2015 Informs Annual Meeting

TC51

INFORMS Philadelphia – 2015

TC51 51-Room 106B, CC Online Retailing Sponsor: Manufacturing & Service Operations Management Sponsored Session Chair: Dorothee Honhon, Associate Professor, University of Texas at Dallas, 800 W. Campbell Road, Richardson, TX, 75080, United States of America, Dorothee.Honhon@utdallas.edu Co-Chair: Amy Pan, Assistant Professor, University of Florida, Dept. of ISOM, Warrington College of Business Administr, Gainesville, FL, 32608, United States of America, amy.pan@warrington.ufl.edu 1 - Counteracting Strategic Purchase Deferrals: The Impact of Online Retailers’ Return Policy Decisions Tolga Aydinliyim, Baruch College, One Bernard Baruch Way, Dept of Management Box B9-240, New York, United States of America, Tolga.Aydinliyim@baruch.cuny.edu, Mehmet Sekip Altug We study the impact of (i) forward-looking (i.e., discount-seeking) consumer behavior and (ii) consumers’ sensitivity to clearance period stock availability on retailers’ returns management decisions and the ensuing demand segmentation and profit effects in both monopolistic and competitive settings. 2 - Replenishment under Uncertainty in Online Retailing Jason Acimovic, Penn State University, 462 Business Building, University Park, PA, 16802, United States of America, jaa26@smeal.psu.edu, Stephen Graves Online retailers often may serve most customers from any warehouse location. Simple order-up-to policies are easy to implement; however, they may perform suboptimally leading to high shipping costs. We propose a replenishment heuristic based on bringing inventory up to a target level on the day inventory arrives. We calculate robust target levels, taking into account demand uncertainty, shipping costs, and estimated stockout costs. We show how this policy performs on realistic data. 3 - Optimal Spending for a Search Funnel Shengqi Ye, The University of Texas at Dallas, 800 West Campbell Sponsored search marketing has been a major advertising channel for online retailers. Recent observation indicates that not all customers finalize their purchase decision after their first search query. Instead, customers might take a path of keywords and clicks - a search funnel - to complete a conversion. Noting this behavior, we investigate a retailer’s optimal advertising budget allocation across keywords in the search funnel. 4 - Omnichannel Inventory Management with Buy-Online-and-Pickup-in-Store Fei Gao, The Wharton School, University of Pennsylvania, 3730 Walnut Street, 500 Jon M. Huntsman Hall, Philadelphia, PA, United States of America, feigao@wharton.upenn.edu, Xuanming Su Many retailers offer customers the option to buy online and pick up orders in store. We study the impact of this omnichannel strategy on store operations and offer recommendations to retailers. Road, Richardson, TX, 75080, United States of America, sxy143530@utdallas.edu, Goker Aydin, Shanshan Hu TC52 52-Room 107A, CC Machine Learning Applications in Marketing Sponsor: Marketing Science Sponsored Session Chair: Daria Dzyabura, Assistant Professor of Marketing, NYU Stern School of Business, 40 West 4th Street, Tisch 805, New York, NY, 10012, United States of America, ddzyabur@stern.nyu.edu 1 - Big Data Pricing Eric Schwartz, University of Michigan, Ann Arbor, Michigan, United States of America, ericmsch@umich.edu, Kanishka Misra We study how a firm should maximize revenue by dynamically setting its price over time for a new product. We solve this optimal experimentation problem as a multi-armed bandit problem combined with economic theory. The approach adds to dynamic pricing in marketing and econometrics using non-parametric identification of demand by using reinforcement learning. In particular, we derive a pricing algorithm based on upper confidence bound and illustrate its theoretical and empirical properties.

2 - Nonparametric Demand Predictions for New Products Srikanth Jagabathula, NYU, 44 West Fourth Street, New York, NY, United States of America, sjagabat@stern.nyu.edu, Lakshminarayana Subramanian, Ashwin Venkataraman Predicting demand for new products is important and challenging. Existing parametric approaches require selection of relevant features of products and specification of the parametric structures, both of which are challenging. We propose a non-parametric approach combining ideas from “Learning to Rank” in machine learning and “Choice Estimation” in operations and marketing. The resulting methods can be used out-of-the-box and allow us to predict the impact of changes in product features. 3 - A Structured Analysis of Unstructured Big Data Leveraging Cloud Computing Xiao Liu, Assistant Professor Of Marketing, New York University, In this study, we combine methods from cloud computing, machine learning and text mining to illustrate how content from social media can be effectively used for forecasting purposes. We conduct our analysis on a staggering volume of nearly two billion Tweets. Our main findings highlight that, in contrast to basic surface- level measures such as volume or sentiment, the information content improve forecasting accuracy significantly. 4 - Modeling Multi-taste Consumers Liu Liu, NYU Stern School of Business, 40 West 4th Street, Tisch Hall, Room 825, New York, NY, 10012, United States of America, lliu@stern.nyu.edu, Daria Dzyabura In many product categories where recommendation systems are used, a single consumer may have multiple different tastes. We propose a framework for modeling choice behavior of such a multi-taste consumer and an iterative algorithm for estimation. We test it in numerical studies and an empirical application (Allrecipes.com). Our results show that it has superior out-of-sample predictive performance than single-taste models and is able to accurately recover parameters in simulation studies. TC53 53-Room 107B, CC Grab Bag of Behavioral Papers Sponsor: Behavioral Operations Management Sponsored Session Chair: Kenneth Schultz, Associate Professor, AFIT, 2950 Hobson Way, WPAFB, OH, 45433, United States of America, Kenneth.Schultz@afit.edu 1 - The Influence of Education and Experience Upon Contextual and Task Performance in Warehouse Operations Allen Miller, Student, Air Force Institute of Technology, 2950 Hobson Way, WPAFB, OH, 45433, United States of America, Allen.miller@afit.edu We believe worker-performance may be affected by the individual’s knowledge of why and where they fit into a larger system, defined as mission clarity. We conduct a controlled experiment to discern how education, experiences and subject characteristics impact mission clarity and subsequently contextual and 44 West 4th Street, New York, NY, 10012, United States of America, xiaoliu@andrew.cmu.edu, Kannan Srinivasan, Param Vir Singh The efficacy and necessity of coordinating contracts has a strong analytical support, but experimental work shows that decision makers do not set the contracts as theory prescribes. Alternative objective functions of risk and fairness are explored at the individual level to understand when and for whom the contracts are most and least effective and necessary. 3 - The Influence of Emergency Medical Services Load on Paramedics On-scene Clinical Decisions Mohammad Delasay, Post-doctoral Fellow, Tepper School of Business, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA, United States of America, delasays@andrew.cmu.edu, Kenneth Schultz, Armann Ingolfsson We investigate the effect of emergency medical system load on paramedics’ medical decisions. We hypothesize that paramedics’ decisions about on-scene time and transporting a patient to hospital are influenced by the emergency system load. We test our hypotheses by analyzing a data set of emergency responses in Calgary, Canada. task performance in a pick-and-pack operation. 2 - Personal Bias and Contract Setting Julie Niederhoff, Syracuse University, Syracuse, NY United States of America, jniederh@syr.edu

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