Hostname: page-component-586b7cd67f-g8jcs Total loading time: 0 Render date: 2024-11-26T06:13:54.009Z Has data issue: false hasContentIssue false
  • English
  • Français

Managing uncertainty in potential supplier identification

Published online by Cambridge University Press:  30 September 2014

Yun Ye ,
Marija Jankovic ,
Gül E. Kremer  and
Jean-Claude Bocquet
Yun Ye
Affiliation:
Laboratoire Génie Industriel, Ecole Centrale Paris, Châtenay-Malabry, France
Marija Jankovic*
Affiliation:
Laboratoire Génie Industriel, Ecole Centrale Paris, Châtenay-Malabry, France
Gül E. Kremer
Affiliation:
Engineering Design and Industrial Engineering, Pennsylvania State University, University Park, Pennsylvania, USA
Jean-Claude Bocquet
Affiliation:
Laboratoire Génie Industriel, Ecole Centrale Paris, Châtenay-Malabry, France
*
Reprint requests to: Marija Jankovic, Laboratoire Genie Industriel, Batiment Olivier, Ecole Centrale Paris, Grande Voie des Vignes, 92 295 Châtenay-Malabry Cedex, France; E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

As a benefit of modularization of complex systems, original equipment manufacturers (OEMs) can choose suppliers in a less constricted way when faced with new or evolving requirements. However, new suppliers usually add uncertainties to the system development. Because suppliers are tightly integrated into the design process in modular design and therefore greatly influence the outcome of the OEM's products, the uncertainty along with requirements satisfaction of the suppliers and their modules should be controlled starting from potential supplier identification. In addition, to better satisfy new requirements, the potential supplier identification should be combined with architecture generation to enable the new technology integration. In this paper, we propose the Architecture & Supplier Identification Tool, which generates all possible architectures and corresponding suppliers based on new requirements through matrix mapping and propagation. Using the Architecture & Supplier Identification Tool, the overall uncertainty and requirements satisfaction of generated architectures can be estimated and controlled. The proposed method aims at providing decision support for early design of complex systems, thereby helping OEMs have an integrated view of suppliers and system architectures in requirements satisfaction and overall uncertainty.

