Informs Annual Meeting 2017

WB45

INFORMS Houston – 2017

2 - Antecedents of Financial Recovery Effectiveness from Floods: A Structural Econometric Analysis of Flooding in Thailand Niratcha Tungtisanont, Clemson University, 100 Sirrine Hall, Management Dept, Clemson, SC, 29634, United States, ntungti@g.clemson.edu, Aleda Roth, Bernardo F. Quiroga Using a sample survey of approximately 34,000 households in Thailand with data pre-, during- and post-flood disaster in 2011. Taking a service operations lens, our study aims to identify where and how in the flood cycle can various stakeholders—individuals, communities and governments act to increase the likelihood of a successful service recovery. More specifically, this research asks the question of what type of strategic actions can policy makers take in order to better allocate precious resources in pre-, during-, and post-flood phases to improve the overall effectiveness of the recovery process? 3 - Volunteer Management in Charity Storehouses Gloria Urrea, Universita della Svizzera Italiana, Lugano, Switzerland, gloria.urrea@usi.ch, Alfonso J. Pedraza-Martinez, Maria Besiou We study volunteer management at a large faith-based organization that operates a steady aid delivery system. The whole supply chain operates exclusively with volunteers (from supply to delivery). We study the preparation of beneficiaries’ orders by volunteers in the storehouse as a function of volunteer experience and warehouse congestion. Using empirical data, we build a simulation model to explore the drivers of on-time order fulfillment at the storehouse level. 4 - Proactive and Reactive Supply Management for Emergency Relief Period: Effects of Demand and Supply Uncertainties and Budget Limit Mahyar Eftekhar, Arizona State University, BA 433, Main Campus, P.O. Box 874706, Tempe, AZ, 85287, United States, eftekhar@asu.edu, Jeannette Song, Scott Webster To fulfill beneficiaries’ demands, humanitarian organizations should design a cost- efficient and time-effective procurement policy. We consider and analyze two common supply management policies: pre-positioning and local-purchasing. Our analysis takes demand, supply, and budget uncertainties into account. 360C Operations Management/Marketing Interface Contributed Session Chair: Tuhin Sengupta, Indian Institute of Management-Indore, Indore, India, f13tuhins@iimidr.ac.in 1 - Aligning Price Adjustment Protection with Return Policy Moutaz J.Khouja, University of North Carolina-Charlotte, BISOM.Department, College of Business, Charlotte, NC, 28223, United States, mjkhouja@uncc.edu, Haya Ajjan, Xin Liu Retailers provide a return policy because it reduces the risk customers might perceive in purchasing. Retailers also provide price adjustment protection policy under which the retailer promises to refund consumers the difference between the full price and the discount price, should the product’s price drop within a specified duration of time after purchase. We identify the optimal duration of the two policies. We also examine their effect on optimal inventory and pricing decisions. 2 - Self Price Beating in a Market with Preference Interdependence and Uncertainty Ting Luo, California State University Fullerton, 800 N State College Blvd, Fullerton, CA, 92831, United States, ting.luo@fullerton.edu, Lijia Shi, Upender Subramanian Self price beating pricing strategy promises the early buyers that if the seller lowers the price in the later period, an ex post price refund that is more than the price difference will be refunded to them. We study self price beating as a pricing policy when there is market externality and uncertainty. We find this strategy blends advantages of both price commitment and no price commitment, and it produces the highest total profit as well. 3 - Designing Product Rollovers and Managing Style Obsolescence Esma Koca, PhD Candidate, Imperial College Business School, PhD Students Postbox, Tanaka Building, Level 2, Imperial College Business School, London, SW2 7AZ, United Kingdom, e.koca13@imperial.ac.uk, Tommaso Valletti, Wolfram Wiesemann When releasing a new version of a durable product, a firm should consider whether to convince its consumer base to upgrade while attracting new consumers. To this end, the firm designs a rollover strategy, which selects the price of the new product, as well as whether to continue the sales of the old product at a discount (dual rollover) or not (solo rollover). We argue that the effectiveness of a rollover crucially depends on the consumers’ perception of the obsolescence of the old version, and that the firm can influence obsolescence by a prudent timing of the new product launch. We explore the impact of the timing decision in solo and dual rollovers in markets consisting of myopic and strategic consumers. WB44

