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Fall 2010
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Fall 2010

<
 Stefanos Zenios Stanford GSB September 23rd Lauren Lu UNC September 29th Gurhan Kok Fuqua School of Business October 6th Sridhar Tayur Tepper School of Business October 13th Yong-Pin Zhou University of Washington October 20th Retsef Levi MIT Sloan October 29th Milind Sohoni ISB November 3rd S. Rajagopalan USC November 17th

"Health Savings Accounts: Consumer Contribution Strategies & Policy Implications"
Talk by Stefanos Zenios, Stanford GSB
September 23rd, 2010

12:00-1:00 PM, room 561
A Health Savings Account (HSA) is a tax-advantaged savings account available only to households with high-deductible health insurance. Each year, the household contributes a pre-tax dollar amount to the HSA and uses the account to cover its out-of-pocket medical expenses. This paper provides initial answers to two questions related to HSAs: 1) How should a household's annual contributions be influenced by the health status of the members of its household? and 2) Do current contribution limits provide households with the flexibility to use HSAs efficiently? To answer these questions, we formulate the household's decision problem as one of determining annual contributions that minimize total expected discounted medical costs, with costs not covered by the HSA balance paid with after tax dollars and thus carrying an extra penalty. In this formulation, costs vary from year to year according to a discrete time continuous space markov process with the state reflecting the household's health status. An optimal dynamic threshold policy, in which the contribution each year brings the HSA balance to a health state-dependent threshold, is derived. This is compared to a simpler static policy in which the annual contribution is state-independent. The parameters of the Markovian cost model are estimated using longitudinal cost data for 43,000 households from the 1996- 2004 Medical Expenditure Panel Survey. Policies are then derived and tested for the Stanford University HSA plan using the estimated cost model. The results show that: a) Optimal annual contributions can vary by as much as $3,500 because of differences in health status; b) Total discounted costs for static policies are$12,000-\$150,000 higher then the corresponding dynamic policies; c) A two-tiered form for tax-advantaged contribution limits, in which the contribution size is unrestricted up to a certain HSA balance threshold (tied to the plan's out-of-pocket maximum) and restricted beyond it, is necessary for the household to enjoy the benefits of the optimal threshold policies.

"Is Outsourcing a Win-Win Game? The Effect of Competition, Contractual Form, and Merger"
Talk by Lauren Lu, UNC
September 29th, 2010

12:00-1:00 PM, room 561
Two well-accepted notions exist in the outsourcing literature: First, outsourcing is a win-win game when suppliers possess operational advantage; Second, outsourcing mitigates competition. This paper challenges these notions using a differentiated duopoly model with two competing supply chains. Each supply chain consists of an upstream supplier and a downstream manufacturer, who engage in a bilateral negotiation of an outsourcing contract. We demonstrate that the benefits of outsourcing depend on some key economic primitives of a model, such as the mode of competition, contractual form, and upstream industry structure. With a wholesale price contract, outsourcing is always a win-win game as long as the suppliers possess operational advantage. When the outsourcing contract takes the form of a two-part tariff, however, the suppliers’ operational advantage is a double-edged sword for outsourcing. On the one hand, it reduces the manufacturers’ costs. On the other hand, it may intensify downstream competition or weaken the manufacturers’ bargaining position, depending on the mode of competition. Specifically, outsourcing imposes a strategic liability by intensifying the competition between the manufacturers who compete with quantities. Because quantities are strategic substitutes, the suppliers are trapped in a race to over-subsidize downstream production, in an attempt to gain market share by negotiating a two-part tariff with a below-cost unit price. The suppliers’ operational advantage exacerbates the intensity of this race. When the manufacturers compete with prices, in contrast, the suppliers have an incentive to charge above-cost unit prices to soften downstream competition, because prices are strategic complements. However, the manufacturers may still get hurt by the upstream’s operational advantage – their bargaining position is weakened due to a less profitable option to insource when competing against a rival who outsources to a low-cost supplier. These results continue to hold qualitatively when the upstream suppliers merge into a single firm. Whether the suppliers have an incentive to merge depends on the changes in both the competition externality and the bargaining structure, which again hinge on the mode of competition and the contractual form.

"Dynamic Assortment Customization with Limited Inventories"
Talk by Gurhan Kok, Fuqua School of Business
October 6th, 2010

12:00-1:00 PM, room 561

We consider a retailer with limited inventories of identically priced, substitutable products. Customers arrive sequentially and the firm decides which subset of the products to offer to each arriving customer depending on the customer's preferences, the inventory levels and the remaining time in the season. We show that the optimal assortment policy is to offer all available products if the customer base is homogeneous with respect to their product preferences. However, with multiple customer segments characterized by different product preferences, it may be optimal to limit the choice set of some customers. That is, it may be optimal not to offer products with low inventories to some customer segments and reserve them for future customers (who may have a stronger preference for those products). For the case of two products and two customer segments and for a special case with multiple products and multiple customer segments, we show that the optimal assortment policy is a threshold policy under which a product is offered to a customer segment if its inventory level is higher than a threshold value. The threshold levels are decreasing in time and increasing in the inventory levels of other products. For the general case, we perform a large numerical study, and confirm that the optimal policy continues to be of the threshold type. We find that the revenue impact of assortment customization can be significant, especially when customer heterogeneity is high and the starting inventory levels of the products are asymmetric. This demonstrates the use of assortment customization as another lever for revenue maximization in addition to pricing.

