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Week 8 - Quality Management Learning Objectives

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  1. Week 8 - Quality ManagementLearning Objectives You should be able to: • List and explain common principles of quality management (QM) • List, distinguish between, and describe the processes and tools of Quality Planning, Assurance, and Control • Apply QM principles to Project Management • Apply QM principles to software development project management • Demonstrate how the CMM incorporates QM principles

  2. Quality: • is everyone’s job, • comes from prevention not inspection, • means meeting the needs of customers, • demands teamwork, • requires continuous improvement, • involves strategic planning, • means results, • requires clear measures of success.

  3. History of QM/QC/QA • Deming: plan, do, check, act • Juran: improvement, planning, control • Crosby: zero defects, management commitment • Ishikawa • quality circles, root cause of problems • Taguchi: prevention vs. inspection • Feigenbaum: worker responsibility

  4. Quality Management • Organization-wide commitment: culture • Results and measurement focus • Tools and technical support needed • Training and learning • Continuous improvement of each process • Is it necessary? • Can it be done better?

  5. Pre-production leadership information and analysis strategic quality planning Production human resource allocation quality assurance 7 Malcolm Baldrige Award Categories Post-production • quality results • customer satisfaction

  6. ISO 9000 Standard:5 Elements (500 points) • Quality Planning • Performance Information • Cost of Quality (economics) • Continuous Improvement • Customer Satisfaction

  7. Quality in Project Management I • ISO 9000, TQM, CQI principles • Prevention over inspection • lower cost, higher productivity, more cust. satisfaction • Management responsibility and team participation • Plan-do-check-act (re: Deming, etc.) - PDCA • Applied successfully in environments that have well-defined processes and products • More difficult in areas like software development

  8. Quality in Project Management II • Customer satisfaction • validation: “the right job done” • Conformance to specifications • verification: “the job done right” • Fitness for use • can be used as intended • Satisfaction of implied or stated needs • All project stakeholders considered • Project Management: making implicit needs explicit • Project Processes and Product • continuous improvement of both

  9. Product Description QualityStandards Checklists QualityManagementPlan Project Scope Quality Planning Work Results Quality Policy Quality Assurance OperationalDefinitions Quality Control Quality Improvement Actions

  10. Quality Planning (QP) • Identifying relevant quality standards • Determining how to meet them • QP inputs: • quality policy: adopted, disseminated • scope and product description • standards, regulations

  11. Software Quality Planning • Functionality • features: required and optional • Outputs • Performance • volume of data, number of users • response time, growth rate • Reliability: MTBF (mean time between failures) • Maintainability

  12. QP Outputs • Quality management plan • how team will implement quality policy • structure, responsibilities, resources, processes • (same as project plan?) • Operational definitions • metrics: what it is and how it’s measured • Checklists • industry-specific

  13. Quality Assurance (QA) • Evaluating project performance regularly to assure progress towards meeting standards • Inputs: • quality management plan • operational definitions • results of measurements • Outputs: • quality improvement actions • Tools: QP tools, quality audits

  14. QP/QA Tools • Cost / benefit analysis and tradeoffs • less rework = higher productivity, lower costs, stakeholder satisfaction • Design of Experiments • comparison of options, approaches • Benchmarking • comparison of project practices to best practices • Cause and effect (fishbone, etc., diagrams)

  15. Quality Control (QC) • Monitoring project results • Measuring compliance with standards • Determining causes if not in compliance • Identifying ways to eliminate causes • Performed throughout project life cycle

  16. Inputs: Work results Quality Management Plan Operational Definitions Checklists Outputs: Quality Improvement Acceptance decisions Rework Process adjustments corrective or preventive actions Completed checklists project records QC Inputs and Outputs

  17. Inspection: measuring, examining, testing products Control Charts: monitor output variables detect instability in process graphical display of results Pareto analysis 80 / 20 rule histogram: frequencies Statistical sampling acceptable deviation 6-sigma 7-run rule QC Tools

  18. Statistical Quality Control • Prevention • keeping errors out of the process • Inspection • keeping defects from the customer • Sampling: attributes and variables • Tolerances: acceptable ranges • Control limits: acceptable levels

