Informs 2015

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INFORMS Philadelphia – 2015
361
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,
1 - Dynamic Pricing with Demand Covariates
Sheng Qiang, Student, Stanford University, 41 Olmsted Road,
Apt 108, Stanford, CA, 94305, United States of America,
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, 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,
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,
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,
, 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
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,
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, ,
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, , 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,
1 - Mobile Money Agent Inventory Management
Karthik Balasubramanian, Harvard Business School, 25 Harvard
Way, Boston, MA, 02163-1011, United States of America,
, 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, 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, , 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,
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.
TD47