题目：Value of consumer scanning technology for supply chain information learning营销调研技术在供应链信息学习中的价值
演讲人：岳晓航，美国威斯康星大学教授
主持人：镇璐，上海大学管理学院教授
时间：2021年3月18日，上午9:00
腾讯会议ID： 410 961 610 （密码：123456）
主办单位：上海大学管理学院、上海大学管理学院青年教师联谊会
演讲人简介：
岳晓航教授，国际知名专家，美国威斯康星大学密尔沃基分校(The University of Wisconsin- Milwaukee)商学院教授，美国供应链管理协会、管理科学及运筹学协会和运营管理协会会员；主要从事供应链（金融）与物流管理、生产与市场管理和工业制造系统管理的研究工作, 担任运营管理领域国际顶级期刊POM的编委。
岳晓航教授在包括Operations Research、Production and Operations Management、Naval Research Logistics、IIE Transactions、IEEE Transactions、Decisions Science、EJOR等国际权威主流期刊上以一作/通讯身份发表论文数十篇。
演讲内容简介：
Recently, Chinese firms have begun to deploy popular mobile apps (e.g., WeChat) into their supply chain practices to improve demand visibility. These efforts rely on consumers to scan the products they purchase using these apps, which we refer to as consumer scanning technology (CST). CST can be an alternative to conventional inter-organizational information technology (IOIT) that relies on collaboration between supply chain firms. We develop a theoretical model to examine the value of CST to learn supply chain (demand) information. In this model, an upstream supplier bypasses the downstream retailer and employs CST to directly collect end scan information from consumers who are incentivized with a reward for participating. The theoretical analysis of the model demonstrates both operational and strategic values of CST. On the operational level, noticing that the scan information gathered by CST is a censored version of the true information, we develop a simple and effective approach for the supplier to learn the true information from the censored one, and then investigate the learning efficiency of our approach and the optimal reward decisions. On the strategic level, we examine the equilibrium choice of IOIT and CST within supply chains and investigate their interplay. Contrary to conventional view, we find that the availability of CST may expand (instead of suppressing) the use of IOIT within supply chains. Using real-life data from a manufacturer that has implemented a CST program for learning demand information, we show that the value of CST can be substantial.
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