  19. Testing (Software) • During most phases of product development • Unit tests • Integration testing • System testing • User acceptance testing

  20. Improving Software Quality • Leadership • top management and organization-wide commitment to quality • Costs of quality • cost of non-conformance • costs: prevention, appraisal, failures, testing • Work environment

  21. PMI Maturity Model: 5 levels • Ad-hoc: chaotic, chronic cost & schedule delays • Abbreviated: processes in place, but not predictable • Organized: documented, standards that are used • Managed: measures are collected • Adaptive: • feedback enables continuous improvement • project success is norm

  22. Capability Maturity Model (CMM) - 5 levels 1. Initial: chaotic, heroic efforts, unpredictable 2. Repeatable: processes & standards established 3. Defined: documented standards, training, use 4. Managed: quantitative measures, predictable 5. Optimizing: defect-prevention, organization-wide continuous improvement

  23. CMM and Quality(see Appendix A: goals for key process areas) Level 2: • requirements management (customer focus) • project planning (quality planning) • project tracking and oversight (quality control) • software quality assurance • configuration management (prevention)

  24. CMM Level 3 and Quality • organization process focus (commitment) • organization process definition (operational definitions) • training program • software product engineering (prevention) • intergroup organization (teamwork) • peer reviews (teamwork)

  25. CMM Level 4 and Quality • Quantitative process improvement • Software quality management goals • planned and measured CMM Level 5 and Quality • Defect Prevention (prevention) • Technology and Process Change Management (continuous improvement)

  26. Achieving Software Quality • Focus on critical requirements early • Use metrics early and continuously • Provide development tools supporting: • configuration control, change control • test automation, self-documentation • abstraction, reliability, reuse • Early and continuous demonstration-based evaluations • Major milestone demonstrations assessed against critical requirements

  27. Software Quality Measurement • Software quality measured by ease of change • Examples of data collected: • Number and types of changes • number of components / effort (FPs, SLOC, classes...) • number of change orders (SCOs) • number of defective and fixed components • Baseline: total size (SLOC, FP, classes, etc.) • Scrap: broken code, may or may not be fixed • Rework: healthy early in project, should decrease

  28. SCO: Software Change Order 1. rework a poor quality component (fix) 2. rework to improve quality (enhancement) 3. accommodate new customer requirement (scope change) Configured Baseline: • the set of products subject to change control • size of “completed” components

  29. Software Quality Metrics • Modularity: • breakage localization: extent of change re: baseline size • Adaptability • cost of change (effort needed to resolve and retest) • Maturity • number of SCO’s over time = MTBF during testing • Each of above 3 should decrease over time • Maintainability • productivity of rework / productivity of development

  30. Operational Definitions • Defects: measured by change orders SCOs • Open rework (breakage) • broken components measured by SCOs • Closed rework (fixes) • fixed SCOs • Rework effort: effort expended fixing SCOs • Usage time: baseline testing in normal use

  31. Quantifying Quality Metrics • Modularity: • breakage / SCOs • Adaptability • rework effort / SCOs • Maturity • usage time / SCOs (mean time between defects) • Maintainability • (percent broken) / (percent rework vs. total effort) • End-product and “over time” indicators

  32. Pros Team development Accountability Determine causes of defects 20%: critical components Cons Superficial Not cost effective Other QA activities are more effective Peer inspections: pros and cons

  33. Project Quality Management (cont.)

  34. Quality of IT Projects • Many people joke about the poor quality of IT products (cars and computers joke) • People seem to accept systems being down occasionally or needing to reboot their PCs • There are many examples in the news about quality-related problems

