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中国管理科学 ›› 2019, Vol. 27 ›› Issue (2): 107-118.doi: 10.16381/j.cnki.issn1003-207x.2019.02.011

• 论文 • 上一篇    下一篇

政府奖惩下闭环供应链中需求预测信息分享研究

张盼1,2   

  1. 1. 南昌大学中国中部经济社会发展研究中心, 江西 南昌 330031;
    2. 南昌大学经济管理学院, 江西 南昌 330031
  • 收稿日期:2017-07-30 修回日期:2017-12-13 出版日期:2019-02-20 发布日期:2019-04-24
  • 通讯作者: 张盼(1989-),男(汉族),湖北英山人,南昌大学经济管理学院讲师,博士,研究方向:物流与供应链管理,E-mail:zhangpanyjs@163.com. E-mail:zhangpanyjs@163.com
  • 基金资助:

    江西省高校人文社会科学研究项目(GL18240);江西省社会科学规划资助项目(18GL33)

Demand Forecast Sharing in a Closed-loop Supply Chain under the Government's Reward-penalty Mechanism

ZHANG Pan1,2   

  1. 1. Research Center of the Central China for Economic and Social Development, Nanchang University, Nanchang 330031, China;
    2. School of Economics and Management, Nanchang University, Nanchang 330031, China
  • Received:2017-07-30 Revised:2017-12-13 Online:2019-02-20 Published:2019-04-24

摘要: 考虑政府奖惩机制,在制造商直接回收的闭环供应链中,当市场不确定且制造商和零售商都能预测需求时,为研究零售商的需求预测信息分享问题,本文构建一个Stackelberg博弈模型,分别求得零售商信息分享和不分享情形下的均衡结果,探讨了需求预测信息精度对供应链成员利润及信息分享价值的影响,并研究了供应链均衡的信息分享策略。研究发现,需求预测精度的提高在大多数情形下会使供应链成员都受益。当制造商回收效率较高时,自愿分享需求信息是一个均衡;当制造商回收效率较低时,信息不分享是一个均衡;当制造商回收效率处于中等水平时,通过设计一个讨价还价机制,可以促使信息分享是一个均衡。此外,政府奖惩力度也会影响均衡的信息分享策略。

关键词: 闭环供应链, 奖惩机制, 需求预测, 信息分享

Abstract: Driven by the government's mechanism of reward-penalty, many manufacturers are engaging in collecting and remanufacturing operations. For the members of the closed-loop supply chain with the manufacturer collecting, they can forecast the uncertain market demand through using information technology. Generally, the forecast information is the private information of the retailer, which results in demand forecast information asymmetry in the closed-loop supply chain with the manufacturer collecting. In order to solve this problem, the demand forecast sharing decisions of the retailer is studied. Specifically, the following questions are mainly investigated:When both the manufacturer and the retailer can forecast, whether the retailer shares the forecast information voluntarily? If impossible, how to design a mechanism to induce it to share information? How does the government's mechanism of reward-penalty affect the equilibrium of information sharing? And how does the forecast accuracy affect the supply chain members' profits and the value of information sharing? A Stackelberg model between the manufacturer and the retailer is constructed. The manufacturer and the retailer first negotiate on information sharing agreement through a bargaining mechanism. And then the manufacturer acts as a Stackelberg leader and determines the wholesale price and the collection rate. As a follower, the retailer determines the retail price. This paper solves the multistage game by using a standard backward induction technique. In detail, the equilibrium decisions of the manufacturer and the retailer are first solved under the case of information sharing and non-information sharing, and then the firms' ex ante profits of both cases are computed, thereby obtaining the value of information sharing. Based on these results, this paper analyses the impact of demand forecast accuracy on the supply chain members' profits and the value of information sharing, and the values of information sharing and equilibrium information sharing decisions are investigated. The results show that in most cases, both the supply chain members can benefit from the precise demand forecasts. Besides, when the manufacturer's collection efficiency is high, voluntary information sharing is a unique equilibrium. When the efficiency is low, non-information sharing is a unique equilibrium. When efficiency is medium, the equilibrium of information sharing can be realized through a bargaining mechanism. Furthermore, the government's level of reward-penalty affects the equilibrium information sharing decisions. The research provides useful managerial insights to the government and supply chain managers in terms of sharing information, and collection and remanufacturing operations.

Key words: closed-loop supply chain, mechanism of reward-penalty, demand forecast, information sharing

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