在由一个制造商和多个外部供应商构成的多产品、多阶段供应链中，将经济增加值（EVA）作为体现价值创造的绩效指标，并考虑决策者的经营风险偏好，采用已知概率的离散情景描述资本成本与需求的波动情况，利用鲁棒随机规划方法，建立了以价值创造为目标的供应链鲁棒优化模型。应用分析的结果表明，模型能够将供应链的绩效与风险管理结合起来，减少资本成本与需求不确定对目标值的影响，得到具有鲁棒性的最优解，而且越是风险厌恶型的决策者越会为了保持较低的经营风险而放弃较大的EVA值。同时，决策者可选择不同的权重系数侧重于解鲁棒或模型鲁棒，保证供应链运作的鲁棒性，实现价值创造的目标。
Creating shareholder value is commonly considered as the paramount business goal, which depends on the performance model and risk management. The core concept of Economic Value Added (EVA), as a prevalent metric of value-based performance, is capital cost that is uncertain due to the interest of debt and the hurdle rate changing, and thus there is much risk of cash flow in value creation. A major factor causing operational risks is the uncertainty of product demand. In addition, due to the operating leverage, fluctuation of demand may cause more change in operating income, and influence value creation directly. Recent papers show increasing interest in value-based supply chains management. However, these researches neglect the influence of risk preference of decision-makers on EVA, and ignore the corresponding risk from the uncertainty of capital cost.
In the multi product and multi stage supply chain that compose of one manufacturer and multiple external suppliers,economic value added (EVA) is regarded as a performance index to reflect the value creation,the management risk preferences of decision makers are considred,and the discrete scenario with known probabilities is adopted to describe the fluctuation of capital costs and demand and uses robust stochastic programming method to establish a robust optimization model of supply chain based on the goal of value creation. The objective function of the model includes three items, the first item is the expected EVA; the second is the product of risk preferences parameter of decision makers with business risk measured operating leverage coefficients; the third is feasible penalty function to punish deviation control constraints. The constraints consider comprehensively the financial constraints associated with supply chain capital flow and the non-financial constraints with supply chain logistics The decision goal hopes to get optimal solution (robust solution) which is feasible (model robust) under any uncertain situation when supply chain performance and risk are optimized comprehensively.
Application analysis results show that the model can integrate the performance and risk management, and reduce the influence of uncertain capital cost and demand, obtain the optimal solution with robustness. What is more, the decision maker with high risk averse prefer to give up the larger EVA to maintain a lower business risks. Meantime, the decision makers can choose different weight coefficients to focus on robust solution or model robust, ensure the robustness of supply chain operation and achieve the goal of value creation.
[1] Young S D, O'Byrne S F. EVA and value-based management:A practical guide to implementation[J].New York:McGraw-Hill Professional, 2001.
[2] Ritchie B, Brindley C. Supply chain risk management and performance[J].International Journal of Operations & Production Management, 2007,27(3):303-322.
[3] Ray R. Economic value added:Theory, evidence:A missing link[J].Review of Business, 2001, 22(1/2):66-70.
[4] Christopher M, Ryals L. Supply chain strategy:Its impact on shareholder value[J].International Journal of Logistics Management, 1999,10(1):1-10.
[5] Walters D. The implications of shareholder value planning and management for logistics decision making[J]. International Journal of Physical Distribution & Logistics Management, 1999, 29(4):240-258.
[6] Lambert D M, Pohlen T L. Supply chain metrics[J]. International Journal of Logistics Management, 2001, 12(1):1-20.
[7] Hahn G. J, Kuhn H. Optimising a value-based performance indicatior in mid-term sales and operations planning[J]. Journal of the Operational Research Society, 2011, 62(3):515-525.
[8] Pongsakdi A, Rangsunvigit P, Siemanond K, et al. Financial risk management in the planning refinery operations[J].International Journal of Production Economics,2006, 103(1):64-86.
[9] You Fengqi, Wassick J M, Grossmann I E. Risk management for a global supply chain planning under uncertainty:Model and algorithms[J].AIChE Journal, 2009, 55(4):931-946.
[10] Goh M, Lim J Y S, Meng Fanwen. A stochastic model for risk management in global supply chain networks[J]. European Journal of Operational Research, 2007, 182(1):164-173.
[11] Sodhi M S, Tang C S. Modeling supply-chain planning under demand uncertainty using stochastic programming:A survey motivated by asset-liability management[J]. International Journal of Production Economics,2009, 121(2):728-738.
[12] Scholl A. Robust Planung und optimierung:Grundlagen, konzepte und methoden-Experimentelle Untersuchungen[M].Heidelberg-Verlay:Springer,2001.
[13] Mulvey J M, Zenios S A. Robust optimization of large-scale systems[J].Operations Research, 1995, 43(2):264-281.
[14] Yu C S, Li H L. A robust optimization model for stochastic logistic problems[J]. International Journal of Production Economics,2000, 64(1-3):385-397.
[15] Leung S C H, Tsang S O S, Ng W L, et al. A robust optimization model for multi-site production planning in uncertainty environment[J]. European Journal of Operational Research, 2007, 181(1):224-238.
[16] Aghezzaf E. Capacity planning and warehouse location in supply chains with uncertain demands[J]. Journal of Operational Research Society, 2005, 56(4):453-462.
[17] 李春发,徐伟,朱丽. 考虑风险偏好的动态生产库存问题的鲁棒优化模型[J]. 运筹与管理, 2014, 23(5):48-54.
[18] Hahn G J, Kuhn H. Value-based performance and risk management in supply chains:A robust optimization approach[J].International Journal of Production Economics, 2012,139(1):135-144.
[19] Tang C S. Perspectives in supply chain risk management[J]. International Journal of Production Economics, 2006, 103(2):451-488.
[20] Christopher M, Towill D R. Developing market specific supply chain strategies[J]. International Journal of Physical Distribution and Logistics Management, 2002, 13(1):1-14.
