عملکرد ارزش- محور و مدیریت ریسک در زنجیره های تأمین: روش نیرومند بهینه سازی

Integrated performance and risk management is the key lever to increase shareholder value holistically. In this paper, we develop a corresponding framework for value-based performance and risk optimization in supply chains. Economic Value Added (EVA) as a prevalent metric of value-based performance is applied to mid-term sales and operations planning (S&OP). Robust optimization methods are utilized to deal with operational risks in physical and financial supply chain management due to the uncertainty of future events. Multiple aspects of robustness and general implications of the framework are highlighted using a case-oriented numerical analysis.

Creating shareholder value is commonly considered the paramount business goal (Young and O’Byrne, 2001) and requires an integrated approach to performance and risk management (cf. Ritchie and Brindley, 2007 and Oehmen et al., 2009). Value-based management (VBM) provides a corresponding framework utilizing value driver trees and risk-adjusted performance metrics as major concepts for performance and risk management (Kaplan and Atkinson, 1998). Value driver trees drill down a top-level performance metric into operational levers for performance management (Rappaport, 1998). Risk implications are considered within the performance metrics via risk-adjusted cost of capital (Young and O’Byrne, 2001). From an operations research perspective, there are two major drawbacks of this common VBM approach. First, value driver trees are only explanatory frameworks and do not provide decision support. Second, risk implications are only covered indirectly omitting scenario-based information to derive robust plans. Conceptual frameworks for value-based performance (cf. Walters, 1999 and Lambert and Pohlen, 2001) and risk management (cf. Cavinato, 2004 and Oehmen et al., 2009) are widely discussed in the supply chain context. Lainez et al. (2009) and Hahn and Kuhn (2011) provide decision models for value-based performance optimization at the long-term and mid-term level of supply chain management. However, the authors cover risk implications only indirectly via risk-adjusted cost of capital and the management of supplier–customer relationships. Therefore, the aim of this paper is to develop a framework for integrated value-based performance and risk optimization with a primary focus on the mid-term level. Robust optimization methods are applied to account for the risk-averse attitude of corporate decision-makers and to immunize financial performance against the impact of imperfect information (cf. Mulvey et al., 1995 and Bai et al., 1997). The remainder of this paper is structured as follows: Section 2 provides a literature review on decision-oriented approaches to financial performance and risk management in supply chains as well as robust optimization methods. The conceptual framework for value-based performance and risk optimization is derived in Section 3. In Section 4, a corresponding decision model for the supply chain context is presented. Multiple aspects of robustness and general implications of the framework are highlighted in Section 5 using a case-oriented example. Section 6 concludes the paper with a summary of the findings and an outlook for further research.

In this paper, we present a holistic framework for value-based performance and risk management in supply chains. Value drivers of mid-term supply chain management are highlighted and performance levers as well as sources of operational risk are examined from a decision-oriented perspective. The concept of Economic Value Added (EVA) as a prevalent performance indicator is applied to manage upside potential and downside risk in terms of value creation. Different criteria of robustness are integrated into a comprehensive approach for robust optimization. The optimization-based framework presented in this paper provides real decision support for value-based management as opposed to common explanatory approaches. A direct approach to risk management is pursued utilizing scenario-based information instead of expected values and risk-adjusted cost of capital. Different implications of a robust optimization approach are derived using a case-oriented example. Solution and objective robustness are conflicting criteria and depend on the risk preference of the decision-maker. However, the risk preference should be calculated in retrospect by balancing upside potential against downside risk for different values of the risk preference parameter. Information robust results can be derived even for comparably small scenario sets which allows the decision-maker to focus on the most relevant scenarios for data gathering and scenario generation. Regarding further research, there are multiple opportunities to extend the presented framework. Integrated pricing could be covered to improve operating profit margin as one major value driver. The influence of an ‘incorrect’ forecasting model for scenario data could be analyzed to derive implications for data preparation and model selection. Moreover, the decision model could be extended towards a hierarchical planning framework by introducing a short-term planning level below mid-term S&OP. This would allow investigation into the effects of detailed lot-sizing and scheduling as well as short-term financial planning in the supply chain. Mid-term investment and financial planning covering purchase and sale of technical equipment as well as further means of funding could be considered to enhance asset utilization as a key value driver. (Dis-)Investment and capacity adjustment planning promise to be interesting aspects since they enable performance gains due to business growth but at the same time imply significant risk due to limited reversibility.