2015 Informs Annual Meeting

TA48

INFORMS Philadelphia – 2015

4 - Improving Food Bank Gleaning Operations: An Application in New York State Erkut Sonmez, Assistant Professor, Boston College, 140 Commonwealth Ave, Fulton Hall, Chestnut Hill, 02446, United States of America, erkut.sonmez@bc.edu, Miguel Gomez, Deishin Lee, Xiaoli Fan We develop a stochastic optimization model to help food banks to improve their gleaning operations. Gleaning refers to collecting food from what is left in the fields after harvest, and donating the goods to food banks or pantries that service food insecure individuals. TA48 48-Room 105A, CC Managing Contracts and Financial Flow in Supply Chain Sponsor: Manufacturing & Service Oper Mgmt/iFORM Sponsored Session Chair: Lingxiu Dong, Associate Professor, Washington University in St. Louis, One Brookings Drive, St. Louis, MO, 63132, United States of America, dong@wustl.edu 1 - Buyer Intermediation in Supplier Finance Tunay Tunca, ttunca@rhsmith.umd.edu, Weiming Zhu We analyze the role and the efficiency of buyer intermediation in supplier financing (BIF). We theoretically demonstrate that BIF can significantly improve the supply chain surplus over traditional financing. Using data from a large Chinese online retailer, we estimate model parameters, empirically verify the theory, and predict efficiency gains. 2 - Financial Pooling in Supply Chains S. Alex Yang, Assistant Professor, London Business School, Sussex Place, London, United Kingdom, sayang@london.edu, Qu Qian, Ming Hu Trade credit pools liquidity between suppliers and retailers. Due to this pooling effect, even if the supplier’s cost of capital is higher, the retailer may still demand for trade credit. Supply chain finance increases the efficiency of this pooling effect, and hence reduces the overall chain financing cost. 3 - Trade Credit and Supplier Competition Jiri Chod, Boston College, Carroll School of Management, Chestnut Hill, MA, jiri.chod@bc.edu, S. Alex Yang, Evgeny Lyandres We study the effect of competition among suppliers on their willingness to provide trade credit. Providing trade credit to a financially constrained buyer allows this buyer to reallocate his cash budget to purchasing from competing suppliers. Thus, relaxing the buyer’s financial constraint may backfire at the supplier who provides financing. This is a possible explanation of the empirical regularity that firms selling differentiated products tend to offer more trade credit. 4 - Push, Pull, and Delayed Payment Contracts when a Manufacturer Expands His Product Line Xiaomeng Guo, PhD Candidate, Olin Business School, Washington University in St. Louis, Campus Box 1156, One Brookings Drive, St. Louis, MO, 63130, United States of America, xiaomeng.guo@wustl.edu, Lingxiu Dong, Danko Turcic A manufacturer’s ability to sell a new product often depends on a retailer’s willingness to stock the product. We construct a game-theoretic model of a supply chain with stochastic, price-sensitive demand and consider three basic wholesale price contracts: push, pull and delayed payment contracts. We show how a manufacturer can influence the retailer’s incentive to carry a second product by choosing a “correct” contract type and clarify which contract should be expected in equilibrium. TA49 49-Room 105B, CC Uncertainty in Sourcing and Procurement Sponsor: Manufacturing & Service Oper Mgmt/Supply Chain Sponsored Session Chair: Zohar Strinka, PhD Candidate, University of Michigan, 1205 Beal Ave., Ann Arbor, MI, 48109, United States of America, zstrinka@umich.edu 1 - Supplier Diversification under Random Yield and Price Dependent Demand Guang Xiao, Olin Business School, Washington University in St. Louis, St. Louis, United States of America, xiaoguang@wustl.edu, Lingxiu Dong, Nan Yang

