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Technique to develop simplified and linearised models of complex supply chain systems'

Spiegler, V.L.M., Naim, M.M., Towill, Denis R, Royston, D, Wikner, Joakim (2012) Technique to develop simplified and linearised models of complex supply chain systems'. In: The 54th Conference of Operational Research (OR54), 4-6 September 2012, Edinburgh UK. (Unpublished) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:57243)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
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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: Conference or workshop item (Paper)
Subjects: H Social Sciences
H Social Sciences > H Social Sciences (General)
Divisions: Faculties > Social Sciences > Kent Business School > Management Science
Faculties > Social Sciences > Kent Business School > Centre for Logistics and Heuristic Organisation (CLHO)
Depositing User: Virginia Spiegler
Date Deposited: 12 Sep 2016 15:07 UTC
Last Modified: 01 Aug 2019 10:40 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/57243 (The current URI for this page, for reference purposes)
Spiegler, V.L.M.: https://orcid.org/0000-0002-7130-3151
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