Keywords

Complex System DesignModularityPotential Supplier IdentificationUncertainty Management
Type
Special Issue Articles
Information
AI EDAM , Volume 28 , Issue 4: Design of Complex Engineered Systems , November 2014 , pp. 339 - 351
Copyright
Copyright © Cambridge University Press 2014 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Altabbakh, H., Murray, S., Grantham, K., & Damle, S. (2013). Variations in risk management models: a comparative study of the Space Shuttle Challenger disaster. Engineering Management Journal 25(2), 13.Google Scholar
Boehm, B. (1989). Software risk management. Proc. ESEC ’89 (Ghezzi, C., & McDermid, J.A., Eds.), pp. 1–19. Berlin: Springer.Google Scholar
Bryant, C.R., McAdams, D.A., Stone, R.B., Kurtoglu, T., & Campbell, M.I. (2005). A computational technique for concept generation. Proc. ASME 2005 Int. Design Engineering Technical Conf. and Computers and Information in Engineering Conf. Long Beach, CA: American Society of Mechanical Engineers.Google Scholar
Chiriac, N., Hölttä-Otto, K., Lysy, D., & Suk Suh, E. (2011). Level of modularity and different levels of system granularity. Journal of Mechanical Design 133(10), 101007.Google Scholar
Chiu, M.-C., & Okudan, G. (2011). An integrative methodology for product and supply chain design decisions at the product design stage. Journal of Mechanical Design 133(2), 021008.Google Scholar
Clarkson, J., & Eckert, C. (2005). Design Process Improvement: A Review of Current Practice. London: Springer.CrossRefGoogle Scholar
Clarkson, P.J., Simons, C., & Eckert, C. (2004). Predicting change propagation in complex design. Journal of Mechanical Design 126(5), 788–797.Google Scholar
Clemen, R.T., & Winkler, R.L. (1999). Combining probability distributions from experts in risk analysis. Risk Analysis 19(2), 187–203.Google Scholar
Conrow, E.H. (2003). Effective Risk Management: Some Keys to Success. Reston, VA: American Institute of Aeronautics and Astronautics.CrossRefGoogle Scholar
de Boer, L., Labro, E., & Morlacchi, P. (2001). A review of methods supporting supplier selection. European Journal of Purchasing & Supply Management 7(2), 75–89.Google Scholar
de Weck, O., Eckert, C., & Clarkson, J. (2007). A classification of uncertainty for early product and system design. Proc. Int. Conf. Engineering Design, Paper No. DS42P480, Paris.Google Scholar
Fiod-Neto, M., & Back, N. (1994). Assessment of product conception: a critical review. Proc. 1994 Lancaster Int. Workshop on Engineering Design, pp. 35–45, Lancaster, UK.Google Scholar
Fixson, S.K., & Park, J.-K. (2008). The power of integrality: linkages between product architecture, innovation, and industry structure. Research Policy 37(8), 1296–1316.CrossRefGoogle Scholar
Funtowicz, S.O., & Ravetz, J.R. (1993). Science for the post-normal age. Futures 25(7), 739–755.CrossRefGoogle Scholar
Gan, T.-S., & Grunow, M. (2013). Concurrent product–supply chain design: a conceptual framework & literature review. Procedia CIRP 7, 91–96.Google Scholar
Garud, R., & Kumaraswamy, A. (1995). Technological and organizational designs for realizing economies of substitution. Strategic Management Journal 16(S1), 93–109.CrossRefGoogle Scholar
Garud, R., Kumaraswamy, A., & Langlois, R. (2009). Managing in the Modular Age: Architectures, Networks, and Organizations. Hoboken, NJ: Wiley.Google Scholar
Gershenson, J.K., Prasad, G.J., & Zhang, Y. (2003). Product modularity: definitions and benefits. Journal of Engineering Design 14(3), 295–313.Google Scholar
Ha, S.H., & Krishnan, R. (2008). A hybrid approach to supplier selection for the maintenance of a competitive supply chain. Expert Systems With Applications 34(2), 1303–1311.Google Scholar
Halliwell, J., & Shen, Q. (2009). Linguistic probabilities: theory and application. Soft Computing 13(2), 169–183.Google Scholar
Hamm, R.M. (1991). Selection of verbal probabilities: a solution for some problems of verbal probability expression. Organizational Behavior and Human Decision Processes 48(2), 193–223.CrossRefGoogle Scholar
Hillson, D. (2005). Describing probability: the limitations of natural language. Proc. Project Management Institute Global Conf. EMEA, Edinburgh.Google Scholar
Hoetker, G. (2005). How much you know versus how well I know you: selecting a supplier for a technically innovative component. Strategic Management Journal 26(1), 75–96.Google Scholar
Hoetker, G. (2006). Do modular products lead to modular organizations? Strategic Management Journal 27(6), 501–518.Google Scholar
Jankovic, M., Holley, V., & Yannou, B. (2012). Multiple-domain design scorecards: a method for architecture generation and evaluation through interface characterisation. Journal of Engineering Design 23(10–11), 746–766.Google Scholar
Janssen, J.A.E.B., Krol, M.S., Schielen, R.M.J., & Hoekstra, A.Y. (2010). The effect of modelling expert knowledge and uncertainty on multicriteria decision making: a river management case study. Environmental Science & Policy 13(3), 229–238.Google Scholar
Keeney, R.L. (2009). The foundations of collaborative group decisions. International Journal of Collaborative Engineering 1(1), 4–18.Google Scholar
King, A.M., & Sivaloganathan, S. (1999). Development of a methodology for concept selection in flexible design strategies. Journal of Engineering Design 10(4), 329–349.Google Scholar