Winter/Spring 2010

<
 Burhaneddin Sandikci Chicago Booth January 27th Robert Boute Vlerick Leuven Gent Management School, visiting Kellogg School of Management February 10th Beril Toktay GIT February 17th Jim Dai Georgia Tech March 1st Nicole Adler The Hebrew University of Jerusalem April 21st Irina Dolinskaya Northwestern University April 28th Rodney Parker Chicago Booth May 5th Leon Yang Chu USC May 12th Srinagesh Gavirneni Cornell May 19th Vivek F. Farias MIT May 26th

"Estimating the value of waiting list information in liver transplant decision making"
Talk by Burhaneddin Sandikci, Chicago Booth
January 27th, 2010

2:00-3:00 PM, room 561
In the United States, patients with end-stage liver disease must join a waiting list to be eligible for cadaveric liver transplantation. Due to privacy concerns, the details of the composition of this waiting list are not publicly available. This paper considers the benefits associated with creating a more transparent waiting list. We study these benefits by modeling the organ accept/reject decision faced by these patients as a Markov decision process in which the state of the process is described by patient health, quality of the offered liver, and a measure of the rank of the patient in the waiting list. We prove conditions under which there exist structured optimal solutions, such as monotone value functions and control-limit optimal policies. We define the concept of the patient’s price of privacy, namely, the number of expected life days lost due to the lack of complete waiting list information. We conduct extensive numerical studies based on clinical data, which indicate that this price of privacy is typically on the order of 5% of the optimal solution value.

"Coordinating lead-time and safety stock decisions in a two-echelon supply chain"
Talk by Robert Boute, Vlerick Leuven Gent Management School, visiting Kellogg School of Management
February 10th, 2010

12:00-1:00 PM, room 561
We study a two-echelon (retailer-manufacturer) supply chain, modeled as a discrete time production/inventory system with random period consumer demands. The retailer’s inventory levels are reviewed periodically and managed using a base-stock policy. The manufacturer produces the retailer’s orders on a make-to-order basis and he decides on the lead time based on the retailer’s order process. The manufacturer’s production system is capacitated in the sense that there is a single server that sequentially processes single units one at a time with stochastic unit processing times. The resulting lead times determine safety stock levels at the retailer. Making use of matrix-analytic methods and Phase Type distributions, we analyze the interaction between the consumer demand process, the retailer’s replenishment decision (and corresponding safety stocks), and the manufacturer’s production lead time. We show that by including endogenous lead times in our analysis, the retailer’s order variability can be dampened without increasing his stock levels. This leads to a situation where both supply chain echelons are better off.

"Is Leasing Greener than Selling?"
Talk by Beril Toktay, GIT
February 17th, 2010

12:00-1:00 PM, room 561

Based on the proposition that leasing is environmentally superior to selling, some firms have adopted a leasing strategy and others promote their existing leasing programs as environmentally superior in order to green'' their image. The argument is that as a leasing firm retains ownership of the off-lease units, it has an incentive to remarket the products, resulting in a lower production and disposal volume. However, some argue that leasing might be environmentally inferior due to the direct control the firm has over the off-lease products, which may prompt their premature disposal to avoid cannibalizing the demand for new products. Motivated by these issues, we adopt a life-cycle environmental impact perspective and analytically investigate if either leasing or selling can be both more profitable for a monopolist and have a lower total environmental impact. We identify conditions where each of these outcomes can occur, depending on the magnitude of the disposal cost, the differential in disposal costs faced by the firm and consumers, and the environmental impact profile of the product. These results provide insights for firms who want to promote their marketing strategy as the greener'' choice.

"Distributional sensitivity in many-server queues"
Talk by Jim Dai, Georgia Tech
March 1st, 2010
2:00-3:00 PM, room 561

Many-server queues are building blocks to model many large-scale service systems such as call centers and hospitals. In such a system, customer abandonment can have a significant impact on revenue and system performance. When a customer's waiting time in queue exceeds his patience time, he abandons the system without service. We assume that customer service and patience times form two sequences of independent, identically distributed (iid) nonnegative random variables, having general distributions. Recent call center and hospital data show that these distributions are not exponential, despite most of the research to date assumes that at least one of these two distributions is exponential. I will discuss the sensitivity of service and patience time distributions for queues in many-server heavy traffic, and its implications on model identification, numerical algorithms, and asymptotic analysis. This is joint work with Shuangchi He at Georgia Tech.

Talk by Nicole Adler, The Hebrew University of Jerusalem
April 21st, 2010
12:00-1:00 PM, room 561

We present a modeling approach that accounts for desire for capacity within the consumer demand function. Using a two stage hybrid competitive-cooperative game, we analyze differentiated oligopolies under varying market structures from competition through different pooling contracts up to anti-trust immune alliances and mergers. The results suggest that pooling agreements maximize consumer surplus and social welfare, which may be of interest to competition authorities. Moreover cooperatively setting capacity and pooling, but competing in price, appears to be preferable to no agreement for both consumers and overall social welfare alike on 'thin' markets, defined as low demand with weak initial profit margins. A numerical analysis applying the model to the airline industry demonstrates our findings under asymmetric and uncertain demand, suggesting that codesharing on parallel links may sometimes be preferable to the competitive outcome for multiple consumer types.

