Hostname: page-component-586b7cd67f-r5fsc Total loading time: 0 Render date: 2024-11-24T01:57:39.110Z Has data issue: false hasContentIssue false
  • English
  • Français

EFFICIENT DATA GATHERING IN SUPPORT OF DESIGN ISSUE RESOLUTION IN AN AUTOMOTIVE COMPANY

Part of: Design Support Tools

Published online by Cambridge University Press:  11 June 2020

T. M. Sissoko ,
M. Jankovic ,
C. J. J. Paredis  and
E. Landel
T. M. Sissoko*
Affiliation:
CentraleSupélec, France Renault, France
M. Jankovic
Affiliation:
CentraleSupélec, France
C. J. J. Paredis
Affiliation:
Clemson University, United States of America
E. Landel
Affiliation:
Renault, France
*
*[email protected]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

When designing complex systems, multiple people contribute to the process of information collection in support of decision making. In this paper, we study information collection in the Issue Resolution Decision Support (IRDS) framework. We assess the difficulties associated with uncertainty in the often scarce data when implementing the framework in a company and map out how the data sources are scattered across the organization. We study the elicitation process and propose to leverage sensitivity analysis to better allocate data collection efforts.

Keywords

uncertaintydecision makingknowledge sharingdata collectiondecision support
Type
Article
Information
Proceedings of the Design Society: DESIGN Conference , Volume 1 , May 2020 , pp. 403 - 412
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2020. Published by Cambridge University Press

References

Aladwani, A.M. (2001), “Change management strategies for successful ERP implementation”, Business Process Management Journal, Vol. 7 No. 3, pp. 266–275.CrossRefGoogle Scholar
Blessing, L.T.M. and Chakrabarti, A. (2009), DRM, a Design Research Methodology, Springer London, London, available at: https://doi.org/10.1007/978-1-84882-587-1.CrossRefGoogle Scholar
Chen, W., Hoyle, C. and Wassenaar, H.J. (2013), Decision-Based Design, Springer London, London, available at: https://doi.org/10.1007/978-1-4471-4036-8.CrossRefGoogle Scholar
Hollard, G., Massoni, S. and Subjective, J.V. (2010), “Subjective beliefs formation and elicitation rules : experimental evidence HAL Id : halshs-00543828 Centre d'Economie de la Sorbonne Documents de Travail du Subjective beliefs formation and elicitation rules:”Google Scholar
Howard, R. and Abbas, A. (2015), Foundations of Decision Analysis, Prentice Hall, New-York.Google Scholar
Iooss, B. and Lemaître, P. (2015), “A Review on Global Sensitivity Analysis Methods”, In: G., D. and C., M. (Eds.), Uncertainty Management in Simulation-Optimization of Complex Systems, Springer Boston, Boston, MA, pp. 101–122.CrossRefGoogle Scholar
Jones, M.C., Cline, M. and Ryan, S. (2006), “Exploring knowledge sharing in ERP implementation: an organizational culture framework”, Decision Support Systems, Vol. 41 No. 2, pp. 411–434.CrossRefGoogle Scholar
Laskey, K.B. (1995), “Sensitivity analysis for probability assessments in Bayesian networks”, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 25 No. 6, pp. 901–909.CrossRefGoogle Scholar
Leurent, B. et al. (2018), “Sensitivity Analysis for Not-at-Random Missing Data in Trial-Based Cost-Effectiveness Analysis: A Tutorial”, PharmacoEconomics, Vol. 36 No. 8, pp. 889–901.CrossRefGoogle ScholarPubMed
Matt, C., Hess, T. and Benlian, A. (2015), “Digital Transformation Strategies”, Business & Information Systems Engineering, Vol. 57 No. 5, pp. 339–343.CrossRefGoogle Scholar
Rocquigny, E. de Devictor, N. and Tarantola, S. (2008), Uncertainty in Industrial Practice: A Guide to Quantitative Uncertainty Management, edited by de Rocquigny, E., Devictor, N. and Tarantola, S., Wiley & Sons, Ltd.CrossRefGoogle Scholar
Sissoko, T.M. (2019), Supporting Decision-Making for Solving Design Issues in the Development Phase of Automotive Vehicles, Université Paris-Saclay, CentraleSupélec.Google Scholar
Sissoko, T.M. et al. (2018), “An Empirical Study of a Decision-making Process Supported by Simulation in the Automotive Industry”, Proceedings of the ASME 2018 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference.CrossRefGoogle Scholar
Sissoko, T.M. et al. (2019), “A Proposal for a Decision Support Framework to Solve Design Problems in the Automotive Industry”, Proceedings of the ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference IDETC/CIE2019, August 18-21, 2019, Anaheim, CA, USA, pp. 1–10.CrossRefGoogle Scholar
Thompson, S.C. and Paredis, C.J.J. (2010), “An Investigation Into the Decision Analysis of Design Process Decisions”, Journal of Mechanical Design, Vol. 132 No. 12, p. 121009.CrossRefGoogle Scholar