双向部分透明供应链的大数据投资决策与激励

针对供应链中需求大数据的分散投资与决策激励问题，考虑单个供应商和制造商均能通过大数据投资预测需求，且彼此可共享部分大数据。建立基于上下游间双向部分透明的大数据投资决策模型，揭示双向透明对于大数据应用价值与投资激励的影响，并设计契约合作机制解决大数据投资的激励失调问题。研究发现：双向透明总是对供应商有利，当逆向透明度低时，正向部分透明可能对制造商最有利，当正向透明度低时，促进双向透明可能对所有参与者均有利；正向透明而逆向不透明有利于提高大数据投资的可行性；制造商只存在大数据投资激励不足，而供应商在双向透明度低时还存在大数据投资激励过度；投资补偿契约能协调各参与者的大数据投资激励，且可提升系统投资利润5-49%。

Incentives for Big Data Investment in Supply Chains with Two-way Partial Transparency

The rapid growth of big data has provided tremendous opportunities for enterprises to understand the market and make decisions better. However, taking the significant cost into account, practitioners have also raised questions about the financial returns on the big-data investment. Inter-enterprise sharing of big data may be an effective approach to alleviate the big-data investment pressure by avoiding inefficiently redundant development of big-data resources, nevertheless, partial sharing or non-sharing is more common. In this context, the decision and incentive alignment issues arising from big-data investment in a supply chain that consists of one upstream supplier and one downstream manufacturer are investigated. Both members can invest in big data to obtain accurate demand forecasts. The forecasts can be shared partially not only from the supplier to the manufacturer (i.e., top-down transparency), but also from the manufacturer to the supplier (i.e., bottom-up transparency). As the two-way partial transparency is exogenously given, a theoretical analysis model is established to solve the decision problems of big-data investment and then the impacts of the two-way transparency on the utilization value and investment incentives of big data are analyzed.The results indicate that the supplier always benefits from two-way transparency, whereas the manufacturer can benefit not only from top-down partial transparency with lower bottom-up transparency, but also from bottom-up complete transparency with lower top-down transparency. Therefore, both members can benefit from two-way increased transparency when the top-down transparency is sufficiently low. In addition, it is most conducive to the feasibility of big-data investment with top-down complete transparency and bottom-up non-transparency. Overinvestment never happens to the manufacturer, while it may happen to the supplier when both the top-down and bottom-up transparency is sufficiently low. To address the incentive alignment issues, a contract scheme based on investment compensation is proposed, which can achieve the optimal investment levels under the centralized investment setting and realize Pareto improvement. Finally, numerical experiments are conducted to obtain more managerial insights, and show that the return on investment can be improved by 5-49% under the contract scheme.In summary, the decision making and incentive mechanism for big-data investments of multiple enterprises under partial transparency are studied by employing the game theory. The findings in this paper can provide academic and practical insights for sustainable utilization of big data.