you cannot measure it, when you cannot express it in numbers, your knowledge is of a meager and unsatisfactory kind; it may be the beginning of knowledge, but you have scarcely in your thought advanced to the stage of science” -- Lord Kelvin (of Kelvin Temperature fame!) 7
    • 8. Importance of Metrics  “If you don’t know where you are going, any road would do” -  A Chinese Proverb  “If you don’t know where you are, a map won’t help”  Watts Humphrey 8 Let us take a real life example!
    • 9. Metrics – Topics  Importance of Metrics  Metrics Roadmap  A typical Metrics strategy  People and Organizational Issues in Metrics  Common Pitfalls to watch out for 9
    • 10. Metrics Roadmap 10 “Where do we want to go?” “What do we have to do?” “How do we measure progress?” “Where are we today?”
    • 11. Metrics Strategy  Decide what you want to measure  Set Targets and track them  Understand and try to minimize variability  Act on data!  Consider the human angle 11
    • 12. What to Measure? 12 Measure things that make an impact on the company goals
    • 13. Decide What You Want To Measure  Is the Metric tied to company vision?  Is it natural?  Is it controllable?  Is it measurable?  Do you know the cost of measuring?  Do you know the benefits of measuring?  Can you afford not to measure it? 13
    • 14. End Goal Metrics and In-process Metrics 14 Customer encounters a problem Customer gets a resolution Problem Reporting Problem To Developers Problem Resolution Fix Distribution tb te t1 t2 t3 t4 t5 End goal Metric: te - tb In-process Metrics: t2 – t1 (Reporting Time), t3 – t2 (Initial Analysis Time), t4 – t3 (Resolution Time), t5 – t4 (Distribution Time), Dictated by Market Controlled internally
    • 15. Set Targets and Track Them ..  SMART Metrics  S pecific  M easurable  A ggressive yet Achievable  R esults-oriented  T ime-bound 15
    • 16. Understand and Minimize Variability: Use of Control Charts or x- charts 16 UCL LCL Mean
    • 17. Characteristics of a Process  Mean  Average; measure of expected value  Upper Control Limit  Maximum value below which a specified % of observations will lie  Lower Control Limit  Minimum value above which a specified % of observations will lie  UCL and LCL are fixed statistically to start with (typically µ+3σ and µ-3σ) and refined later 17
    • 18. Case 1: All observations hovering within UCL- LCL band 0 20 40 60 80 100 1 2 3 4 5 6 7 8 9 10 Engineer Linesofcode/day Value UCL LCL Mean 18
    • 19. Case 2: Some observations outside UCL-LCL band 0 20 40 60 80 100 120 140 1 2 3 4 5 6 7 8 9 10 Engineer Linesofcode/day Value UCL LCL Mean 19
    • 20. Case 3: Most observations outside UCL-LCL band 0 20 40 60 80 100 120 140 1 2 3 4 5 6 7 8 9 10 Engineer Linesofcode/day Value UCL LCL Mean 20
    • 21. Case 4: Observations within UCL-LCL band but with a clear trend 0 20 40 60 80 100 1 2 3 4 5 6 7 8 9 10 Engineer Linesofcode/day Value UCL LCL Mean 21
    • 22. Case 5: "Hugging the Limits" 0 20 40 60 80 100 1 2 3 4 5 6 7 8 9 10 Engineer Linesofcode/day Value UCL LCL Mean 22
    • 23. Act on Data!  Analyze the Data and ask “So What”/”What If”/”Why Not”  Convert Data to Information!  Analyze Root Causes  Process Change? Product Change?  Any Metrics to be added or dropped?  Complete the feedback loop to the people who gave the raw data 23
    • 24. REMEMBER • WHAT YOU CANNOT MEASURE, YOU CANNOT MANAGE • STORY OF THE THREE BLIND MEN AND THE ELEPHANT 24
    • 25. Management Issues in Metrics  Management Commitment  Top Down Vs Bottom Up  Not shooting the messenger  Aggregate Results Vs Individual Performance  Ensuring operational issues are addressed 25
    • 26. Common Pitfalls in Metrics  “Cool Tool Syndrome”  Metrics for appraisals  Not being flexible  Not clarifying operational responsibilities  Not having a common understanding on the significance of the metrics collected  Viewing metrics as a “policing” activity  Perception of metrics as a bureaucratic activity  Not closing the feedback loop fast enough 26
    • 27. Common Metrics by Project Types: Development Projects  Requirement Stability Index  Defect Density by module  Defect Distribution by cause / severity  Defect Containment Efficiency  Rework  … 27
    • 28. Common Metrics by Project Types: Testing Projects  Defect Find Rate  Defect Yield of Tests  Code Coverage (and its variants)  Defects by phase of injection / phase of detection 28
    • 29. Common Metrics by Project Types: Maintenance Projects  Defect Root Cause Classification  By Severity  By Function / Module / product  Defect Frequency  By Severity  By Function / Module / product  Defect Age  Mean Time To Repair  Defect Density  By Module or by LOC 29
    • 30. Common Infrastructure Metrics  HR Metrics  Mean Time to fill a vacancy  Conversion rate of offers  joins  Retention metrics  Interviewing effort  Training Metrics  Training program planned vs. conducted  Attendees planned vs. actual  Satisfaction levels and trends  System Administration Metrics  Server uptime  Link uptime  MTTR  Service calls by priority / cause 30
    • 31. Common “Process” Metrics Across Project Types  Effort Variance  Effort Distribution  Schedule Variance  Productivity Metrics  Review Effectiveness 31
    • 32. Other SQC Tools  Pareto Chart  80-20 rule  Identifies major factors to be addressed to get the maximum “bang for the buck”  Fish Bone Diagram  Identifies root causes for each anomaly 32
    • 33. Example of Pareto Chart (Maintenance Book and also Stats Book pg 541) 33 %of Defects By Category 0 10 20 30 40 50 Memoryerrors Pointererrors Boundary conditions Miscellaneous Designerrors %ofDefects % of Total
    • 34. 34 Delay in Responding to Customer bugs Delay in Getting Right Info from customer Delay in Routing the Problem correctly Delay in Resolving The bugs Delay due to External factors Inadequately Trained Support Analysts Sub-optimal Work Routing Unclear Norms For Routing bugs To correct person Too many products And shuttling across Support Analysts Inadequately Trained developers Poor Documentation / Training High Turnover Code difficult to maintain Not well architected, High cohesion among Modules Poor Documentation Unresponsive Customers Right Info Not Asked From Customers Lack of Automation Lack of a Reproducible test case Multiple Vendors Involved Interfaces not Well defined No agreed upon SLAs New Hardware / Software Configurations not supported
    • 35. Metrics – Topics Re-cap  Importance of Metrics  Metrics Roadmap  A typical Metrics strategy  People and Organizational Issues in Metrics  Common Pitfalls to watch out for 35
    • 36. SQC – Session Objectives Recap  During this session you would get to:  Characterize what is “statistical” QC, taking from QA vs QC  Briefly revise the basic concepts of statistics  Appreciate the attributes of an effective measurement system / metrics (essential part of SQC)  Know some of the common pitfalls to watch out for while establishing a Metrics Program  Look at some of the common metrics for different project types  Look at the SQC Tools 36

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