A Dynamic Look at Commodity Price Risk Management: a case study by using System Dynamics

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As the global economy improves, the raw material markets have shown major price volatility. These price fluctuations can have a significant impact on manufacturers. Companies are suffering from the increasing prices and the greater and greater price volatility for key materials and components, across their supply chains. Similar to these companies, D Company is exposed to price changes in commodities such as raw materials, which represent 13% of total procurement. The procurement risk management department at D Company started the Commodity Price Risk Management (CPRM) project, to explore the potential for a risk management strategy that protects the margin against price volatility in raw material markets. This research aims to support the CPRM project to support policymaking by D Company to improve risk management in this area. Risk means both the uncertainty of the commodity price and the reaction to these uncertainties. The management team often feel that they lack a structured framework for informed decision making in dealing with price changes of their raw materials. In this research, a system dynamics model is built to visualize the structure of pricing mechanism and the resultant price dynamic impact from both the upstream and downstream part of the supply chain. Using scenario analysis on the basis of model simulation to quantify the impact of risk, managers can get a deeper insight into and understand of how the uncertainty of commodity price impacts the supply chain of D Company. In combination, the project at D Company and the quantitative simulation functions of system dynamics models, are able to describe business performance under a range of different scenarios with the alternative hedging policies. This can provide the management team with guidelines that define the actions they should take to either minimize the negative impacts of volatility or capitalize on the positive effects. In doing so, this research further shows how system dynamics models can be used to support decision making in a real business environment. Since there are only a few SD models developed to analyze how a company can manage commodity price risk with the commodity market price captured as an exogenous variable, the most valuable innovation of this research is to show a new way of using a system dynamics model to simulate the forecasted commodity future price based on the historical real data for scenario analysis and measure the performance of different hedging policies. Aligned with the objective and contribution of risk management, the SD model simulation results with hedging policy shows the risk management can reduce the variance of cash flow to increase the firm value (Levi & Sercu, 1991). Keywords System dynamics, Commodity price risk, Risk management, Hedging policy, risk management performance measurement
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