Research in Engineering Design

, Volume 24, Issue 1, pp 1–18 | Cite as

Fuzzy decision support for tools selection in the core front end activities of new product development

  • Sofiane Achiche
  • Francesco Paolo Appio
  • Tim C. McAloone
  • Alberto Di Minin
Original Paper


The innovation process may be divided into three main parts: the front end (FE), the new product development (NPD) process, and the commercialization. Every NPD process has a FE in which products and projects are defined. However, companies tend to begin the stages of FE without a clear definition or analysis of the process to go from Opportunity Identification to Concept Generation; as a result, the FE process is often aborted or forced to be restarted. Koen’s Model for the FE is composed of five phases. In each of the phases, several tools can be used by designers/managers in order to improve, structure, and organize their work. However, these tools tend to be selected and used in a heuristic manner. Additionally, some tools are more effective during certain phases of the FE than others. Using tools in the FE has a cost to the company, in terms of time, space needed, people involved, etc. Hence, an economic evaluation of the cost of tool usage is critical, and there is furthermore a need to characterize them in terms of their influence on the FE. This paper focuses on decision support for managers/designers in their process of assessing the cost of choosing/using tools in the core front end (CFE) activities identified by Koen, namely Opportunity Identification and Opportunity Analysis. This is achieved by first analyzing the influencing factors (firm context, industry context, macro-environment) along with data collection from managers followed by the automatic construction of fuzzy decision support models (FDSM) of the discovered relationships. The decision support focuses upon the estimated investment needed for the use of tools during the CFE. The generation of FDSMs is carried out automatically using a specialized genetic algorithm, applied to learning data obtained from five experienced managers, working for five different companies. The automatically constructed FDSMs accurately reproduced the managers’ estimations using the learning data sets and were very robust when validated with hidden data sets. The developed models can be easily used for quick financial assessments of tools by the person responsible for the early stage of product development within a design team. The type of assessment proposed in this paper would better suit product development teams in companies that are cost-focused and where the trade-offs between what (material), who (staff), and how long (time) to involve in CFE activities can vary a lot and hence largely influence their financial performances later on in the NPD process.


Decision support Fuzzy front end New product development Fuzzy logic Genetic algorithms 



Artificial intelligence


Core front end




Decision support system


Estimate Investment


Fuzzy decision support model


Fuzzy decision support system


Final evaluation card


Front end (fuzzy front end)