  35. What Went Wrong? • In one of the biggest software errors in banking history, Chemical Bank mistakenly deducted about $15 million from more than 100,000 customer accounts one evening. The problem resulted from a single line of code in an updated computer program that caused the bank to process every withdrawal and transfer at its automated teller machines (ATMs) twice. For example, a person who withdrew $100 from an ATM had $200 deducted from his or her account, though the receipt only indicated a withdrawal of $100. The mistake affected 150,000 transactions from Tuesday night through Wednesday afternoon. • In 1996 Apple Computer's PowerBook 5300 model had problems with lithium-ion battery packs catching fire, causing Apple to halt shipments and replace all the packs with nickel-metal-hydride batteries. Other quality problems also surfaced, such as cracks in the PowerBook's plastic casing and a faulty electric power adapter. • Hundreds of newspapers and web sites ran stories about the "Melissa" virus in March of 1999. The rapidly spreading computer virus forced several large corporations to shut down their e-mail servers as it rode the Internet on a global rampage, according to several leading network security companies.

  36. What Is Project Quality Management? • The International Organization for Standardization (ISO) defines quality as the totality of characteristics of an entity that bear on its ability to satisfy stated or implied needs • Other experts define quality based on • conformance to requirements: meeting written specifications • fitness for use: ensuring a product can be used as it was intended

  37. Project Quality Management Processes • Quality planning: identifying which quality standards are relevant to the project and how to satisfy them • Quality assurance: evaluating overall project performance to ensure the project will satisfy the relevant quality standards • Quality control: monitoring specific project results to ensure that they comply with the relevant quality standards while identifying ways to improve overall quality

  38. Modern Quality Management • Modern quality management • requires customer satisfaction • prefers prevention to inspection • recognizes management responsibility for quality • Noteworthy quality experts include Deming, Juran, Crosby, Ishikawa, Taguchi, and Feigenbaum

  39. Quality Experts • Deming was famous for his work in rebuilding Japan and his 14 points • Juran wrote the Quality Control Handbook and 10 steps to quality improvement • Crosby wrote Quality is Free and suggested that organizations strive for zero defects • Ishikawa developed the concept of quality circles and using fishbone diagrams • Taguchi developed methods for optimizing the process of engineering experimentation • Feigenbaum developed the concept of total quality control

  40. Sample Fishbone or Ishikawa Diagram

  41. Malcolm Baldrige Award and ISO 9000 • The Malcolm Baldrige Quality Award was started in 1987 to recognize companies with world-class quality • ISO 9000 provides minimum requirements for an organization to meet their quality certification standards

  42. Quality Planning • It is important to design in quality and communicate important factors that directly contribute to meeting the customer’s requirements • Design of experiments helps identify which variables have the most influence on the overall outcome of a process • Many scope aspects of IT projects affect quality like functionality, features, system outputs, performance, reliability, and maintainability

  43. Quality Assurance • Quality assurance includes all the activities related to satisfying the relevant quality standards for a project • Another goal of quality assurance is continuous quality improvement • Benchmarking can be used to generate ideas for quality improvements • Quality audits help identify lessons learned that can improve performance on current or future projects

  44. Quality Control • The main outputs of quality control are • acceptance decisions • rework • process adjustments • Some tools and techniques include • pareto analysis • statistical sampling • quality control charts • testing

  45. Pareto Analysis • Pareto analysis involves identifying the vital few contributors that account for the most quality problems in a system • Also called the 80-20 rule, meaning that 80% of problems are often due to 20% of the causes • Pareto diagrams are histograms that help identify and prioritize problem areas

  46. Sample Pareto Diagram

  47. Statistical Sampling and Standard Deviation • Statistical sampling involves choosing part of a population of interest for inspection • The size of a sample depends on how representative you want the sample to be • Sample size formula: Sample size = .25 X (certainty Factor/acceptable error)

  48. Commonly Used Certainty Factors 95% certainty: Sample size = 0.25 X (1.960/.05) = 384 90% certainty: Sample size = 0.25 X (1.645/.10) = 68 80% certainty: Sample size = 0.25 X (1.281/.20) = 10

  49. Standard Deviation • Standard deviation measures how much variation exists in a distribution of data • A small standard deviation means that data cluster closely around the middle of a distribution and there is little variability among the data • A normal distribution is a bell-shaped curve that is symmetrical about the mean or average value of a population

  50. Normal Distribution and Standard Deviation