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What's the buzz about moving from 'lean' to 'agile' integrated supply chains? A fuzzy intelligent agent-based approach.

Authors :
Jain, Vipul
Benyoucef, Lyes
Deshmukh, S.G.
Source :
International Journal of Production Research; Dec2008, Vol. 46 Issue 23, p6649-6677, 29p, 3 Diagrams, 6 Charts, 8 Graphs
Publication Year :
2008

Abstract

The ability to build lean and agile supply chains has not developed as rapidly as anticipated, because the development of technology to manage such concepts of lean/agile for integrated supply chains is still under way. Also, due to ill-defined and vague indicators, which exist within leanness/agility assessment, many measures are described subjectively by linguistic terms, which are characterized by vagueness and multi-possibility, and the conventional assessment approaches cannot suitably or effectively handle such dynamic situations. In this paper, we propose a novel approach to model agility (which includes leanness) and introduce dynamic agility level index (DALi) through fuzzy intelligent agents. Generally, it is difficult to emulate human decision making if the recommendations of the agents are provided as crisp, numerical values. The multiple intelligent agents used in this study communicate their recommendation as fuzzy numbers to accommodate ambiguity in the opinion and the data used for modelling agility attributes for integrated supply chains. Moreover, when agents operate based on different criteria pertaining to agility like flexibility, profitability, quality, innovativeness, pro-activity, speed of response, cost, robustness, etc., for integrated supply chains, the ranking and aggregation of these fuzzy opinions to arrive at a consensus is complex. The proposed fuzzy intelligent agents approach provides a unique and unprecedented attempt to determine consensus in these fuzzy opinions and effectively model dynamic agility. The efficacy of the proposed approach is demonstrated with the help of an illustrative example. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
00207543
Volume :
46
Issue :
23
Database :
Complementary Index
Journal :
International Journal of Production Research
Publication Type :
Academic Journal
Accession number :
34804978
Full Text :
https://doi.org/10.1080/00207540802230462