The concept of risk has been extensively studied in literature from different perspectives. The result of an analysis of the scientific and managerial literature and manuals led to the identification of several paths of development in the overall field of risk management, such as Financial Risk Management (FRC), Project Risk Management (PRM), Supply Chain Risk Management (ScRM) etc.
The topic of ScRM has received wide attention from researchers. A supply chain is a network of suppliers, manufacturing plants, warehouses, and distribution channels. The main objective is to acquire raw materials, convert these raw materials to finished products, and distribute these products to customers. Academic interest appears to focus mostly on risks associated with logistics and its impact over the supply chain network. However, managing supply-chain risk is a difficult task, due to the fact that individual risks are often interconnected. As a consequence, traditional risk management approaches derived from a single company perspective are not ideally suited to accommodate the requirements in a supply chain context.
Many research works depict the importance and simultaneously the need of ScRM. Chopra and Sodhi (2004) presented a list of supply chain risks associated with disruptions, delays, systems, forecast, intellectual property, procurement, receivables, inventory and capacity. Ritchie and Brindley (2007) examined the constructs underpinning risk management and explored its application in the supply chain context through the development of a framework. Moreover, Cagliano et al. (2012) developed a risk identification and analysis methodology in order to integrate widely adopted supply chain risk management tools.
As far as the mitigation of the impact of supply chain risks is concerned, emphasis is placed on supply management, product management, demand management and information management. Especially a number of papers in the supply management literature deal with the supply network design problem. Published work in this area is based on various deterministic as well as stochastic models.
Arntzen et al. (1995) have considered cost factors that include fixed and variable production charges, inventory charges, distribution expenses via multiple modes, taxes, duties, and duty drawback, and have implemented a mixed integer programming model for Digital Equipment Corporation that serves as a planning system for determining optimal decisions. Huchzermeir and Cohen (1996) developed a modeling framework for global manufacturing strategy planning providing the means for valuation of global manufacturing strategy options under exchange risk. They formulated the problem as a multi-period stochastic programming problem that aims to maximize the discounted after-tax profit. In addition, the structure of the optimal policies for a firm operating plants in different countries subject to exchange rate variability has been studied by Dasu and Li (1997) who formulated a two-country, single market, stochastic dynamic programming model where the combined capacity of the plants exceeds to single product, deterministic demand. A case of particular interest is presented by Camm et al. (1997) who developed a methodology merging integer programming, network optimization models, and a geographical information system for Procter & Gamble’s Supply Chain.
Finally, an active interest has been observed in the area of facility location problems with disruptions. For instance, Holmberg (1999) studied exact solution methods for uncapacitated facility location problems where the transportation costs are nonlinear and convex. Snyder and Daskin (2005) presented models for choosing facility locations to minimize cost, while also taking into account the expected transportation cost after failures of facilities. The goal is to choose reliable facility locations that are inexpensive under traditional objective functions. The Stochastic R-Interdiction Median Problem with Fortification (S-RIMF) presented by Liberatore et al. (2011). The model optimally allocates defensive resources among facilities to minimize the worst-case impact of an intentional disruption.
Arntzen, B.C., G.G. Brown, T.P. Harrison, and L.L. Trafton. 1995. Global Supply Chain Management at Digital Equipment Corporation. Interfaces 25: 69-93.
Cagliano, A.C., A. De Marco, S. Grimaldi, and C. Rafele. 2012. An Integrated Approach to Supply Chain Risk Analysis. Journal of Risk Research 15, no. 7: 817-840.
Chorpa, S., and M.S. Sodhi. 2004. Managing Risk to Avoid Supply-Chain Breakdown. MIT Sloan Management Review 46, no. 1: 53-62.
Dasu, S., and L. Li. 1997. Optimal Operating Policies in the Presence of Exchange Rate Variability. Management Science 43: 705-727.
Holmberg, K. 1999. Exact Solution Methods for Uncapacitated Location Problems with Convex Transportation Costs. European Journal of Operational Research 114: 127-140.
Huchzermeier, A., and M..A. Cohen. 1996. Valuing Operational Flexibility under Exchange Rate Risk. Operations Research 44, no. 1:100-113.
Ritchie, B., and C. Brindley. 2007. Supply Chain Risk Management: A Guiding Framework for Future Development. International Journal of Operations & Production Management 27, no. 3: 303-322.
Liberatore, F., M.P. Scaparra, and M.S. Daskin. 2011. Analysis of Facility Protection Strategies against an Uncertain Number of Attacks: The Stochastic R-Interdiction Median Problem with Fortification. Computers & Operations Research 38: 357-366.
Snyder, L.V., and Daskin, M.S. 2005. Reliability Models for Facility Location: The Expected Failure Cost Case. Transportation Science 39, no. 3: 400-416.
M A N A G E M E N T S C I E N C E L A B O R A T O R Y