Integrated supply chain risk management via operational methods and financial instruments

Abstract

Supply chain risk management (SCRM) is an emerging field that generally lacks integrative approaches across different disciplines. This study contributes to narrowing this gap by developing an integrated approach to SCRM using operational methods and financial instruments. We study a supply chain composed of an aluminium can supplier, a brewery and a distributor. The supply chain is exposed to aluminium price fluctuation and beer demand uncertainty. A stochastic optimisation model is developed for managing operational and financial risks along the supply chain. Using this model as a base, we compare the performance of an integrated risk management model (under which operational and financial risk management decisions are made simultaneously) to a sequential model (under which the financial risk management decisions are made after the operational risk management decisions are finalised). Through simulation-based optimisation and using experimental designs and statistical analyses, we analyse the performance of the two models in minimising the expected total opportunity cost of the supply chain. We examine the supply chain performance as a function of three factors, each at three levels: risk aversion, demand variability and aluminium price volatility. We find that the integrated model outperforms the sequential model in most but not in all cases. Furthermore, while the results indicate that the supply chain improves its performance by being less risk averse, there exists a threshold beyond which accepting a higher risk level is not justified. Managerial insights are provided for various business scenarios experimented with.