講座題目：基於機器學習和金融風險控製的供應商采購決策

報 告 人：Youhua (Frank) Chen

時 　 間🧑🏻‍🦯：2019年12月27日(周五)14:30-16:30

地 　 點：中關村校區主樓317室

主辦單位🛢🧛🏼‍♀️：意昂平台🏌️‍♂️、管理與經濟學院

報名方式⚀：登錄意昂官网微信企業號---第二課堂---課程報名中選擇“【百家大講堂】第306期：基於機器學習和金融風險控製的供應商采購決策”

【主講人簡介】

  Youhua (Frank) Chen👩🏿，多倫多大學博士🧥😏，現任香港城市大學管理科學系講座教授及系主任🥬。在2012年加入香港城市大學之前，Youhua (Frank) Chen教授曾在新加坡國立大學商學院（1997-2001）和香港中文大學系統工程與工程管理系（2001-2012）任職。Youhua (Frank) Chen教授的研究興趣包括共享經濟🤾🏿‍♀️、醫療健康管理、供應鏈建模和庫存系統分析,在OR、MS、POM👩🏻‍🌾、M&SOM👩🏻‍🦳、NRL等運作管理領域國際頂級期刊發表多篇學術論文，例如代表作“Quantifying the bullwhip effect in a simple supply chain: The impact of forecasting, lead times, and information”發表後已經被引用2200余篇次，在供應鏈管理領域名列前茅👃🏻。

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.  
       

【講座信息】

  許多零售商會定期推出短生命周期的新產品。不同於現有產品能夠根據歷史銷售數據來預測未來銷售👎🏼，新產品沒有這樣的數據。取而代之的是，一家公司過去可能一直在銷售類似的產品🙆🏼‍♀️，並很好地保存了銷售數據。除了需求/銷售數據外，數據記錄還可能包含有關產品屬性（特征）的豐富信息，如零售價格、設計風格和季節🪀，即所謂的需求協變量信息。在本研究中👎🏿🚴‍♂️，我們試圖通過使用協變量信息將一個新產品與歷史上銷售的“類似”產品聯系起來。采用權重來度量新產品和歷史產品之間的相似性🖖🏿，將機器學習方法（如k近鄰法🫖🤶🏻、分類回歸樹法和隨機森林法）應用到數據中來估計權重值🖥。類似歷史產品的現實需求及其對應的權重，連同來自其他類似產品的需求，被用來近似估計期望利潤和其他（按條件）需求分布的數量🫦。該方法應用於風險規避企業在推出新產品前確定最優訂貨量⚇。風險規避要求企業獲得一個高置信度的利潤目標🤌🏿，該目標可以表述為風險價值約束👳🏿。除了設計有效的解決方案外，我們還證明了所提出的近似估計方法是漸近最優的，即使是使用依賴於風險價值約束的樣本。我們還將使用實際中的數據來驗證我們的模型和方法，並提出關鍵的管理啟示。

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.