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Urjan Jacobs fPET-2010 presentation

Urjan Jacobs fPET-2010 presentation

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  • 1. Quantitative Design Tools Decision Matrices in Engineering Design of Innovative Technology Option 1 Option 2 Option 3 … Weight Criterion A ++ - 0 … … Criterion B 1 5 3 … … Criterion C 0.1 m/s 0.4 m/s 0.03 m/s … … … … … … … … Score … … … … ir Urjan Jacobs 10 May 2010 1 Biotechnology and Society - TNW & Philosophy - TPM
  • 2. Contents Quantitative Design Tools • Innovative conceptual design • Case study & matrix methods • Methodological problems • Examples of issues • A way forwards May 20, 2010 2
  • 3. Innovative technology Engineering design of a system with a new concept Nanotechnology Biotechnology Chemical technology May 20, 2010 3
  • 4. The conceptual design phase Problem definition Concept generation Evaluation & selection Detailed design May 20, 2010 4
  • 5. Case studies Conceptual Process/Product Design (bio)chemical engineering MSc students PDEng trainees 10-12 working weeks May 20, 2010 5
  • 6. Case studies Research methods Observations of design team Following meetings Analysing design documents Semi-structured interview May 20, 2010 6
  • 7. Quantitative design tools Decision matrix methods Quality function deployment Pair-wise comparison charts Analytic Hierarchy Process May 20, 2010 7
  • 8. Matrix methods Multi-criteria decision analysis Decision matrix Solution m rid atrix lect ion g Se Decision grid May 20, 2010 8 Note: Multi-attribute value theory (MAVT) and multi-attribute utility theory (MAUT) have a very different starting point.
  • 9. Arrowian impossibility theorem Considering a finite number of evaluation criteria and at least three alternative design concepts, no method can simultaneously satisfy: Global rationality theory Voting • • Unrestricted scope • Independence of irrelevant concepts • Weak pareto principle • Non-dominance Social choice theory May 20, 2010 9 K.J. Arrow, Journal of Political Economy 58, 1950, 328-346 A. Hylland, Econometrica 48, 1980, 539-542
  • 10. Source of the issues Commensurability of criteria • Measurability (scale of measurement) • Comparability (relation between measures) May 20, 2010 10
  • 11. Measurability Scale Type Admissible Transformation Example Nominal One to one Labels Ordinal Monotonic increasing Mohs scale Interval Positive linear Celsius scale Ratio Positive similarities Miles scale Unknown to Engineers May 20, 2010 11 S.S. Stevens, Science 103, 1946, 677-680
  • 12. Comparability Trade-off relation between measures • Value comparability Revenues • Technical comparability e tu r Safety p era Pr o d t em uctio tor n vol ume R eac Reliability Sustainability May 20, 2010 12
  • 13. Other issues Uncertainty • Setting up of full set criteria. • Independent criteria. • Assigning performance ratings. Design concepts not at same level of abstraction Weights dependant on concept performance May 20, 2010 13
  • 14. Example: Weighted objectives Convincing the design engineers Option 1 Option 2 … Option n Weight Criterion 1 Performance11 Performance12 … Performance1n w1 w2 Criterion 2 Performance21 Performance22 … Performance2n … … … … … … Criterion m Performancem1 Performancem2 … Performancemn wm Score S1 S2 … Sn m Sj = ∑w i =1 i ⋅ Pij May 20, 2010 14
  • 15. Grading issue Criteria Weight Option 1 Option 2 Option 3 Yield 1 2 3 1 By-products 1 3 1 2 Safety 1 2 3 1 Controllabity 1 2 3 1 Revenues 1 3 1 2 Score 12 11 7 Grade: 1=worst, 2=neutral, 3=best. Criteria Weight Option 1 Option 2 Option 3 Yield 1 2 5 1 By-products 1 5 1 2 Change grading Safety Controllabity 1 1 2 2 5 5 1 1 (best 3 5) Revenues 1 5 1 2 Score 16 17 7 Grade: 1=worst, 2=neutral, 5=best. May 20, 2010 15