"Optimal Path Finding in Direction, Location and Time Dependent Environments "
Talk by Irina Dolinskaya, Northwestern University
April 28th, 2010

12:00-1:00 PM, room 561
Real-time determination of an optimal path in a changing medium (such as winds and ocean waves) requires explicit incorporation of this cost function location- and time- dependency into the model. Furthermore, the direction-dependency of a cost function adds another layer of difficulty to the problem at hand.  In this talk, we present methods to efficiently incorporate the complex structure of the cost function into the path planning process. We also integrate the system’s operability and dynamics constrains in the optimization model, hence combining traditionally separated optimal-path finding and path-following stages of problem solving. An application to ship routing is introduced throughout the talk to motivate this research.

"Dynamic Inventory Competition with Stockout-Based Substitution "
Talk by Rodney Parker, Chicago Booth
May 5th, 2010

12:00-1:00 PM, room 561
This paper continues the stream of literature observed in Olsen and Parker (2008) where retailers compete under a Markov equilibrium solution concept. In this presentation, we consider a duopoly where retailers compete by providing inventory under the circumstances where unsatisfied customers may seek satisfaction elsewhere or leave. A very general framework is formulated to address a variety of customer avenues when stock is unavailable. We find a base-stock inventory policy is the equilibrium policy in the infinite horizon (open loop) under several mild conditions; this model's solution is known as an equilibrium in stationary strategies (ESS). We consequently determine conditions under which the parsimonious base-stock policy is the Markov equilibrium (closed loop) in a discrete-time dynamic game for a general time horizon, coinciding with the ESS base-stock levels. Importantly, when these conditions do not apply, we have counterexamples where a firm has a unilateral incentive to deviate from the ESS, stocking at a higher level. These examples demonstrate a value of inventory commitment, where the retailer may extract a benefit over multiple periods through committing to a higher stocking level and forcing her rival to understock. Our conclusion is that we establish conditions for a Markov solution to coincide with the ESS and this policy is base-stock, but other Markov solutions also exist.

"Optimal Pre-order Discount and Information Release"
Talk by Leon Yang Chu, USC
May 12th, 2010

12:00-1:00 PM, room 561
In this paper, we investigate the information release and pricing strategies for a seller who can take customer pre-orders before the release of a product. The pre-order option enables the seller to sell a product at an early date when consumers’ valuations are relatively homogeneous. We find that the optimal pricing strategy is discontinuous with respect to the amount of information available at pre-order, and a small change in the amount of information may cause a dramatic change in the proportion of consumers who pre-order under the optimal pricing strategy. Furthermore, the seller’s optimal information release strategy depends on a key measure, the normalized margin, which is the ratio between the expected margin and the standard deviation of consumer valuations. While the seller may want to release some (or zero) amount of information, depending on the normalized margin, the seller should never release all information. Finally, under the optimal information release strategy and pricing strategy, the benefit of pre-order is most pronounced when the seller can successfully position the product as a “mass-market” product by withholding information.

"Transfer Pricing and Offshoring in Global Supply Chains"
Talk by Srinagesh Gavirneni, Cornell
May 19th, 2010

12:00-1:00 PM, room 561
Taking advantage of lower foreign tax rates using transfer pricing and taking advantage of lower production costs using offshoring are two strategies that global companies use to increase their profitability. Evidence suggests that firms employ these strategies independently. We study how global firms can jointly leverage tax and cost differences through coordinated transfer pricing and offshoring. We derive a trade-off curve between tax and cost differences that can be used to design sourcing and transfer pricing strategies jointly. However, in a global firm the implementation of such jointly optimal strategies is often hindered by the following incentive problem. The headquarters is more concerned about the consolidated after tax profits than the local divisions. Local divisions, on the other hand, have a better view on the product cost structure and hence, have a better view on the appropriate sourcing strategies. Hence, we need to understand how different transfer price strategies and decentralization of sourcing and/or pricing decisions can be helpful. We find that when the tax differential is large, a fully centralized strategy works best. In other settings, a decentralized sourcing strategy (enabling the global firm to take advantage of the local cost information) should be considered. Finally, we show that when the cost of outsourcing increases, a decentralized company has more flexibility in transfer pricing and hence can achieve higher profits.

"A New Approach to Modeling Choice"
Talk by Vivek F. Farias, MIT
May 26th, 2010

12:00-1:00 PM, room 561
A central push in operations models over the last decade has been the incorporation of models of customer choice. Real world implementations of many of these models face the formidable (and very basic) stumbling block of simply selecting the ‘right’ model of choice to use. Thus motivated, we visit the following problem: For a ‘generic’ model of consumer choice (namely, distributions over preference lists) and a limited amount of data on how consumers actually make decisions (such as marginal preference information), how may one predict revenues from offering a particular assortment of choices? We present a non-parametric framework to answer such questions and design a number of tractable algorithms from a data and computational standpoint for the same. Our approach represents a novel and substantial departure from the typical attack on such basic questions. This departure is necessitated by problem scale and data availability.

In addition to laying out the basic theory, the practical value of the work will be demonstrated with a data-driven study. We will also briefly describe a current effort to build a 'product' based on our approach at Ford Motor.

Fall 2009

 Roger Schmenner Indiana University September 30th Kalyan Talluri UPF October 1st Karl Ulrich Wharton October 6th* Serhan Ziya UNC October 21st Owen Wu University of Michigan November 4th Harish Krishnan UBC November 11th Amy Ward USC November 18th Francis De-Vericourt ESMT December 2nd Anita Tucker HBS December 9th

"Operations Management and History"
Talk by Roger Schmenner, Indiana University
September 30th, 2009

12:00-1:00 PM, room 561
It is my contention that we students of operations management ignore what history can teach us about our discipline. Economists routinely study economic history (e.g., Ben Bernanke’s own work on the origins of the Great Depression, economic history courses), but we operations management types blithely neglect the lessons from our manufacturing, service, and supply chain past. This seminar will try to make sense of some key features of our history.

