Informs 2015

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INFORMS Philadelphia – 2015
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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,
Co-Chair: Amy Pan, Assistant Professor, University of Florida, Dept. of
ISOM, Warrington College of Business Administr, Gainesville, FL,
32608, United States of America,
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,
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,
, 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
Road, Richardson, TX, 75080, United States of America,
, Goker Aydin, Shanshan Hu
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, ,
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.
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,
1 - Big Data Pricing
Eric Schwartz, University of Michigan, Ann Arbor, Michigan,
United States of America, , 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,
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,
44 West 4th Street, New York, NY, 10012, United States of
America, , Kannan Srinivasan,
Param Vir Singh
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,
, 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 ). 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,
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,
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
task performance in a pick-and-pack operation.
2 - Personal Bias and Contract Setting
Julie Niederhoff, Syracuse University, Syracuse, NY
United States of America,
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,
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.
TC51