Murdoch University Research Repository

Welcome to the Murdoch University Research Repository

The Murdoch University Research Repository is an open access digital collection of research
created by Murdoch University staff, researchers and postgraduate students.

Learn more

Supplier portfolio selection based on the monitoring of supply risk indicators

Mokhtar, S., Bahri, P.A.ORCID: 0000-0003-4661-5644, Moayer, S. and James, A. (2019) Supplier portfolio selection based on the monitoring of supply risk indicators. Simulation Modelling Practice and Theory, 97 .

Link to Published Version: https://doi.org/10.1016/j.simpat.2019.101955
*Subscription may be required

Abstract

This paper introduces a dynamic optimisation model for a manufacturer's optimal selection of a portfolio of suppliers. The model uses a set of indicators that measure risks imposed by suppliers on the manufacturing supply chain. These indicators measure financial stability, production stability, product quality and cost of suppliers. The model uses a combined simulation-optimisation framework to select suppliers and allocate orders to them based on real-time monitoring of supply risk indicators. This model uses a multi-period order allocation approach based on the viewpoint of a manufacturer in a manufacturing supply chain system. A system dynamics model simulates the interrelations and feedbacks among parties in the supply chain, i.e., suppliers, the manufacturer, and the manufacturing product market. It models the effect of supply risk indicators on a manufacturer's profit over a planning horizon. The result of the simulation is fed to a portfolio optimisation model to determine an optimal supplier order allocation based on the manufacturer's propensity for risk. The model informs the manufacturer to rebalance its supply portfolio in response to early changes in supply risk indicators over a planning horizon. The results show that supplier portfolio selection based on this framework provides higher expected profit and less risks to the manufacturer over the planning horizon. For instance, in our numerical example, the high-level risk averse decision maker made a profit of 5.4% and a risk of 1% less than those of low-level risk averse decision maker at the end of the planning horizon.

Item Type: Journal Article
Murdoch Affiliation: School of Engineering and Information Technology
Publisher: Elsevier
Copyright: © 2019 Elsevier B.V.
URI: http://researchrepository.murdoch.edu.au/id/eprint/50237
Item Control Page Item Control Page