讲座题目：基于机器学习和金融风险控制的供应商采购决策

报 告 人：Youhua (Frank) Chen

时 　 间：2019年12月27日(周五)14:30-16:30

地 　 点：中关村校区主楼317室

主办单位：研究生院、管理与经济学院

报名方式：登录北京理工大学微信企业号---第二课堂---课程报名中选择“【百家大讲堂】第306期：基于机器学习和金融风险控制的供应商采购决策”

【主讲人简介】

  Youhua (Frank) Chen，多伦多大学博士，现任香港城市大学管理科学系讲座教授及系主任。在2012年加入香港城市大学之前，Youhua (Frank) Chen教授曾在新加坡国立大学商学院（1997-2001）和香港中文大学系统工程与工程管理系（2001-2012）任职。Youhua (Frank) Chen教授的研究兴趣包括共享经济、医疗健康管理、供应链建模和库存系统分析,在OR、MS、POM、M&SOM、NRL等运作管理领域国际顶级期刊发表多篇学术论文，例如代表作“Quantifying the bullwhip effect in a simple supply chain: The impact of forecasting, lead times, and information”发表后已经被引用2200余篇次，在供应链管理领域名列前茅。

Prof. Youhua (Frank) Chen is Chair Professor and Head of Management Sciences at City University of Hong Kong. He holds a bachelor’s degree in Engineering, master’s degree in Economics, and doctoral degree in Management from Tsinghua University, the University of Waterloo, and the University of Toronto, respectively. Before joining National University of Singapore in 1997, he took a post-doctoral fellow position at Northwestern University. After 11 years of teaching at the Chinese University of Hong Kong (CUHK), Prof. Chen joined CityU in 2012. Courses which he taught include Operations Management, Supply Chain Management, Logistics, and Advanced Manufacturing Management. He was also actively involved in executive teaching (EDP and EMBA). Prof. Chen has also been involved in consulting projects in the area of supply chain management and logistics. His current research projects span from healthcare operations management, logistics-supply chain management, to data-driven operations. He was project coordinators of two major projects which completed recently and has been principle investigator of more than 10 earmarked research grants.  
       

【讲座信息】

  许多零售商会定期推出短生命周期的新产品。不同于现有产品能够根据历史销售数据来预测未来销售，新产品没有这样的数据。取而代之的是，一家公司过去可能一直在销售类似的产品，并很好地保存了销售数据。除了需求/销售数据外，数据记录还可能包含有关产品属性（特征）的丰富信息，如零售价格、设计风格和季节，即所谓的需求协变量信息。在本研究中，我们试图通过使用协变量信息将一个新产品与历史上销售的“类似”产品联系起来。采用权重来度量新产品和历史产品之间的相似性，将机器学习方法（如k近邻法、分类回归树法和随机森林法）应用到数据中来估计权重值。类似历史产品的现实需求及其对应的权重，连同来自其他类似产品的需求，被用来近似估计期望利润和其他（按条件）需求分布的数量。该方法应用于风险规避企业在推出新产品前确定最优订货量。风险规避要求企业获得一个高置信度的利润目标，该目标可以表述为风险价值约束。除了设计有效的解决方案外，我们还证明了所提出的近似估计方法是渐近最优的，即使是使用依赖于风险价值约束的样本。我们还将使用实际中的数据来验证我们的模型和方法，并提出关键的管理启示。

Many retailers regularly introduce new, short life-cycle products. Unlike existing products whose historical sales data may be an indicator of future sales, a new product does not have such data. Instead, a firm may have been selling similar products in the past and keeps a good record of them. In addition to demand/sales figures, the data record may contain rich information about the attributes (features) of the products, such as retail price, design style, and season, the so-called covariate information to demand. In this project we attempt to link a new product, by using covariate information, to “similar” products that were sold historically. Weights are used to measure similarities between the new product and historical products, and the values of those weights are estimated by employing machine learning methods  such as   k-nearest neighbours, classification and regression tree, and random forests, to the data.  Then, the pair of the realized demand of a similar historical product and its associated weight, together with those from other similar products, are utilised to approximate the expected profit and other quantities which take on the (conditional) demand distribution. This approach is applied to determine the optimal order quantities before a risk-averse firm launches a new product. Risk aversion requires the firm to attain a profit target with high confidence, which can be formulated as a value-at-risk (VaR) constraint. Besides devising efficient solutions, we also prove the proposed approximation to be asymptotically optimal even with the sample-dependent approximation for the VaR constraint. We will also use real-world data to verify our models and methods and present key managerial insights.