"A new risk-ratio procedure for estimating multinomial logit models with unobservable no-purchases"
Talk by Kalyan Talluri, UPF
October 1st, 2009

12:00-1:00 PM, room 561
Revenue management models in the literature, and in many implementations, make some important assumptions such as Poisson arrivals, independence, and multinomial logit customer purchase behavior. In this talk we describe:

1. A large scale empirical study spanning four RM industries (traditional airline, low-cost airline, cargo and hotel) to test these assumptions (Poisson, Logit, Independence) on transactional data. The data however does not contain information on customers who did not purchase (no-purchases). A novel feature of the study is that we device the tests assuming that the no-purchases are not observed.
2. We examine the standard nite-period, one-arrival-per-period dynamic program. We show that this model is essentially unestimable as the number of periods T is a design param- eter. Speci cally we show that the maximum likelihood estimates are always biased for large enough T. We augment the study with simulation experiments comparing the ML estimates vs. true parameters.
3. We propose an alternate dynamic program that operates with arbitrary variance (uncer- tainty) in the forecasts and is still tractable (for a single resource).
4. One of the most challenging problems in RM is the estimation of customer behavior models when one cannot observe no-purchases. We propose a new risk-ratio procedure that under the assumption that the customers arrive over time deterministically, leads to an exact unbiased estimator. We show conditions under which this estimator can be calculated by solving a convex or quasi-convex program. We describe simulation experiments where the method in many cases recovers the true parameters to the second decimal place, without observing no-purchases.

"Innovation Tournaments"
Talk by Karl Ulrich, Wharton
October 6th, 2009

4:00-5:00 PM, Ford Motor Company Engineering Design Center (joint seminar with Segal Design Institute) [link to Segal site]*

Extremely valuable innovations are usually based on statistically exceptional opportunities. In most settings, organizations use tournaments to find these exceptional opportunities, by which I mean they generate many candidate opportunities and develop and filter them until only the very best remain. Although the basic idea of a tournament is common in industrial practice, very little science has been brought to bear on the problem of generating more, better opportunities and on more accurately evaluating and selecting the exceptional few. In this talk I lay out the beginnings of a science of innovation tournaments, illustrating how the somewhat random process of identifying and selecting opportunities can be managed more deliberately. I then link the concept of innovation tournaments to the popular notion of "design thinking," arguing that design thinking works well for some types of problems but not others. This event is part of the Segal Seminar Series.

" "
Talk by Serhan Ziya, UNC
October 21st, 2009

12:00-1:00 PM, room 561
In many service systems, customers are not served in the order they arrive, but according to a priority scheme that ranks them with respect to their relative “importance.” However, it may not be an easy task to determine the importance level of customers, especially when decisions need to be made under limited information. A typical example is from health care: When triage nurses classify patients into different priority groups, they must promptly determine each patient’s criticality levels with only partial information on their conditions.

We consider such a service system where customers are from one of two possible types. The service time and waiting cost for a customer depends on the customer’s type. Customers’ type identities are not directly available to the service provider; however, each customer provides a signal, which is an imperfect indicator of the customer’s identity. The service provider uses these signals to determine priority levels for the customers with the objective of minimizing the long-run average waiting cost. In most of the paper, each customer’s signal equals the probability that the customer belongs to the type that should have a higher priority and customers incur waiting costs that are linear in time. We first show that increasing the number of priority classes decreases costs, and the policy that gives the highest priority to the customer with the highest signal outperforms any finite class priority policy . We then focus on two-class priority policies and investigate how the optimal policy changes with the system load. We also investigate the properties of “good” signals and find that signals that are larger in convex ordering are more preferable. In a simulation study, we find that when the waiting cost functions are nondecreasing, quadratic, and convex, the policythat assigns the highest priorityto the customer with the highest signal performs poorlywhile the two-class priority policy and an extension of the generalized cµ rule perform well.

"Seasonal Storage Asset Valuation: Uncovering the Value of Limited Flexibility"
Talk by Owen Wu, University of Michigan
November 4th, 2009

12:00-1:00 PM, room 561
The value of a seasonal commodity storage asset depends not only on the seasonal price spread, but also on its operational flexibility: The maximum storing and delivering rates depend on the inventory level in the storage, and thus the firm has limited flexibility in choosing when and how much inventory to procure or sell. Using the heuristics in practice, the firm would pick the periods with the most favorable prices to procure and sell. We characterize the optimal strategy, analyze the underlying tradeoffs under limited flexibility, and decompose the value of flexibility. We show that, contrary to intuition and the heuristics, it may be sub-optimal to buy or sell when all future prices are expected to be worse than the current price, because delaying operations captures the value of flexibility in the future. On the other hand, over-delaying operations would reduce flexibility and forgo the value of counter-season operations, and striking a balance is thus necessary. Also contrary to intuition, we show that even if the storage can be filled up or emptied at better prices later in the season, it may be optimal to buy or sell some inventory at the current least favorable price, because this allows the firm to buy less at the adverse price in the future. We also show that more flexibility is not necessarily beneficial when heuristic policies are used.

