2016 INFORMS Annual Meeting Program

MC57

INFORMS Nashville – 2016

3 - An Inventory Model With Fuzzy Demand In a Two-echelon Supply Chain Management: Drug Distribution In Hospital Pharmacy Parisa Jannatifard, graduate Student and teacher assistant, Southern Illinoise University Edwardsville, Edwardsville, IL, 62026, United States, pjannat@siue.edu, Javad Sadeghi This paper develops an inventory model with fuzzy demand regarding the ven- dor managed inventory (VMI) policy. In recent past few decades, VMI policies have been used in modeling inventory problems. The vendor’s warehouse has a capacity constraint while a vendor supplies several products to retailers. This pol- icy reduces total costs in healthcare system like drug distribution in hospital pharmacy. The aim of this paper is to find a near optimal solution including order quantities for vendor and retailer and replenishment frequencies to mini- mize the total cost with a metaheuristic algorithm. 4 - Inventory Pooling And Transshipment Under Correlated Fat-Tail Demands Zhen Liu, Numerix LLC, 1237 Bristol Ln, Buffalo Grove, IL, 60089, United States, zhenliu@alum.northwestern.edu We study the classic inventory pooling problem by Eppen (1979) under a special class of multivariate fat-tail distribution: Normal Inverse Gaussian (NIG) demands to better fit real-world demand data. We obtain the optimal inventory level in a closed form by employing standardized NIG density function, and express the optimal expected costs in terms of unit NIG loss function. Rather than independent and identically distributed demands, our results complement Bimpikis and Markakis (2015) by considering correlated demands. We further discuss the transshipment problem of Dong and Rudi (2004) under NIG demands. 5 - Cyclic Vs. Static Inventory Policy Assumptions When Optimizing Case-pack Sizes In Grocery Retailing Heinrich Kuhn, Catholic University of Eichstaett-Ingolstadt, Auf der Schanz 49, Ingolstadt, 85049, Germany, heinrich.kuhn@ku.de, Thomas Wensing, Michael Sternbeck We analyze two possible approaches quantifying the optimal case-pack (CP) size in retail distribution systems. One approach assumes a cyclic inventory policy taking into account the weekly seasonality of product demands. The other approach assumes an equal demand distribution on each day of the week, i.e., assuming a stationary inventory policy. The general assumptions of a periodic review reorder inventory policy, i.e., (r, s, nq) policy, are assumed. We conduct several experiments analyzing the question under what circumstances both approaches achieve equal results. MC56 Music Row 4- Omni IT and Services Sponsored: EBusiness Sponsored Session Chair: Atanu Lahiri, University of Texas at Dallas, Richardson, TX, United States, atanu.lahiri@utdallas.edu 1 - Data-driven Optimization: Revenue Analytics For A Supply-side Network Via Traffic-stream Mixing Zhen Sun, University of Texas-Dallas, 800 west campbell rd, Richardson, TX, United States, zhen.sun@utdallas.edu, Milind Dawande, Ganesh Janakiraman, Vijay S Mookerjee This study develops a data-driven approach to solve constrained optimization problems in which the decision maker does not have an analytic form for the objective function, but knows what decision variables affect the function. Our approach comes with a worst-case performance guarantee that improves with the characteristics (size, pervasiveness) of the available data. We apply our technique to a traffic-stream mixing problem encountered by a supply-side internet advertising network that wishes to optimize the click revenue earned from ads. 2 - Cardinality Bundles With Complex Costs Jianqing Wu, Foster School of Business, University of Washington, Seattle, WA, United States, fisherwu@uw.edu, Mohit Tawarmalani, Karthik Kannan This paper studies pricing of cardinality bundles (CB) when bundling involves complex costs. When implementing CB, a firm set prices on the sizes of bundle and lets consumers choose specific products based on bundle sizes. The basic model of CB is analyzed in Wu et al. (2016). In this paper, we first extend the existing CB model to allow fixed costs in adding additional bundles. We show that CB problem with fixed costs can be solved as a shortest-path problem. We then extend the CB model in another way to solve CB problem with submodular cost structure. Such analysis is especially useful when there exists economies of scale in production.

4 - An Economic Analysis Of The Impact Of Recommender Systems On Product Search Abhijeet Ghoshal, University of Louisville, abhijeet.ghoshal@louisville.edu, Vijay M. Mookerjee, Sumit Sarkar We perform an economic analysis of firms providing recommendation services considering the influence of recommendations on the search process of customers. We determine the price and recommendation system effectiveness equilibrium and analyze how the equilibrium shifts when costs of recommendations change. MC57 Music Row 5- Omni Experiments in Supply Chain Management Sponsored: Behavioral Operations Management Sponsored Session Chair: Andrew M. Davis, Cornell University, Ithaca, NY, United States, adavis@cornell.edu 1 - Experimental Evidence On Post-choice Forecasting Bias: Do Optimizers Know They’re Cursed? Jordan Tong, University of Wisconsin, jtong@bus.wisc.edu Choose the best alternative and predict its outcome. We find that people tend to be too optimistic in such post-choice forecasting tasks. This tendency has important implications. For example, it suggests that managers who pick products to include in assortments tend to forecast higher demand and order more inventory than managers who don’t pick. We develop a behavioral model to help explain the phenomenon and provide supporting experimental evidence in three settings: guessing the number of pennies in jars, forecasting and making inventory decisions for products, and estimating sales prices of houses. Finally, we study factors that exacerbate the bias and possible ways to mitigate it. 2 - Firm Objectives And Managers’ Pricing Decisions: Theory And Experiments Rashmi Sharma, Penn State University, rashmi.sharma@psu.edu, Saurabh Bansal, Elena Katok In this paper we develop a model to determine what compensation plan a firm should offer to managers who make pricing decisions under price-responsive, uncertain demand. We show that the structure of the compensation plan depends on the firm’s objective function. We then report the results of a behavioral experiment to test the model’s predictions. 3 - Multidimensional Bargaining In Supply Chains: An Experimental Study We experimentally investigate the impact of bargaining and the allocation of inventory risk on the performance of a two-stage supply chain. We show that when allowing the parties to bargain over all contract terms simultaneously, observed supply chain efficiencies are at least 90%, which are considerably higher than those seen in past studies. Second, contrary to the theoretical prediction, the party incurring the inventory risk always earns a substantially lower profit than the other party. Third, a win-win situation is created when all contract terms are simultaneously negotiated. 4 - Group Identity To Manipulate Social Preferences In Sales And Operations Planning Felix Papier, Associate Professor, ESSEC Business School, 3 Av Bernhard Hirsch BP 50105, Cergy Pontoise Cedex, France, papier@essec.edu, Ulrich Thonemann, Torsten Gully We analyze a supply chain in which a demand planner provides demand forecasts to a production planner. The production planner needs information about the forecast accuracy. If the actual effort of the demand planner and the belief of the production planner are not aligned, the supply chain performance suffers. We develop a game theoretic model to show how social preferences affect the alignment between the two supply chain actors. Using lab experiments we find that some demand planners invest effort, and that production planners anticipate this effort. We further show that group identity can increase social preferences, which ultimately leads to higher supply chain profit. Kyle Hyndman, University of Texas at Dallas, kyleb.hyndman@utdallas.edu, Andrew M. Davis

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