4 - Product Quality and Incentives in Handicraft Supply Chain Tuhin Sengupta, Indian Institute of Management-Indore, Rau-Pithampur Road, Indore, 453331, India, f13tuhins@iimidr.ac.in In this article, we study how retailers, taking into account customer preferences, design its product to meet its objectives. We study how preferences of different customer segments and the degree of customer knowledge about the handicraft products drive the quality and pricing decisions of a retailer. Furthermore, we examine the implications for a manufacturer in the cooperative setting against the profit maximizing private setting. 360D Retail Management Contributed Session Chair: Ameera Ibrahim, Saint Mary’s College of California, Moraga, CA, United States, ai7@stmarys-ca.edu 1 - Markdown Optimization using Randomized Decomposition Approach Andrew Vakhutinsky, Consulting Member of Technical Staff, Oracle, 35 Network Dr., Burlington, MA, 01803, United States, andrew.vakhutinsky@oracle.com, Kresimir Mihic, Su-Ming Wu Despite the fact that markdown optimization problem has been known for more than twenty years, it still presents certain challenges for the real-life instances due to non-linearity of the demand function and complex side constraints. We present a new approach to solving MDO called randomized decomposition (RD). RD solver randomly selects subsets of the decision variables and finds an optimal solution for the variables in each subset, keeping all other variables fixed. We describe the results of our computational experiments comparing different solution approaches including MILP-based formulations based on the linearization of the demand function. 2 - Multi-location Multi-product Substitution with Strategic Consumers Chengfan Hou, Tsinghua University, Beijing, 100084, China, houcf16@mails.tsinghua.edu.cn, Tianhu Deng, Qiankai Qing, Jianghua Zhang, Simin Huang In this paper, we explicitly model when does a customer prefer to purchase at another store an item that is out of stock at a given store, rather than substituting it with a different item at the same store. From the perspective of a retail chain owner, we found the following: First, under a constant service level, the effective demand rate is higher when a product’s inventory is spread over more stores. Second, this relative increment increases with increase in cost of visiting a store and consumer valuation of the substitute product, but decreases with service level and consumer valuation of the primary product. Third, the results still hold when extended to an exogenous model. 3 - A Randomized Pricing Strategy with Price Guarantee Jianghua Wu, Professor, Renmin University of China, 59 Zhongguancun Street, Beijing, 100872, China, jwu@ruc.edu.cn, Xian Wu, Yunjie Wang This study proposes a randomized pricing strategy with posterior price guarantee for online retailers. We assume there are two segments of consumers with different valuations to the product. The price variation is set as a Markov process in which theprice randomly switches between high level and low level. Given the price guarantee, once the retailer offers promotion within the guarantee period, the retailer has to pay the price difference back to those who buy at regular price and claim the guarantee. This price strategy encourages consumers to buy instantly at the high price instead of waiting for the low price. we derive the optimal prices promotion probability, and length of guarantee period. 4 - Optimal Bundle-size Pricing Ameera Ibrahim, Assistant Professor, Saint Mary’s College of California, 1928 Saint Mary’s Road, Moraga, CA, 94575, United States, ai7@stmarys-ca.edu We analyze the bundle-size pricing problem in retail. Consumers can choose N goods, from a larger pool of J goods, for a fixed price that depends on the number of goods in the bundle. With the objective of maximizing the total profit, the retailer decides on the optimal assortment of bundles and their prices. We propose a mixed-integer nonlinear model that is then reformulated into a mixed-integer linear model and its computational tractability is demonstrated. Managerial insights are derived based on the computational results. WB45

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