"Quick Response and Retailer Effort"
Talk by Harish Krishnan, UBC
November 11th, 2009

12:00-1:00 PM, room 561

The benefits of supply chain innovations such as quick response (QR) have been extensively investigated.  This paper highlights a potentially damaging impact of QR on retailer effort.  By lowering downstream inventories, QR may compromise retailer incentives to exert sales effort on a manufacturer’s product and may lead instead to greater sales effort on a competing product.  Manufacturer-initiated quick response can therefore backfire, leading to lower sales of the manufacturer’s product and, in some cases, to higher sales of a competing product.  Evidence from case studies and interviews confirms that some manufacturers view high retailer inventory as a means of increasing retailer commitment (“a loaded customer is a loyal customer”).  By implication, manufacturers should recognize the effect we highlight in this paper: the potential of QR to lessen retailer commitment.  We show that relatively simple distribution contracts such as minimum-take contracts, advance-purchase discounts, and exclusive dealing, when adopted in conjunction with QR, can remedy the distortionary impact of QR on retailers’ incentives.  In two recent antitrust cases we find evidence that, consistent with our theory, manufacturers adopted exclusive dealing at almost the same time that they were making QR-type supply chain improvements.

"Blind Fair Routing in Large-Scale Parallel-Server Systems "
Talk by Amy Ward, USC
November 18th, 2009

12:00-1:00 PM, room 561

In a call center, there is a natural trade-off between minimizing customer delay costs and fairly dividing the workload amongst agents of different skill levels. The relevant control is the routing and scheduling policy. The routing component specifies which agent should handle an arriving call when more than one agent is available, and the scheduling component decides which class a newly idle agent should serve when there are waiting customers in more than one class.

We formulate an optimization problem whose objective is to minimize the sum of class-dependent convex delay costs subject to a constraint that requires a “fair” division of the total idle time amongst the agents. We solve this optimization problem in the Halfin-Whitt many-server heavy-traffic limit regime. However, there is an important objection to the routing and scheduling policy that arises: its implementation requires extensive system parameter information. Therefore, we relax our original objective of finding a routing and scheduling policy that is optimal as the number of servers becomes large to finding a blind policy that is close to optimal. By blind, we mean that the implementation of the policy does not require system parameter information such as arrival and service rates.

* This is joint work with Mor Armony from NYU.

"Diagnostic Services Under Congestion"
Talk by Francis De-Vericourt, ESMT
December 2nd, 2009
12:00-1:00 PM, room 561

In diagnostic services, agents typically need to weigh the benefit of running an additional test and improve the diagnosis accuracy against the cost of delaying the provision of service to others. Our paper analyzes how to dynamically manage this accuracy/congestion tradeoff. To that end, we study an elementary congested service facing an arriving stream of customers.  The diagnostic process consists of a search problem in which the agent conducts a sequence of imperfect tests to determine whether a customer is of a given type. Our analysis yields counter-intuitive insights into managing diagnostic services. First, we find that the maximum number of customers allowed in the system should initially increase with the number of performed tests. This result is in sharp contrast with the established literature on value/congestion tradeoffs, which consistently asserts that congestion levels should decrease with service times. In our diagnosis system, only after the agent has run enough tests without identifying the customer type should the level of congestion decrease. This non-monotonic structure disappears when the base rate of the searched type is below a simple critical fraction, which captures the value of rightly identifying the customer type. Second, we find that the agent should sometimes diagnose the customer with the searched type, even when all tests are negative. This surprising result disappears when controlling for congestion, i.e. in a single diagnostic task.

"An Empirical Test of Management Involvement in Process Improvement"
Talk by Anita Tucker, HBS
December 9th, 2009

12:00-1:00 PM, room 561

Managers play a critical role in process improvement efforts. Despite potential gains in quality and efficiency, prior research has shown that many improvement efforts fail due to insufficient senior management involvement or a weak organizational climate for improvement. Less is known, however, about mechanisms that foster managers’ involvement with improvement efforts, which in turn may strengthen organizational climate. This paper addresses this gap with a field experiment of a bundle of process improvement activities suggested by “Management By Walking Around” (MBWA) (Peters and Waterman 1982) and the Toyota Production System. The three sequential activities were (1) interacting with frontline workers to learn about existing problems, (2) ensuring that action is taken to address these problems, and (3) communicating to frontline workers about actions taken. We compare before and after survey results from 20 hospitals randomly selected to engage in the activities for 18-months with 49 hospitals that served as controls. We found that identifying problems had a negative impact on organizational climate for improvement while taking action had a positive impact. Together these results suggest a theoretical reason for the success of Toyota’s problem solving system, which solves problems as they arise rather than gathering large amounts of data about problems before solution efforts begin. Contrary to our expectations, providing feedback about actions taken negatively impacted frontline workers’ perceptions. Qualitative results suggest that feedback can backfire when managers go through the motions of process improvement activities without making a sincere effort to learn about and resolve staff concerns.

