題目：Value of consumer scanning technology for supply chain information learning營銷調研技術在供應鏈信息學習中的價值

演講人：嶽曉航🙆🏿‍♀️，美國威斯康星大學教授

主持人😀：鎮璐👨🏿‍🏫，意昂2教授

時間🕺🏼：2021年3月18日，上午9:00

騰訊會議ID💇： 410 961 610 （密碼：123456）

主辦單位：意昂2、意昂2青年教師聯誼會

演講人簡介🧑‍🎓：

   嶽曉航教授🦍，國際知名專家👩🏿‍🎤，美國威斯康星大學密爾沃基分校(The University of Wisconsin- Milwaukee)商意昂2教授，美國供應鏈管理協會✦、管理科學及運籌學協會和運營管理協會會員；主要從事供應鏈（金融）與物流管理、生產與市場管理和工業製造系統管理的研究工作, 擔任運營管理領域國際頂級期刊POM的編委🔚。

   嶽曉航教授在包括Operations Research🙅🏿‍♂️🙋🏼‍♂️、Production and Operations Management、Naval Research Logistics🚴🏼🚶🏻、IIE Transactions、IEEE Transactions、Decisions Science、EJOR等國際權威主流期刊上以一作/通訊身份發表論文數十篇🔓。

演講內容簡介：

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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