The New ISO/CD Standard

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1 The New ISO/CD Standard and the Effect of Ratio Scale in QFD Thomas M. Fehlmann, Zürich Eberhard Kranich, Duisburg Euro Office AG E: H: Budapest, Hotel Kempinsky October 26 & 27,

2 Dr. Thomas Fehlmann l 1981: Dr. Math. ETHZ l 1991: for Software Black Belt l 1999: Euro Office AG, Zürich l 2001: Akao Price 2001 for original contributions to QFD l 2003: SwissICT Expert for Software Metrics, ICTscope.ch l 2004: Member of the Board QFD Institute Deutschland QFD Architect l 2007: CMMI for Software Level 4 & 5 l 2011: Net Promoter Certified Associate l 2012: Member of the DASMA Board l 2013: Vice-President ISBSG 2

3 Eberhard Kranich l Mathematics and Computer Science l Emphasis on Mathematical Statistics l Mathematical Optimization l Theory of Polynomial Complexity of Algorithms l Worked at T-Systems International GmbH in Bonn, Germany l Black Belt for Software Development l Software Quality Assurance Manager l Member of the DASMA Board 3

4 Agenda l Modern QFD l The Eigenvector Method for Quality Function Deployment l Some important Characteristics of Priority Profiles Quality of a Quality Function Deployment Profiles and Weights 4

5 Modern QFD l Traditional QFD Goal Topic Profiles Scale 0 to 5 Cell Values as Symbols Quality Indicator not used Goal Profile (ζi) Solution Topic Solution Comp. A b.1 Solution Comp. B b.2 60 Cause-Effect Relationship Strong 9 3 Medium 3 o Weak 1 Solution Comp. C b.3 a.1 Major Need Effective Profile (λi) 2.6 Selection Criteria l Modern QFD Profiles are Vectors of Length = 1 Cells have Ratio Scale Teams optimize Convergence Gap Deployment Combinator 's Needs Goal Profile Solution Comp. A x1 Solution Comp. B x2 Solution Comp. C x3 Achieved Profile y1 Major Need a.2 Major Need 2 2 o 36 3 y2 Major Need Raw Weight Solution Profile Effective Profile (λ i ) 5 4 Sum of (ζi-λi) 2 = 1.1 Suitable For Measurement 3 Convergence Factor Solution Profile for Convergence Gap Convergence Range 0.20 Convergence Limit 5

6 The Draft ISO/CD Standard l Applications of statistical and related methods to new technology and product development process Part 1: General Principles and Perspectives of the QFD Method l Drafted by the international QFD Community Spirit of QFD QFD Teams QFD s Scientific foundation QFD Supplier Point of View QFD User Point of View 6

7 The Draft ISO/CD Standard l Major Steps forward Ratio Scale for matrix coefficients Identification of the QFD Matrix as a linear Function Mapping technical solutions into responses as required by customer s needs 7

8 P NP Cost-Driver CD PD linear Understanding 's Voice VoC NPS Non-Polynomial : not computable in predictable amount of time e.g., Weather, Climate Change Quality Function Deployment VoE VoC x y Test Coverage Unit Test VoE Detecting Extra-solar Planets Orbit Star Ripping Audio or Video mp3 Sound Test Coverage App Test VoC 8

9 Control x Control x Control x Control x Measurement Principle with Convergence Gap : Intended Response : Goal Profile : Solution : Achieved Response Goal Topic y Goal Topic y Goal Topic y Goal Topic y The Implement Analyze Implement The Controls Control x 3 9

10 Solving a QFD Matrix Deployment Combinator Guess Goal Profile Deployment Combinator Goal Profile Goal Profile Goal Profile Control 1 CtQ1 Control 1 CtQ1 Control 2 CtQ2 Control 2 CtQ2 Control 3 CtQ3 Control 3 CtQ3 Control 4 CtQ4 Control 4 CtQ4 Achieved Profile y1 Target y2 Target y3 Target Solution Profile for Critical T o Quality Convergence Gap Convergence Range Control 0.20 Convergence Limit Theory Achieved Profile y1 Target y2 Target Deployment Combinator Goal Profile Goal Profile Control 1 CtQ1 Control 2 CtQ2 Control 3 CtQ3 Control 4 CtQ4 Achieved Profile y1 Target y2 Target y3 Target Solution Profile for Critical T o Quality Convergence Gap Convergence Range 0.20 Convergence Limit Practice y3 Target Solution Profile for Critical T o Quality Convergence Gap Convergence Range 0.20 Convergence Limit 10

11 A Measure for Quality the Convergence Gap l The Convergence Gap τ m τ i Goal Profile Achieved Profile i= reveals the quality of the goal profile s approximation by the achieved solution profile l This is the Euclidean Norm Distance between vectors and τ Convergence Gap

12 Comparing Vectors Eigenvector Convergence Gap small Eigenvector Convergence Gap large Control Profile Vectors Control Profile Vectors See Schurr,

13 Traditional Solution Profile and Modern Solution Profile l Eigensolution level Inconsistencies out Similar to Saaty s AHP Calculation Deployment Combinator 's Needs Goal Profile Browser Style GUI Keep to XSQL Standard Open Interfaces Programming x1 x2 x3 x4 Reusable classes x5 Custom Extensions x6 Portfolio Management x7 Reliable Functionality x8 Moderated Forum x9 Achieved Profile y1 Competency to answer inquiries y2 Confidentiality y3 Suitability for business needs y4 Short Development Cycles y5 Functionality where you need it y6 Social competency y7 Communication Solution Profile for Convergence Gap Convergence Range 0.20 Convergence Limit 13

14 Profiles and Weights l In the columns, two priority profiles are summed up yielding the sum of profiles in the third row, and normalized again in the fourth row l Left are the corresponding weight vectors l Summing up the weight vectors and transform them back to profiles yields different results than the sum of profiles l Summing up the corresponding weight vectors is bad mathematics l l Good mathematics is with profiles only When calculating with weights, large vector components leave a bias Weights Profiles Weights Topic 1 5% % Topic 2 85% % Topic 3 10% % 100% % Weight & Profile 1 plus plus Weights Topic 1 33% % Topic 2 34% % Topic 3 33% % 100% % Weight & Profile 2 sum sum Weights Topic % Topic % Topic % % Sum of Profiles 1+2 norm Profiles Weights Topic 1 19% % Topic 2 60% % Topic 3 22% % 100% % Sum of Weights Convergence Gap 14

15 Conclusion l The ISO/CD proposed standard projects QFD into the 21 st century l QFD is thanks to good mathematics implementable in quality processes At least one QFD user has implement New Lanchester Theory into its New Feature Prioritization concept Impact of QFD on agile software development is on the horizon now l QFD will always depend on the teams using it Because it record and documents the reasons for taking some decision l QFD will go mainstream 15

16 Questions? 16

17 World Quality Forum Theory T Solving Multilinear A T Eigenvectors: Theory T A T 17

18 Advantages of Eigensolution Method l Eigensolutions are stable When repeatedly applying the process represented by the transfer function, the response remains always the same l Other solutions might also yield good even better convergence gaps but when repeated the process diverges l Eigensolutions level out inconsistencies 18

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