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Make-to-order supply chain coordination through option contract with random yields and overconfidence

机译:通过随机收益率和过度交付的选项合同进行按订单供应链协调

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This paper investigates a two-echelon supply chain with a supplier and a manufacturer with yield uncertainty. We assume that the manufacturer is overconfident about its yield and examine how this could impact on the performance for each company and the supply chain. We propose option contracts to be used to coordinate the ordering and production across the supply chain. A wholesale price contract without overconfidence (i.e., with complete rationality) about the manufacturer's yield is first developed as a benchmark. Based on that, we analyze the impact of overconfidence on the decisions and the expected profits for each player in the supply chain under the wholesale price contract. This setting is then applied to the case of option contracts. We derive analytical results on the decisions and the expected profits, and propose conditions for the option price and the option execution price to achieve supply chain coordination. Overall we find that overconfidence has an adverse impact on the performance of both companies. Their expected profits will decline as the level of overconfidence goes up. Option contracts can improve the performance for both companies but cannot completely eliminate the impact of overconfidence. This paper therefore could be used to guide the coordination and cooperation of companies along a supply chain when there exists yield uncertainty and overconfidence. Our propositions are validated through numerical experiments on a hypothetical case and supported by extensive parameter sensitivity analyses.
机译:本文调查了一个双梯队供应链,供应商和制造商,具有不确定性的制造商。我们假设制造商对其产量提供过度自信,并研究如何影响每家公司和供应链的绩效。我们提出了选择合同,以便在供应链中协调订购和生产。批发价格合同没有过度自信(即,完全合理)关于制造商的产量是首先作为基准开发的。基于此,我们分析了在批发价格合约下供应链中每位参与者的决定和预期利润的影响。然后将此设置应用于选项合同的情况。我们派生分析结果对决策和预期的利润,并提出了期权价格的条件和期权执行价格,以实现供应链协调。总的来说,我们发现过度自信地对两家公司的表现产生不利影响。随着过度交通水平上涨,他们的预期利润将下降。选项合同可以提高两家公司的表现,但不能完全消除过度自信的影响。因此,本文可用于指导当存在不确定性和过度交付时沿着供应链的公司协调与合作。我们的命题通过对假设案例的数值实验进行验证,并通过广泛的参数敏感性分析支持。

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