系统管理学报

系统管理学报 ›› 2020, Vol. 29 ›› Issue (3): 590-600.DOI: 10.3969/j.issn.1005-2542.2020.03.019

• 运营管理 • 上一篇    下一篇

互联网平台下基于众包的供应链订货策略

黎继子1, 2，张念2，刘春玲2，汪忠瑞3   

  1. 1.南昌大学 管理学院，南昌 330031； 2.武汉纺织大学 供应链系统研究中心，武汉 430071； 3.华中科技大学 管理学院，武汉 430070
  • 出版日期:2020-05-29 发布日期:2020-07-09
  • 作者简介:黎继子（1970—），男，博士，教授，博士生导师。研究方向为供应链与运营管理。
  • 基金资助:

    国家自然科学基金资助项目（71872076，71472143）；教育部人文社科项目（15YJA630035）

Ordering Policy for Crowdsourcing Supply Chain on Internet Platform

LI Jizi 1, 2, ZHANG Nian 2, LIU Chunling 2, WANG Zhongrui 3   

  1. 1. School of Management, Nanchang University, Nanchang 330031, China;  2. Research Center of Supply Chain System, Wuhan Textile University, Wuhan 430073, China;  3. School of Management, Huazhong University of Science and Technology, Wuhan 430070, China
  • Online:2020-05-29 Published:2020-07-09
  • Supported by:

    黎继子（1970—），男，博士，教授，博士生导师。研究方向为供应链与运营管理。E-mail：lijison.csc@qq.com

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摘要：

众包供应链(CSC)作为一种以“互联网+”为背景的新型供应链，正成为当今研究的热点。基于互联网平台，将众包设计环节嵌入到供应链中，建立了整个众包供应链多周期基本订货模型。在此基础上，考虑到互联网众包平台具有开放式的多频快速交付设计的特征，引入资金周转时间价值影响因子，将多周期模型扩展到基于快速周转的时间价值模型，并对模型进行了分析；考虑到众包供应链实施和落地的前提条件，进一步探讨众包供应链制造商对众包设计产品的生产规模和零售商风险规避的影响，提出了基于风险厌恶的最小生产批量的供应链订货策略，并对该策略进行了优化和对比分析，得出在互联网平台下基于众包设计的各个模型策略最优订货量，以及对制造商和零售商有效协调的实施条件。通过多重激励契约设计，实现了互联网平台下对众包设计者、制造商和零售商之间多重协调，使得众包供应链达到Pareto最优。最后，通过算例对模型和策略对比分析和敏感性分析，进一步证实了该新型策略的可行性和实用价值。

关键词: 互联网平台, 众包供应链, 订货策略, 激励, 回购契约

Abstract:

Crowdsourcing supply chain (CSC), as a new supply chain with the background of “Internet plus”, is becoming a hot topic of research nowadays. This paper first embedded the design link of crowdsourcing into supply chain, and established a multi-period basic ordering model for the whole crowdsourcing supply chain, based on which, considering that the Internet crowdsourcing platform had the features of open multi-frequency and rapid delivery design, it introduced the rapid turnover factor with time value of capital into extended model. In addition, taking into account the pre-requirement for implementing crowdsourcing supply chain, it further developed and discussed the ordering policy under minimum production quantity and risk aversion in the crowdsourcing supply chain. Moreover, through the optimization and comparative analysis of the models, it obtained the optimal order quantity and implementing conditions for manufacturers and retailers. Furthermore, it designed the multiple incentive contract to achieve Pareto optimality. Finally, it conducted a numerical study which confirmed the feasibility and efficiency of the ordering policy and contract.

Key words: internet platform, crowdsourcing supply chain, ordering policy, incentive, buy-back contract

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