文章基本信息

标题：Trust-Embedded Information Sharing among One Agent and Two Retailers in an Order Recommendation System
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作者：Fu, Xiao ; Han, Guanghua
期刊名称：Sustainability
印刷版ISSN：2071-1050
出版年度：2017
卷号：9
期号：5
页码：1-18
出版社：MDPI, Open Access Journal
摘要：Trust potentially affects the decision-makers’ behaviors and has a great influence on supply chain performances. We study the information sharing process considering trust in a two-tier supply chain with one upstream agent and two retailers, where the agent recommends ordered quantities (ROQ) to retailers and the retailer decides her/his ordered quantities according to the agent’s recommendation and self-collected information. There exist three types of information sharing patterns among the agent and two retailers, i.e., both retailers share their demand prediction (Pattern 1), one retailer shares her/his demand prediction (Pattern 2) and none of the retailers share their demand prediction (Pattern 3). Thus, we build corresponding mathematical models and analyze each party’s decision strategies in each pattern, respectively. The findings in this study show that sharing information can generally promote trust among enterprises in the entire supply chain and increase their profits in return. It is found that when the accuracies of the two retailers’ predicted demand differs, their behaviors of information sharing or not sharing significantly affect their expected profits. In Pattern 1 and Pattern 3, we find that retailers’ expected profits are negatively influenced by the agent’s accuracies of demand prediction. However, the retailer’s expected profits are positively linked to the agent’s accuracies of demand in Pattern 2. Consequently, we propose a series of strategies for retailers in different decision patterns after several simulation runs. In addition, we also find that the retailer whose prediction is less accurate can also gain more profits by un-sharing his/her demand prediction when the agent’s predict accuracy is between the two retailers.
关键词：supply chain; trust; information sharing; order recommendation