  • 16. Weighting issue Criteria Weight Option 1 Option 2 Option 3 Yield 0.1 3 2 1 By-products 0.3 1 3 2 Safety 0.2 3 1 2 Controllabity 0.3 3 2 1 Revenues 0.1 1 2 3 Score 2.2 2.1 1.7 Grade: 1=worst, 2=neutral, 3=best. Criteria Weight Option 1 Option 2 Option 3 Yield 0.07 3 2 1 By-products 0.36 1 3 2 Change weighting Safety Controllabity 0.14 0.36 3 3 1 2 2 1 (0.07; 0.14; 0.36) Revenues 0.07 1 2 3 Score 2.14 2.22 1.64 Grade: 1=worst, 2=neutral, 3=best. May 20, 2010 16
  • 17. Buridan's paradox Criteria Weight Option 1 Option 2 Option 3 Yield 0.1 3 2 1 By-products 0.3 2 3 1 Safety 0.2 1 2 3 Controllabity 0.3 2 1 3 Revenues 0.1 3 2 1 Score 2 2 2 Grade: 1=worst, 2=neutral, 3=best. No rational choice … May 20, 2010 17 Aristotle, De Caelo II (On the Heavens), 350 BC
  • 18. Irrelevant alternative issue Criteria Weight Option 1 Option 2 Option 3 Option 4 Yield 1 4 3 2 1 By-products 1 2 4 3 1 Safety 1 4 2 1 3 Controllabity 1 4 2 1 3 Revenues 1 2 4 3 1 Score 16 15 10 9 Grade: 1=worst, 2=poor, 3=fine, 4=best. Criteria Weight Option 1 Option 2 Option 3 Yield 1 3 2 1 By-products 1 1 3 2 Safety 1 3 2 1 Controllabity 1 3 2 1 Revenues 1 1 3 2 Remove/not consider Score 11 12 7 poor option Grade: 1=worst, 2=neutral, 3=best. May 20, 2010 18
  • 19. Traded-away criteria Criteria Weight Option 1 Option 2 Option 3 Yield 1 1 2 3 By-products 1 3 2 1 Safety Controllabity 1 1 1 3 2 1 3 2 Biased on Revenues 1 2 3 1 Sustainability Score 1 3 13 2 12 1 11 sustainability Grade: 1=worst, 2=neutral, 3=best. criterion. Condorcet distortion May 20, 2010 19 M. J.A.N. de Caritat Condorcet, Essai sur l'application de l'analyse à la probabilité des décisions rendues à la pluralité de voix, Paris 1785.
  • 20. How to proceed? Many designers utilize decision matrices. What is their use if not to find the best option? May 20, 2010 20
  • 21. Assessment of design tools Theories of truth Consistent • Coherence with rules Checked • Correspondence by facts • Pragmatic • … Facilitate obtaining goals May 20, 2010 21
  • 22. Pragmatic goals in design practice Goals of matrix methods • Structuring problem • Supports communication • Enhance creativity May 20, 2010 22
  • 23. Problem structuring Ill-structured design problem • No criterion to decide the best solution • Not well defined solution space • No normative framework available Co-evolution of problem & solution May 20, 2010 23
  • 24. Facilitating communication Visual summary Show alternative concepts Converting requirements Judgement on performances Supports debate on the choice CSTR Feb-batch Batch Yield - + ++ By-products + 0 -- Safety + ++ - Revenues + - 0 May 20, 2010 24
  • 25. Creativity enhancement Option 1 Option 2 Option 3 Option 4 Option 5 Option 6 Criterion A + D + ++ + - Criterion B ++ A ++ + - -- Criterion C + T 0 + 0 + Criterion D 0 U - -- ++ 0 Criterion E + M + - -- -- May 20, 2010 25 Controlled convergence method S. Pugh, Total Design, Harlow 1991
  • 26. Conclusion Option 1 Option 2 Option 3 Option 4 … Criterion A + - + 0 … Criterion B ++ ++ + - … Criterion C 0 - -- ++ … Criterion D + + - -- … … … … … … … Keep using the matrix Hold all options & criteria Never calculate a decision May 20, 2010 26
  • 27. Further research Midstream modulation • Collaboration with designers • Stimulate awareness • Motivate to discuss ‘soft’ issues • Safety, sustainability, robustness May 20, 2010 27
  • 28. Many thanks! PDEng trainees MSc students Supervisors & Clients May 20, 2010 28
  • 29. Quantitative Design Tools Decision Matrices in Engineering Design of Innovative Technology Option 1 Option 2 Option 3 Option 4 Option 5 Criterion A + D ++ 0 -- Criterion B ++ A - + - Criterion C + T 0 + 0 Criterion D 0 U - -- ++ Criterion E + M + - - ir. Urjan Jacobs t: +31 (0)15 278 6626 e: j.f.jacobs@tudelft.nl 29 Biotechnology and Society - TNW & Philosophy - TPM