Lamothe, J., Hadj-Hamou, K., & Aldanondo, M. (2006). An optimization model for selecting a product family and designing its supply chain. European Journal of Operational Research 169(3), 1030–1047.Google Scholar
Langlois, R.N., & Robertson, P.L. (2002). Firms, Markets and Economic Change: A Dynamic Theory of Business Institutions. New York: Routledge.Google Scholar
Le Dain, M.-A., Calvi, R., & Cheriti, S. (2011). Measuring supplier performance in collaborative design: proposition of a framework. R&D Management 41(1), 61–79.Google Scholar
Levinthal, D.A., & Fichman, M. (1991). History dependence in professional relationships: ties that bind. Research in the Sociology of Organizations 8, 119–153.Google Scholar
Lichtenstein, S., & Robert, J. (1967). Empirical scaling of common verbal phrases associated with numerical probabilities. Psychonomic Science 9(10), 563–564.CrossRefGoogle Scholar
Lough, K.G., Stone, R., & Tumer, I.Y. (2009). The risk in early design method. Journal of Engineering Design 20(2), 155–173.Google Scholar
Magee, C.L., & de Weck, O.L. (2004). Complex system classification. Proc. 14th Annual Int. Symp. Int. Council on Systems Engineering (INCOSE), June 20–24.Google Scholar
Marie-Lise, M., Marc, B., Marija, J., & Jean-Claude, B.J. (2012). Product architecture generation and exploration using Bayesian networks. Proc. 12th Int. Design Conf., DESIGN 2012, 1761–1770.Google Scholar
McManus, H., & Hastings, D. (2006). A framework for understanding uncertainty and its mitigation and exploitation in complex systems. IEEE Engineering Management Review 34(3), 81.Google Scholar
Meyer, M.A., & Booker, J.M. (2001). Eliciting and Analyzing Expert Judgment: A Practical Guide. Philadelphia, PA: SIAM.Google Scholar
Michelena, N., & Papalambros, P. (1995). A Hypergraph Framework to Optimal Model-Based Partitioning of Design Problems, Technical Report No. M-MEAM 95-02. Ann Arbor, MI: University of Michigan, Department of Mechanical Engineering and Applied Mechanics.Google Scholar
Moore, P.G. (1983). The Business of Risk. Cambridge: Cambridge University Press.Google Scholar
Nepal, B., Monplaisir, L., & Famuyiwa, O. (2012). Matching product architecture with supply chain design. European Journal of Operational Research 216(2), 312–325.Google Scholar
Nguyen Van, T. (2006, December). System Engineering for Collaborative Data Management Systems: Application to Design/Simulation Loops. Paris: Ecole Centrale Paris.Google Scholar
Pero, M., Abdelkafi, N., Sianesi, A., & Blecker, T. (2010). A framework for the alignment of new product development and supply chains. Supply Chain Management 15(2), 115–128.Google Scholar
Petersen, K.J., Handfield, R.B., & Ragatz, G.L. (2005). Supplier integration into new product development: coordinating product, process and supply chain design. Journal of Operations Management 23(3–4), 371–388.CrossRefGoogle Scholar
Ro, Y.K., Liker, J.K., & Fixson, S.K. (2007). Modularity as a strategy for supply chain coordination: the case of U.S. auto. IEEE Transactions on Engineering Management 54(1), 172–189.Google Scholar
Ro, Y.K., Liker, J.K., & Fixson, S.K. (2008). Evolving models of supplier involvement in design: the deterioration of the Japanese model in U.S. auto. IEEE Transactions on Engineering Management 55, 359–377.Google Scholar
Rosenthal, S.R. (1992). Effective Product Design and Development: How to Cut Lead Time and Increase Customer Satisfaction. New York: Business One Irwin.Google Scholar
Singh, K., & Mitchell, W. (1996). Precarious collaboration: business survival after partners shut down or form new partnerships. Strategic Management Journal 17(S1), 99–115.Google Scholar
Stevens, S.S. (1946). On the theory of scales of measurement. Science 103(2684), 677–680.Google Scholar
Tripathy, A., & Eppinger, S.D. (2011). Organizing global product development for complex engineered systems. IEEE Transactions on Engineering Management 58(3), 510–529.Google Scholar
Ülkü, S., & Schmidt, G.M. (2011). Matching product architecture and supply chain configuration. Production and Operations Management 20(1), 16–31.Google Scholar
Ulrich, K.T., & Eppinger, S.D. (2000). Product Design and Development. New York: Irwin/McGraw–Hill.Google Scholar
Van Eikema Hommes, Q. (2008). Comparsion and application of metrics that define the components modularity in complex products. Proc. ASME 2008 Int. Design Engineering Technical Conf., IDETC, Brooklyn, NY.Google Scholar
Van Wie, M., Grantham, K., Stone, R., Barrientos, F., & Tumer, I. (2005). An analysis of risk and function information in early stage design. Proc. ASME Int. Design Engineering Technical Conf. and Computers and Information in Engineering Conf., Vol. 5, pp. 483–496. New York: American Society of Mechanical Engineers.Google Scholar
Wagner, T.C. (1993). A General Decomposition Methodology for Optimal System Design. Ann Arbor, MI: University of Michigan Press.Google Scholar
Ye, Y., Jankovic, M., & Bocquet, J.-C. (2013). Main factor identification for early negotiation in product design. Proc. Int. Conf. Engineering Design (ICED13), Seoul.Google Scholar
Zhang, X., Huang, G.Q., & Rungtusanatham, M.J. (2008). Simultaneous configuration of platform products and manufacturing supply chains. International Journal of Production Research 46(21), 6137–6162.Google Scholar