Genetic algorithm




Management support system


New product development


Real/Binary-like coded genetic algorithm


Structured decision system


  1. Achiche S, Baron L, Balazinski M (2004a) Real/binary-like coded versus binary coded genetic algorithms to automatically generate fuzzy knowledge bases: a comparative study. Eng Appl Artif Intell 17(4):313–325CrossRefGoogle Scholar
  2. Achiche S, Baron L, Balazinski M (2004b) Scheduling exploration/exploitation levels in genetically-generated fuzzy knowledge bases, Annual Conference of the North American Fuzzy Information Processing Society—NAFIPS 2004, pp 401–406Google Scholar
  3. Achiche S, Wozniak A, Balazinski M, Baron L (2006) Fuzzy rule base influence on genetic-fuzzy reconstruction of CMM 3D triggering probe error characteristics. Annual Meeting of the North-American-Fuzzy-Information-Processing-Society, NAFIPS, pp 85–89Google Scholar
  4. Anthony RN, Hawkins D, Merchant K (2007) Accounting: texts and cases, 12th edn. McGraw-Hill, New YorkGoogle Scholar
  5. Brown R (1992) Managing the “S” curves of innovation. J Mark Manag 9(1):61–72Google Scholar
  6. Bruseberg A, McDonagh-Philp D (2002) Focus groups to support the industrial/product designer: A review based on current literature and designers’ feedback. Elsevier, AmsterdamGoogle Scholar
  7. Chan Kim W, Mauborgne R (eds) (2005) Blue ocean strategy: how to create uncontested market space and make competition irrelevant, 1st edn. Harvard Business Press, BostonGoogle Scholar
  8. Chang H, Wei C, Lin R (2008) A model for selecting product ideas in fuzzy front end. SAGE Publications, Thousand OaksGoogle Scholar
  9. Cooper RG (2001) Winning at New Products: Accelerating the Process from Idea to Launch, 3rd edn. Addison-Wesley, Cambridge, MAGoogle Scholar
  10. Cordon O, Herrera F, Villar P (2000) Analysis and guidelines to obtain a good uniform fuzzy partition granularity for fuzzy rule-based systems using simulated annealing. Int J Approximate Reasoning 25(3):187–215MATHCrossRefGoogle Scholar
  11. Dahl DW, Moreau P (2002) The influence and value of analogical thinking during new product ideation. J Mark Res 39(1):47–60CrossRefGoogle Scholar
  12. Dalkey N, Helmer O (1963) An experimental application of the Delphi method to the use of experts. Manage Sci 9(3):458–467CrossRefGoogle Scholar
  13. Dore R, Pailhes J, Fischer X, Nadeau JP (2007) Identification of sensory variables towards the integration of user requirements into preliminary design. Int J Ind Ergon 37(1):1–11CrossRefGoogle Scholar
  14. Duda RO, Hart PE, Stork DG (2001) Pattern Classification, 2nd edn. Wiley-Interscience, HobokenMATHGoogle Scholar
  15. Eshelman LJ, Schaffer JD (1993) Real-Coded Genetic Algorithms and Interval-Schemata. Proc Conf Found Genet Algorithms 2:187–202Google Scholar
  16. European Commission Directorate-General for Enterprise and Industry (2008) Insights on Innovation Management in Europe: Tangible Results from IMP3rove. 10. Office for Official Publications of the European Communities, LuxembourgGoogle Scholar
  17. Goldberg DE (1989) Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Longman Publishing Co., Inc., Boston, MAMATHGoogle Scholar
  18. Green PE, Srinivasan V (1990) Conjoint-analysis in marketing - new developments with implications for research and practice. J Mark 54(4):3–19CrossRefGoogle Scholar
  19. Grundy T (2006) Rethinking and reinventing Michael Porter’s five forces model. Strategic Change 15(5):213–229CrossRefGoogle Scholar
  20. Jones KL (2007) PEST or STEP Analysis.
  21. Kahaner L (1998) Competitive intelligence: how to gather, analyze, and use information to move your business to the top. Touchstone Books, USAGoogle Scholar
  22. Karsak EE (2000) Fuzzy MCDM procedure for evaluating flexible manufacturing system alternatives. In: Proceedings of the 2000 IEEE Engineering Management Society, 2000. pp 93–98. doi:10.1109/EMS.2000.872483
  23. Karwowski W, Mital A (1986) Applications of approximate reasoning in risk analysis. Applications of fuzzy set theory in human factors. Elsevier Science, AmsterdamGoogle Scholar
  24. Kim J, Wilemon D (2002) Focusing the fuzzy front-end in new product development. R&D Manag 32(4):269–279CrossRefGoogle Scholar
  25. Koen PA (2004) The fuzzy front end for incremental, platform, and breakthrough products. In: Kahn KB (ed) The PDMA handbook of new product development, 2nd edn. Wiley, HobokenGoogle Scholar
  26. Koen PA, Ajamian GM, Boyce S, Clamen A, Fisher E, Fountoulakis S, Johnson A, Puri P, Seibert R (2002) Fuzzy front end: effective methods, tools, and techniques. In: Belliveau P, Griffin A, Somermeyer S (eds) PDMA ToolBook for New Product Development. Wiley, New YorkGoogle Scholar
  27. Kostoff RN, Scaller RR (2001) Science and technology roadmaps. IEEE Trans Eng Manage 48(2):132–143CrossRefGoogle Scholar
  28. Kotler P (1990) Marketing management: analysis, planning, implementation, and control, 7th edn. Prentice Hall, New JerseyGoogle Scholar
  29. Lai HH, Lin YC, Yeh CH, Wei CH (2006) User-oriented design for the optimal combination on product design. Int J Prod Econ 100(2):253–267CrossRefGoogle Scholar
  30. Lilien GL, Morrison PD, Searls K, Sonnack M, von Hippel E (2002) Performance assessment of the lead user idea-generation process for new product development. Manage Sci 48(8):1042–1059CrossRefGoogle Scholar
  31. Lin CT, Chen CT (2004) A fuzzy-logic-based approach for new product Go/NoGo decision at the front end. IEEE Transactions on Systems Man and Cybernetics Part A-Systems and Humans 34(1):132–142CrossRefGoogle Scholar