We consider a firm’s supply diversification problem under supply random yield and price sensitive demand. We study two pricing schemes: responsive pricing and ex ante pricing. We characterize the sourcing decisions under each pricing scheme and compare them to study the strategic relation between diversification and responsive pricing. 2 - Risk Pooling under Price and Demand Uncertainty Refik Gullu, Professor, Bogazici University, Industrial Engineering Dept., Bebek, Istanbul, 34342, Turkey, refik.gullu@boun.edu.tr, Nesim K. Erkip We consider purchasing and distribution decisions for a commodity whose price is random and correlated with its demand. A model, where the purchasing decisions of locations are pooled is proposed. We obtain the optimal purchase quantity, time and quantity of allocation, and quantify the benefits of pooling price risk. 3 - Bunching Supply Contracts with Information Asymmetry in a Two-echelon Supply Chain Zahra Mobini, Erasmus University Rotterdam, Rotterdam, Netherlands, mobinidehkordi@ese.eur.nl, Albert Wagelmans, Wilco Van Den Heuvel In a two-echelon supply chain consisting of a supplier and a retailer where the latter has private information about his cost parameter, we analyze the design of the supplier’s optimal menu of contracts. Instead of offering a separating menu, the supplier offers a menu of bunching contracts where each contract is intended to appeal to more than one retailer type. We investigate the effects of offering such a menu on the supplier’s profit. 4 - Overstock Goods Auctions Zohar Strinka, PhD Candidate, University of Michigan, 1205 Beal Ave., Ann Arbor, MI, 48109, United States of America, zstrinka@umich.edu, H. Edwin Romeijn Manufacturers sometimes find themselves with a considerable quantity of overstock goods. In these cases, some turn to online liquidation auctions to sell excess inventory. We propose implementing US Treasury-style auctions in this setting which allow bidders to specify pairs of bid price and a desired quantity at that price. We consider bidders who are themselves retailers and face newsvendor-type costs based on the number of units won and uncertain demand. Operations Management and Marketing Interface Sponsor: Manufacturing & Service Operations Management Sponsored Session Chair: Ozge Sahin, Johns Hopkins University, ozge.sahin@jhu.edu Co-Chair: Yao Cui, Cornell University, 401N Sage Hall, Ithaca, United States of America, yao.cui@cornell.edu 1 - Econometric Models of Pairwise Externalities and Social Attractiveness for the Music Industry Stefano Nasini, Post-doctoral Researcher, IESE Business School, 3-7, Arnus i Gari, Barcelona, Spain, snasini@iese.edu, Victor Martínez-de-Albéniz We developed an econometric model of social attractiveness that integrates time variation of individual decisions with the structural information concerning their spillovers. The exponential family of distributions is used to jointly deal with the dynamic and structural aspect of such a complex statistical setting. It resulted in a well-suited model for the analysis of artist goods. An application to a large data set of song diffusion on the radio is presented. 2 - Inventory Management for Luxury Goods Ruslan Momot, INSEAD, Boulevard de Constance, Fontainebleau, 77305, France, ruslan.momot@insead.edu, Elena Belavina, Karan Girotra Firms selling conspicuous goods face a trade-off: producing more allows for extracting more revenues but compromises the product’s reputation for exclusivity. We capture this trade-off in a dynamic model of strategic customer and firm behavior that includes limited memory. Firms should follow stationary cyclic strategies alternating scarcity and overproduction. The former builds a reputation whereas the latter exploits it. The longer the customer memory, shorter is the overproduction phase. 3 - A Newsvendor Model with Product Bundling Qingning Cao, University of Science and Technology of China, 96 Jinzhai Rd, SM 611, Hefei, China, caoq@ustc.edu.cn, Jun Zhang, Kathy Stecke, Xianjun Geng This paper studies a firm’s optimal ordering decision of a primary product when the firm can bundle this product with another product. The firm makes an ordering decision before demand uncertainty resolves, and then retails this primary product either alone or in a mixed bundle with a secondary product. Our results suggest that as compared to a no-bundling benchmark, the firm should overstock (understock) when the wholesale price is high (low). TA50 50-Room 106A, CC

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