Spring 2009

 Laurens Debo Chicago Booth January 14th Dan Adelman Chicago Booth January 21st Fuqiang Zhang Washington University in St. Louis January 29th Nils Rudi INSEAD February 4th Tolga Tezcan UIUC February 18th Opher Baron University of Toronto February 25th Ozge Sahin University of Michigan March 5th Senthil Veeraraghavan Wharton March 11th Dan Guide Penn State March 18th Jayashankar Swaminathan UNC April 15th Saif Benjaafar Unversity of Minnesota April 22nd Jeannette Song Duke April 29th Erica Plambeck Stanford GSB May 7th Che-Lin Su Chicago Booth May 13th Guillaume Roels UCLA May 20th Gabriel Weintruab Columbia GSB May 27th Alan Andrew Scheller-Wolf CMU June 3rd

"Stock-Outs and Customer Purchasing Behavior when Product Quality is Uncertain"
Talk by Laurens Debo, Chicago Booth
January 14th, 2009

1:00-2:00 PM, room 561
Inventory availability can influence consumer's perceptions of product quality, especially with new, un-known or innovative products. Consumers who find a product out of stock at several retailers may infer that many other consumers bought and therefore value it; this information may induce them to buy as well. In this paper, we study this phenomenon. Even though stock-outs may generate the buzz' that the product quality is high, increasing the consumer's willingness to buy, too many retailers that are out of stock may lead to lost sales. Hence, creating buzz through stock-outs is tricky. We analyze a model in which potential
consumers observe privately a noisy signal of the product quality as well as the number of retailers that is out of stock within a subset of retailers. The initial inventory of each retailer, which may either be high or low, is not observable to the consumers. We study how the number of observed stock-outs impacts the
consumer purchasing behavior and the realized sales. We find that (1) the equilibrium willingness to buy may increase when more stock-outs are observed and (2) the ex ante expected realized sales may increase when the variance on the initial inventory at each retailer is high and increases (keeping the expected initial inventory constant). With our model, we explain when the buzz effect can be significant and how firms can leverage this effect.

"Approximate Dynamic Programming on High-Dimensional Continuous Spaces Using Linear Programming"
Talk by Dan Adelman, Chicago Booth
Wednesday, January 21st, 2009

1:00-2:00 PM, room 561
Using the generalized joint replenishment (GJR)  problem as an example, we devise an algorithm for solving the infinite dimensional linear programs that arise from general deterministic semi-Markov decision processes on Borel spaces.   The innovative idea is to approximate the dual solution with continuous piecewise linear ridge functions that naturally represent functions defined on a high dimensional domain as linear combinations of functions defined on only a single dimension.   The algorithm automatically generates a value function approximation basis built upon piecewise-linear ridge functions, by developing and exploiting a theoretical connection with the problem of finding optimal cyclic schedules.   We provide a variant of the algorithm that is effective in practice, and exploit the special structure of the GJR problem to provide a coherent, implementable framework. Finally, we present numerical results demonstrating the performance of the resulting policy.

"Procurement Mechanism Design in a Two-Echelon Inventory System with Price-Sensitive Demand "
Talk by
Fuqiang Zhang, Washington University
Thursday, January 29th, 2009

12:00-1:00 PM, room 561
This paper studies a buyer's procurement strategies in a two-echelon inventory system with price-sensitive demand. The buyer procures a product from a supplier and then sells to the marketplace. Market demand is stochastic and depends on the buyer's selling price. The supplier's production cost is private information, and the buyer only knows the distribution of the cost. Both the buyer and the supplier can hold inventories to improve service, and a periodic review inventory system is considered. The buyer takes two attributes into consideration when designing the procurement mechanism: quantity attribute (i.e., the total purchase quantity) and service-level attribute (i.e., the supplier's delivery performance). We first identify the optimal procurement mechanism for the buyer, which consists of a menu of nonlinear contracts for each of the two attributes. It can be shown that the optimal mechanism induces both a lower market demand and a lower service level compared to the supply chain optimum. In view of the complexity of the optimal mechanism, we proceed to search for simpler mechanisms that perform well for the buyer. We find that the above two attributes have different implications for procurement mechanism design: The value of using complex contract terms is generally negligible for the service-level attribute, while it can be highly valuable for the quantity attribute. In particular, we demonstrate that a fixed service-level contract, which consists of a target service level and a price-quantity menu, yields nearly optimal profit for the buyer. Additionally, the price-quantity menu is essentially a quantity discount scheme widely observed in practice.

"Level, adjustment and observation biases in the newsvendor model"
Wednesday, February 4th, 2009

1:00-2:00 PM, room 561
In an experimental newsvendor setting where 310 subjects make 50 repeated newsvendor decisions, we investigate three forms of biases: Level bias - the average tendency of ordering away from the optimal order quantity; adjustment bias - the tendency to adjust order quantities; and observation bias - the tendency to let the degree of information available influence order quantities. We study these biases in terms of decisions (quantities) and performance (expected mismatch cost) and find evidence of all three of them as well as significant interaction between them.

We find that the portion of mismatch cost due to adjustment bias exceeds the portion of mismatch cost due to level bias in three out of four conditions, highlighting the importance of considering adjustment bias in addition to the more commonly studied level bias. Observation bias is studied through censored demands, a situation which arguably represents the majority of newsvendor settings. When demands are uncensored, subjects tend to order below the normative quantity when facing high margin and above the normative quantity when facing low margin, but in neither case beyond mean demand (a.k.a. pull-to-center effect). Censoring in general leads to lower quantities, magnifying the downward adjustment when facing high margin but partially counterbalancing the upwards adjustment when facing low margin, and in both cases actually violating the pull-to-center effect.

"Control of systems with flexible multi-server pools: A shadow routing approach "
Talk by Tolga Tezcan
, UIUC
Wednesday, February 18th, 2009

1:00-2:00 PM, room 561
A general model with multiple input flows (classes) and several flexible multi-server pools is considered. Applications of this model arise in service systems such as call centers, health care systems and closed-loop supply chain systems. Motivated by such modern service systems that face time dependent and random demand, we focus on systems under arrival rate uncertainty. Our goal is to construct robust control policies that would require minimum information about arrival rates. We first show that commonly used control policies such as FIFO, static priority and longest queue first (and other queue length based policies) are not robust. Indeed, we show that they are unstable in certain systems under arbitrarily low loads.

