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A technique to develop simplified and linearised models of complex dynamic supply chain systems

Spiegler, Virginia L. M., Naim, Mohamed Mohamed, Towill, Denis Royston and Wikner, Joakim 2016. A technique to develop simplified and linearised models of complex dynamic supply chain systems. European Journal of Operational Research 251 (3) , pp. 888-903. 10.1016/j.ejor.2015.12.004

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Abstract

There is a need to identify and categorise different types of nonlinearities that commonly appear in supply chain dynamics models, as well as establishing suitable methods for linearising and analysing each type of nonlinearity. In this paper simplification methods to reduce model complexity and to assist in gaining system dynamics insights are suggested. Hence, an outcome is the development of more accurate simplified linear representations of complex nonlinear supply chain models. We use the highly cited Forrester production-distribution model as a benchmark supply chain system to study nonlinear control structures and apply appropriate analytical control theory methods. We then compare performances of the linearised model with numerical solutions of the original nonlinear model and with other previous research on the same model. Findings suggest that more accurate linear approximations can be found. These simplified and linearised models enhance the understanding of the system dynamics and transient responses, especially for inventory and shipment responses. A systematic method is provided for the rigorous analysis and design of nonlinear supply chain dynamics models, especially when overly simplistic linear relationship assumptions are not possible or appropriate. This is a precursor to robust control system optimisation.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Business (Including Economics)
Centre for Advanced Manufacturing Systems At Cardiff (CAMSAC)
Subjects: H Social Sciences > HD Industries. Land use. Labor
Publisher: Elsevier
ISSN: 0377-2217
Date of First Compliant Deposit: 30 March 2016
Date of Acceptance: 2 December 2015
Last Modified: 23 Dec 2017 14:56
URI: http://orca.cf.ac.uk/id/eprint/84184

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