Lecture Topic:

Suppliers’ Purchasing Decision Based on Machine Learning and Financial Risk Control

Lecturer:

Youhua (Frank) Chen

Time:

December 27, 2019 (Friday) 14:30-16:30

Place:

Room 317, Main Building, Zhongguancun Campus

Organizer:

Graduate School, School of Management and Economics

Registration:

Log-in to WeChat enterprise of Beijing Institute of Technology— 第二课堂（The Second Lecture）— Choose No.306 in the Lecture Registration

【Introduction to the lecturer】

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.

【Lecture Information】

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.