In the second part of this talk we propose a robust, generic scheme for routing new arrivals, which optimally balances server pools’ loads, without the knowledge of the flow input rates and without solving any optimization problem. The scheme is based on Shadow routing in a virtual queueing system. We study the behavior of our scheme in the Halfin-Whitt (or, QED) asymptotic regime, when server pool sizes and the input rates are scaled-up simultaneously by a factor r growing to infinity, while keeping the system load within O(√r) of its capacity.

Specifically, we first show that, in general, a system in a stationary regime has at least O(√r) average queue lengths; strategies achieving this O(√r) growth rate we call order-optimal. Next, we show that some natural algorithms, such as MaxWeight, that guarantee stability, are not order-optimal. Under the complete resource pooling condition, we show the order-optimality of the Shadow routing algorithm. We present simulation results to demonstrate the good performance and robustness of our scheme.

Joint work with Alexander L. Stolyar, Bel l Labs

"Strategies for a single product M/G/1 multi-class make-to-stock queue "
Talk by Opher Baron, University of Toronto
Wednesday, February 25th, 2009

1:00-2:00 PM, room 561

We consider a supplier with a centralized production facility that serves distinguishable markets for a single product. We study two continuously reviewed inventory systems controlled by a base-stock policy: centralized and decentralized. If different markets are prioritized, a product allocation problem arises on whether to make dispatching decisions at the beginning or end of the production. We provide the exact analysis of the decentralized priority policy with dispatching decisions postponed to the end of production. This yields the optimal base-stock levels and cost. For centralized systems, the inventory rationing and Strict Priority (SP) policies were previously considered.  While previous work expressed the corresponding optimal rationing levels and base stock level when service times are exponential, we express them for the --much more realistic and practical case-- of general service times.  Via an extensive numerical study, we show that assuming exponential (or Erlangian) service times can be very costly. We numerically demonstrate that the centralized inventory rationing policy minimizes costs and that the slightly costlier SP policy might still be useful due to its simplicity. Moreover, when, due to external factors, inventory pooling is not feasible the decentralized priority policy we suggest becomes a viable option.

"Revenue Management with Partially Refundable Fares "
Talk by Ozge Sahin
, University of Michigan
Thursday, March 5th, 2009

12:00-1:00 PM, room 561
In the first part of the talk, we introduce and analyze an inter-temporal choice model where customer valuations are uncertain and evolve over time. The model leads directly to the study of partially refundable fares. We analyze a multiple-period fluid model, and obtain structural results on the optimal fare and revenue. We show that offering partially refundable fares can significantly improve expected revenues for a monopolist selling perishable capacity. In addition, we show that partially refundable fares are socially optimal as they maximize the sum of the expected profit for the monopolist plus the expected consumers' surplus.

In the second part of the talk we consider a duopoly Stackelberg game under proportional rationing to find out if the benefits of partially refundable fares prevail under competition. Our results are mixed but sharp. First, competition with partially refundable fares is the only stable equilibrium. Moreover, the expected profits for both the follower and the leader Pareto dominate the expected profits under the restricted equilibrium where both players use fully refundable fares.  The equilibrium under partially refundable fares also Pareto dominates the non-stable equilibrium over non-refundable fares except when the capacities are very large. On the negative side, the equilibrium under partially refundable fares is no longer socially optimal.

(Joint work with Guillermo Gallego).

"Quality-Speed Conundrum: Tradeoffs in Labor-Intensive Services"
Talk by Senthil Veeraraghavan, Wharton
Wednesday, March 11th, 2009

1:00-2:00 PM, room 561

In labor-intensive services such as primary health care, hospitality and education, the quality or value provided by the service  increases with the time spent with the customer (with diminishing returns). However, longer service times (i.e., slower speed of service) also result in longer waits for customers. Thus, labor-intensive services need to make the tradeoff between service quality and service speed.

The interaction between quality and speed is critical for labor-intensive services. In a queueing framework, we parameterize the degree of labor-intensity of the service. The service speed chosen by the service-provider affects the quality of the service through its labor-intensity. Customers queue for the service based on the quality of the service, delay costs and price. We study how a service provider can make the optimal "quality-speed tradeoff" in the face of such self-interested, rational customers. Our results demonstrate that the labor-intensity of the service is a critical driver of  equilibrium price, service speed, demand, congestion in the queue and service provider revenues. We also model service rate competition among multiple servers, whose effects, we find, are very different from price competition. For instance, as the number of servers increases, the price increases and the servers become slower.

Key Words: Service Quality, Customer Behavior, Labor-Intensive Services, Queues, Cost Disease.

(Joint work with Krishnan Anand and Fazil Pac).

"The Potential for Cannibalization of New Product Sales by Remanufactured Products"
Talk by Dan Guide, Penn State
Wednesday, March 18th, 2009

1:00-2:00 PM, room 561

The potential for the cannibalization of new product sales by remanufactured versions of the same product is a central issue in the continuing development of closed-loop supply chains. We investigate the cannibalization question via auctions of new and remanufactured consumer and commercial goods. These auctions allow us to explore the impact of offering new and remanufactured products at the same time, which provides insights into the potential for cannibalization. Our results indicate that for the consumer and commercial products auctioned, there is a clear difference in the willingness-to-pay for new and remanufactured goods. For the consumer product, there is scant overlap in bidders between the new and remanufactured products, suggesting that the risk of cannibalization in this case is minimal. The commercial product exhibits some evidence of overlap in bidding behavior, exposing a greater potential for cannibalization.