  32. List D (2005) Market research methods for innovation development: an overview.
  33. Lu J, Zhu YJ, Zeng XY, Koehl L, Ma J, Zhang GQ (2008) A fuzzy decision support system for garment new product development. In: W Wobcke and M Zhang (eds) Al 2008: advances in artificial intelligence. Lecture Notes in Computer Science. Springer, Berlin/Heidelberg Google Scholar
  34. McAloone TC, Bey N (2009) Environmental improvement through product development: A guide. Danish Environmental Protection Agency, CopenhagenGoogle Scholar
  35. McGrath ME, Akiyama CL (1996) PACE: an integrated process for product and cycle-time excellence. In: McGrath ME (ed) Setting the PACE in Product Development (Revised Edition). Butterworth-Heinemann, Boston, pp 17–30Google Scholar
  36. McKeen JD (1983) Successful development strategies for business application systems. Manag Inf Syst Q 7(3):47–65CrossRefGoogle Scholar
  37. Meador CL, Guyote MJ, Keen PGW (1984) Setting priorities for DSS development. Management Information Systems Quarterly 8(2):117–129CrossRefGoogle Scholar
  38. Monteiro C, Arcoverde DF, Da Silva FQB, Ferreira HS (2010) Software support for the fuzzy front end stage of the innovation process: A systematic literature review. 5th IEEE International Conference on Management of Innovation and Technology, ICMIT2010, pp 426–431Google Scholar
  39. Myers JH (1996) Segmentation positioning strategic marketing decisions. McGraw-Hill Inc., USAGoogle Scholar
  40. Neumann S, Hadass M (1980) DSS and strategic decisions. California Management Review 22(2):77–84CrossRefGoogle Scholar
  41. Prokopska A (2001) Application morphological analysis methodology architectural design. Acta Polytechnica 41(1):46–54Google Scholar
  42. Rangaswamy A, Lilien GL (1997) Software tools for new product development. J Mark Res 34(1):177–184CrossRefGoogle Scholar
  43. Reich Y, Barai SV (1999) Evaluating machine learning models for engineering problems. Artif Intell Eng 13(3):257–272CrossRefGoogle Scholar
  44. Richardson AJ, Hupp RC, Seethaler RK (2003) The use of lateral thinking in finding creative conflict resolutions, 5th Annual Conference of the The American Bar Association Section of Dispute Resolution 2003Google Scholar
  45. Rossiter JR, Lilien GL (1994) New “Brainstorming” Principles. Australian Journal of Management 19(1):61–72CrossRefGoogle Scholar
  46. Sample JA (1984) Nominal Group Technique: an alternative to brainstorming. J Ext 22(2).
  47. Schilling M (2005) Strategic management of technological innovation, 2nd edn. McGraw-Hill, New YorkGoogle Scholar
  48. Schilling MA, Hill CWL (1998) Managing the new product development process: strategic imperatives. Acad Manag Executive (1993–2005) 12(3):67–81Google Scholar
  49. Schoemaker PJH (1995) Scenario planning: a tool for strategic thinking: Sloan management review. Long Range Plan 28(3):25–40Google Scholar
  50. Simon HA (1960) The new science of management decisions. Harper and Row, New-YorkCrossRefGoogle Scholar
  51. Strasser S, London L, Kortenbout E (2005) Developing a competence framework and evaluation tool for primary care nursing in South Africa. Educ Health 18(2):133–144CrossRefGoogle Scholar
  52. Subba Rao S, Nahm A, Shi Z, Deng X, Syamil A (1999) Artificial intelligence and expert systems applications in new product development—a survey. J Intell Manuf 10(3):231–244CrossRefGoogle Scholar
  53. UNIDO (2005) Organization and methods. Technology foresight manual. United Nations Industrial Development Organization, Vienna, pp 1–260Google Scholar
  54. United Nations Centre for Regional Development (2001) KJ method, second thematic training course. United Nations Centre for Regional Development, Nagoya, JapanGoogle Scholar
  55. van Kleef E, van Trijp HCM, Luning P (2005) Consumer research in the early stages of new product development: a critical review of methods and techniques. Food Qual Prefer 16(3):181–201CrossRefGoogle Scholar
  56. Watson B, Radciffe D (1998) Structuring design for X tool use for improved utilization. J Eng Des 9(3):211–223CrossRefGoogle Scholar
  57. Yang CL, Fang HH, Yang CL, Fang HH (2003) Integrating fuzzy logic into quality function deployment for product positioning. J Chin Inst Ind Eng 20(3):275–281Google Scholar
  58. IPU, York Refrigeration, PTC Denmark (2005) Lean product structures—case: York. Denmark.
  59. Zadeh LA (1973) Outline of a new approach to the analysis of complex systems and decision processes. IEEE Trans Syst man Cybern SMC 3(1):28–44MathSciNetMATHCrossRefGoogle Scholar
  60. Zadeh LA (1975) The concept of a linguistic variable and its application to approximate reasoning—II. Inf Sci 8(4):301–357MathSciNetMATHCrossRefGoogle Scholar
  61. Zhang Q, Doll WJ (2001) The fuzzy front end and success of new product development: a causal model. Eur J Innov Manag 4(2):95–112CrossRefGoogle Scholar
  62. Zhang FY, Xu YS, Hu DJ (2004) The objectives decision making study in product innovation development process based on TRIZ technology evolution theory. Mater Sci Forum 471–472:613–619CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2012

Authors and Affiliations

  • Sofiane Achiche
    • 1
  • Francesco Paolo Appio
    • 2
  • Tim C. McAloone
    • 3
  • Alberto Di Minin
    • 2
  1. 1.Department of Mechanical EngineeringÉcole Polytechnique de MontréalMontréalCanada
  2. 2.Istituto di ManagementScuola Superiore Sant’AnnaPisaItaly
  3. 3.Department of Mechanical EngineeringTechnical University of DenmarkKgs. LyngbyDenmark

Personalised recommendations