Forecasting models and risk assessment tools for commodity price risk in supply chains: An information processing perspective

Many firms are exposed to risk associated with volatile commodity prices. However, in order to manage this form of risk, organizations first need to forecast and assess the extent of their risk exposure to commodity price movements. The purpose of this paper is to provide insight to forecasting models and risk assessment tools for managing commodity price risk from an Information Processing Theory (IPT) perspective. This study utilizes case study data from 12 manufacturing companies located in Germany, Italy, and the U.S. The firms in this study employ a variety of methods to forecast commodity price volatility and assess commodity price risk using different approaches for acquiring, integrating, distributing, and creating shared meaning of information. Market indices, external service providers, informal discussions, and formal market research are primary sources of commodity price volatility information. Only four companies in the study interpret information using quantitative approaches, with the remaining firms primarily applying managerial judgment. In particular, organizations facing greater detrimental financial consequences from commodity price risk exposure are more likely to invest in formal forecasting models and information processing capabilities to reduce uncertainty. The volume of spend exposed to price volatility, in conjunction with the use of management approaches such as financial hedging to mitigate risk exposure appears to influence the development and use of forecasting models and formal information processing capabilities. The urgency of decision-making and trust in the supply chain also appear to influence the assessment process.