Risk in the context of supply chains may be associated with the production/procurement processes, the transportation/shipment of the goods, and/or the demand markets. Such supply chain risks are directly reflected in firms' financial performances, and priced in the financial market. For example, it has been estimated that the average stock price reaction to supply-demand mismatch announcements was approximately -6.8%. In addition, supply chain disruptions can cause firms' equity risks to increase by 13.50% on average after the disruption announcements.
So how should we measure risk with uncertainties today associated with exchange rates, production disruption frequencies, and/or material and energy prices?
We take a mean-variance (MV) approach to the measurement of risk, which dates to the work of the Nobel laureate Markowtiz (1952, 1959) and which even today, according to my finance colleagues, Schneeweis, Crowder, and Kazemi (2010), remains a fundamental approach to minimizing volatility. The MV approach has been increasingly used in the supply chain management literature to study decision-making under risk and uncertainty.
In a recent study of ours, "Risk Reduction and Cost Synergy in Mergers and Acquisitions via Supply Chain Network Integration," Dr. Zugang Liu and I developed supply chain network models that allow decision-makers to minimize both total expected costs and risks associated with their supply chain network activities both prior to and post a merger or acquisition. In addition, we developed three synergy metrics to assess a potential merger or acquisition (M&A) a priori. These measures capture, respectively, the expected total cost synergy, the absolute risk synergy, and the relative risk synergy.
We focused on supply chain network models since it has been estimated that 80% of a firm's expenses is due to operations.
Since we can expect additional M&As in emerging countries as well as in the developed ones, especially in the healthcare, high tech, and energy sectors, such metrics can be valuable.
Our study has been accepted in the Journal of Financial Decision Making and we will be presenting it next Friday at the First Northeast Regional INFORMS Conference at UMass Amherst in an invited session on Risk Management: Interfaces Between Finance and Operations.
The numerical simulations in our study reveal interesting managerial insights for executives who are faced with M&A decisions. Our first set of examples showed that if the expected total costs and the risks of the merger are negligible, both the total cost and the total risk would be reduced through the merger. In addition, the risk reduction achieved through the merger was more prominent when the uncertainty of link costs was higher.
Our second set of examples showed that the cost and the risk of merger could have a significant impact on the total cost and the total risk of the post-merger firm, and should be carefully evaluated. Our examples also demonstrated that whether a merger makes sense economically may depend on the priority concerns of the decision-makers, and on the measures used to evaluate the gains. For instance, a merger that could not lower the expected total cost might still be able to reduce the total risk, and, hence, be considered beneficial to the firms' stakeholders.