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运营与物流管理系、信息管理与决策科学系

学 术 报 告

报告时间：2020年7月30日（星期四）上午8:30-12:00

报告地点：腾讯会议

会议链接：https://meeting.tencent.com/s/CiZBIQx86umy

会议 ID：817 261 463

主 持 人：邱若臻 教授

报告A：Categorical Variables in Linear Models

主 讲 人：美国德州大学圣安东尼奥分校 孙明和 教授

报告B：Single sourcing or dual Sourcing? Decision in the presence of the potential entry of counterfeiters

主 讲 人：新加坡管理大学 方心 教授

报告C：Value of consumer scanning technology for supply chain information learning

主 讲 人：美国威斯康星大学米尔沃基分校 岳晓航 教授

报告A摘要：As business firms pay more attention to data and data driven decision making, more data are collected through different techniques and datasets can become very large. Almost all datasets include categorical data in nominal scale. Researchers and practitioners usually use these categorical data when building linear models. Different types of linear models and different ways of constructing linear models are briefly reviewed. Characteristics of categorical variables are discussed and examples of categorical variables in linear models are provided. Correct and incorrect approaches for coding categorical variables in linear models are analyzed. Indicator variables used to represent categorical variables are demonstrated in a regression model through an example. Limitations of indicator variables are also discussed

孙明和 简介：美国佐治亚大学管理科学博士，美国德州大学圣安东尼奥分校商学院终身正教授。主要研究方向：多目标决策、运作管理、组合优化、神经网络等。曾在《Management Science》、《Operation Research》、《Decision Sciences》、《INFORMS Journal on Computing》、《Transportation Science》、《European Journal of Operational Research》等国际著名期刊上发表论文40余篇。此外，孙明和教授还是国际学术期刊《International Journal of Strategic Decision Sciences》和《Information Science and Technology》的副主编，《International Journal of Information Technology & Decision Making》和《ISRN Applied Mathematics》编委，在相关研究领域及英文论文写作等方面具有丰富经验。

报告B摘要：We analyze the sourcing decision of a brand-name firm facing two suppliers: a licit domestic supplier and an oversea supplier who potentially becomes a counterfeiter if the brand-name firm fails to engage it. We obtain the optimal sourcing strategy of the brand-name firm and identify the conditions under which the brand-name firm can prevent the overseas supplier to become a counterfeiter. Specifically, if the penalty from law enforcement to the counterfeiter is high, the brand-name firm engages the overseas supplier by single sourcing from it. If the penalty is not high and the difference in production costs between two suppliers is low, the brand-name firm engages the overseas supplier by dual sourcing. With high perceived quality of the counterfeit product, if both the penalty and the difference in production costs are low, the brand-name firm fails to engage the overseas supplier by single sourcing from the domestic supplier. We also examine how factors, such as the penalty from law enforcement and the quality discount factor, affect firms’ profits, consumer surplus and social surplus.

方心 简介：新加坡管理大学李光前商学院运作管理助理教授。于2008年复旦大学获得信息管理与信息系统专业本科学位，又于2014年从美国卡耐基梅隆大学获得运营管理博士学位。主要从事全球供应链网络中的竞争和合作等问题研究。他的研究把合作博弈、非合作博弈以及社会关系网络理论应用在分销系统、供应链风险与责任以及电子商务领域。他的研究成果发表在《Operations Research》、《Manufacturing and Service Operations Management》《Production and Operations Management》国际顶尖期刊上。

报告C摘要：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.

岳晓航 简介：美国威斯康星大学米尔沃基分校（The University of Wisconsin- Milwaukee）教授，目前是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，其主要从事供应链与物流管理、生产与市场管理和工业制造系统管理等领域的研究工作，在Operations Research、Production and Operations Management、Naval Research Logistics、Decision Sciences、IIE Transactions、IEEE Transactions、European Journal of Operational Research、Omega、International Journal of Production Economics、Journal of Business Research等国际顶级/权威期刊上发表论文40余篇。现担任国际顶尖期刊POM的高级编辑，OR、MSOM、EJOR、JOM等多个国际知名期刊的审稿人。

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