2015 Informs Annual Meeting

TD47

INFORMS Philadelphia – 2015

TD45 45-Room 103C, CC Topics in Dynamic Pricing and Revenue Management Sponsor: Revenue Management and Pricing Sponsored Session Chair: Robert Phillips, Columbia Business School, 2790 Broadway Uris Hall, New York, NY, 10027, United States of America, rp2051@columbia.edu 1 - Dynamic Pricing with Demand Covariates Sheng Qiang, Student, Stanford University, 41 Olmsted Road, Apt 108, Stanford, CA, 94305, United States of America, sqiang@stanford.edu, Mohsen Bayati, Michael Harrison A firm sells products over T periods, without knowing the demand function. The firm sets prices to earn revenue and learn the demand function. In each period before setting the prices, the firm observes some demand covariates, like marketing expenditure, consumer’s attributes, etc. The performance is measured by the regret, which is the expected revenue deviation from the optimal pricing policy when demand function is known. We study the asymptotic near-optimal algorithms to optimize the regret. 2 - What Really Happens in Implementing Revenue Management Capabilities and What to Expect in the Future Vedat Akgun, Director, Revenue Analytics, 3100 Cumberland Blvd SE, Suite 1000, Atlanta, GA, 30339, United States of America, vakgun@revenueanalytics.com, Jon Higbie Implementation of Revenue Management started more than thirty years ago and Revenue management concepts have evolved over time providing extraordinary benefits to companies. In addition to realizing great success, we also face challenges and learn lessons based on our experience and research. We want to discuss what really happens in implementing Revenue Management capabilities and what we can expect in the future. 3 - Nonparametric Algorithm for Joint Pricing and Inventory Control with Lost-sales and Censored Demand Boxiao (Beryl) Chen, University of Michigan-Ann Arbor, 1205 Beal Avenue, Ann Arbor, MI, 48109, United States of America, boxchen@umich.edu, Xiuli Chao, Cong Shi We consider the classic joint pricing and inventory control problem with lost-sales and censored demand in which the demand distribution is not known to the firm a priori. Conventional learning algorithms are not applicable as the firm can observe neither the realized value nor any derivative information of the true objective function, and the estimate of the expected profit function from data is not unimodal. We develop a data-driven algorithm which converges and provide its convergence rate. TD46 46-Room 104A, CC Service Operations Sponsor: Manufacturing & Service Oper Mgmt/Service Operations Sponsored Session Chair: Gad Allon, Professor, Kellogg School of Management, Northwestern University, 2001 Sheridan Road, Evanston, IL, 60201, United States of America, g-allon@kellogg.northwestern.edu 1 - Managing Service Systems in Presence of Social Networks Gad Allon, Professor, Kellogg School of Management, Northwestern University, 2001 Sheridan Road, Evanston, IL, 60201, United States of America, g-allon@kellogg.northwestern.edu, Dennis Zhang We study a service system with the presence of a social network. In our model, firms can differentiate resource allocations among customers, and customers learn the service qualities from the social network. We study the interplay among network structure, customer characteristics, and information structure, and characterize the optimal policy. We further calibrate our model with data from Yelp.com and quantify the value of social network knowledge empirically. 2 - Keeping Up with the Joneses: using Social Network Information to Manage Availability Ruslan Momot, INSEAD, Boulevard de Constance, Fontainebleau, 77305, France, ruslan.momot@insead.edu, Elena Belavina, Karan Girotra Growing availability of data on the patterns of customers’ social interactions has opened up new opportunities for businesses. We identify an optimal distribution strategy for a firm selling to socially connected customers engaged in social comparison. We build a stylized game-theoretic model of strategically interacting customers in a general network. We find that the optimal strategy is non

monotonic-neither most nor least connected customers are prime targets for making the product available. 3 - Supply Disruptions and Optimal Network Structures Kostas Bimpikis, Stanford GSB, 655 Knight Way, Stanford, CA, 94305, United States of America, kostasb@stanford.edu, Ozan Candogan, Shayan Ehsani This paper studies multi-tier supply chain networks in the presence of disruption risk. Firms compete with one another by participating in one of K production stages. We provide a characterization of the equilibrium prices, profits, and sourcing decisions and derive insights on how the network structure and the reliability of production in different tiers affect firms’ profits and the prices of intermediate goods. 4 - Creating Reciprocal Value through Operational Transparency Ryan Buell, Harvard Business School, Morgan Hall 429, Boston, MA, 02163, United States of America, rbuell@hbs.edu, Tami Kim, Chia-Jung Tsay We investigate whether organizations can create value by introducing visual transparency between consumers and producers. Two field and three laboratory experiments in food service settings suggest that transparency that 1) allows customers to observe operational processes and 2) allows employees to observe customers not only improves customer perceptions, but also increases service quality and efficiency. TD47 47-Room 104B, CC Sustainable Operations Management Sponsor: Manufacturing & Service Oper Mgmt/Sustainable Operations Sponsored Session Chair: David Drake, Assistant Professor, Harvard Business School, Morgan Hall 425, Boston, MA, United States of America, ddrake@hbs.edu 1 - Mobile Money Agent Inventory Management Karthik Balasubramanian, Harvard Business School, 25 Harvard Way, Boston, MA, 02163-1011, United States of America, kbalasubramanian@hbs.edu, David Drake, Douglas Fearing Mobile money agents exchange cash for electronic value and vice versa, forming the backbone of an emerging electronic currency ecosystem in the developing world. Unfortunately, low agent service levels are a major impediment to the further development of these ecosystems. We model the agent’s inventory problem and numerically determine optimal quantities. Finally, we evaluate our recommendations with a large dataset of mobile money agent transactions in an East African country. 2 - Energy Efficiency Contracting in Supply Chains under Asymmetric Bargaining Power Ali Shantia, HEC-Paris, 7, Avenue De La Gare, Bievres, 91570, France, ali.shantia@hec.edu, Andrea Masini In a supply chain, consisting of a buyer and a supplier, this study analyzes the effect of relative bargaining power and technology uncertainty on the supplier’s decision to invest in energy efficiency (EE) measures. We analyze price commitment and shared investment contracts and compare the two mechanisms in their ability to boost EE investment when the buyer’s high bargaining power in addition to high technology uncertainty prevent the supplier from investing in EE. 3 - Competitive Industry’s Response to Environmental Tax Incentives for Green Technology Adoption Anton Ovchinnikov, Queen’s University, 143 Union Str West, Kingston, Canada, anton.ovchinnikov@queensu.ca, Dmitry Krass We consider operational aspects of how an industry composed of heterogeneous firms responds to an environmental tax by choosing production quantities and emissions-reducing technologies. We show the existence and uniqueness of the “market-only equilibrium” and demonstrate its many interesting properties. We then discuss the technology-and-market equilibria under different structural assumptions. 4 - Carbon Tariffs: Effects in Settings with Technology Choice and Foreign Production Cost Advantage

David Drake, Assistant Professor, Harvard Business School, Morgan Hall 425, Boston, MA, United States of America, ddrake@hbs.edu

When firms can choose from a set of potential production technologies and offshore facilities hold a production cost advantage, I show that carbon leakage due to offshoring and/or foreign entry can result despite the implementation of a carbon tariff. However, in such a setting, carbon leakage is shown to conditionally decrease global emissions, contradicting prevailing popular opinion and widely reported results that do not account for technology choice or foreign production cost advantage.

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