"Managing Software Operations: Productivity, Task Variety and Resource Planning"
Talk by Jayashankar Swaminathan, UNC
Wednesday, April 15th, 2009

1:00-2:00 PM, room 561
In this talk, I will first present our work the explores factors that affect productivity of engineers in software maintenance operations using a dataset covering 88 individuals who worked on 5711 maintenance tasks in offshore software support services. We show that task variety and turnover have novel and interesting impact on individual productivity. Next, we model the software operations in the form of a queue. Using a combination of empirical and analytical methods, we study threshold type policies in software maintenance and demonstrate their utility in resource planning.

Joint work with Sriram Narayanan (Michigan State University) and Sridhar Balasubramanian (University of North Carolina)

"Capacity Sharing and Cost Allocation among Independent Firms in the Presence of Congestion"
Talk by Saif Benjaafar, University of Minnesota
Wednesday, April 22nd, 2009

1:00-2:00 PM, room 561

The sharing of production/service capacity among independent firms is increasingly common in industry. Capacity sharing allows firms to hedge against demand uncertainty and to achieve economies of scale. The benefits are in the form of lower costs, improved service quality, or both. Capacity sharing among independent firms raises several important questions. Is capacity sharing always beneficial to all firms? Does it always lead to a reduction in total capacity in the system? How should capacity costs be allocated among the different firms? Is capacity sharing among all the firms the best arrangement or would sharing among smaller subsets of the firms be more beneficial to particular firms? Can capacity sharing be beneficial when firms do not report truthfully private information? Is it possible to induce firms, via cost allocation alone, to disclose truthfully their private information? In this talk, we address these and other related questions in settings where production/service facilities can be modeled as queueing systems. Firms decide on capacity levels to minimize delay costs and capacity investment costs subject to service level constraints. We formulate the problem as a cooperative game among independent agents, in which is embedded a non-cooperative information reporting game. We identify various settings where the core of the game is non-empty (no subsets of firms prefer seceding from the grand coalition) and show that it is possible to design a cost allocation rule that is not only in the core but also guarantees truth telling witb truth telling being a do dominant strategy. (This work is joint with Yimin Yu, University of Minnesota, and Yigal Gerchak, Tel Aviv University)

"Recent Developments in Multi-Sourcing Inventory Models"
Talk by Jeannette Song, Duke
Wednesday, April 29th, 2009

1:00-2:00 PM, room 561

This talk reviews some recent developments in inventory models with multiple supply sources, including multiple suppliers, multiple transportation modes, and expediting options.  Multi-source inventory problems are fundamental problems in inventory management, and have been studied ever since the early stage of inventory theory. Unfortunately, these problems are intrinsically complex. Despite several decades of effort, the theory is still limited. In recent years, due to unprecedented forces of globalization and advancement of technology, companies are presented tremendous opportunities to source globally. The ability to take advantage of the available resources from any location in the world has become vital for companies to stay competitive. As such, prudent decisions in supply management are of strategic importance. In response to this, we have seen a resurgence of research interests on these issues. The purpose of this overview is to facilitate our understanding of the state-of-the-art research in this area and shed light on a few future research directions.

"Competitors as Whistle-blowers in Enforcement of Product Standards"
Talk by Erica Plambeck, Stanford GSB
Thursday, May 7th, 2009

12:00-1:00 PM, room 561

Many countries are requiring that products sold in their markets meet new safety and environmental standards. Testing products for compliance is expensive, so enforcement and compliance are necessarily imperfect. Firms have an incentive to test competitors' products, reveal violations to the regulatory authorities, and thus gain market share. This article shows that regulators should rely on competitive testing and whistle-blowing (rather than test products directly) when the social disutility from sale of a noncompliant product is moderately high. Then, each firm's compliance effort increases with its product quality and with market concentration. However, a product standard, enforced through competitive testing, encourages entry by small, low-quality firms and reduces investment by high-quality incumbents, which weakens compliance with the product standard in the long run.

(joint research with Terry A. Taylor)

"Improving the numerical performance of BLP static and dynamic discrete choice random coefficients demand estimation"
Talk by Che-Lin Su, Chicago Booth
Wednesday, May 13th, 2009

1:00-2:00 PM, room 561

The widely-used estimator of Berry, Levinsohn and Pakes (1995) produces consistent, instrumental-variables estimates of consumer preferences from a discrete-choice demand model with random coefficients, market-level demand shocks and endogenous regressors (prices). We derive numerical theory results characterizing the properties of the nested fixed point algorithm used to evaluate the objective function of BLP's estimator. We discuss several related problems with typical implementations and, in particular, cases which can lead to incorrect parameter estimates. As a solution, we recast estimation as a mathematical program with equilibrium constraints, which can be faster and which avoids the numerical issues associated with nested inner loops. The advantages are even more pronounced for forward-looking demand models where Bellman's equation must also be solved repeatedly. Several Monte Carlo and real-data experiments support our numerical concerns about the nested fixed point approach and the advantages of constrained optimization.

Joint work with J.P. Dube and J. Fox.

"Robust Revenue Management"
Talk by Guillaume Roels, UCLA
Wednesday, May 20th, 2009

1:00-2:00 PM, room 561

Revenue management models traditionally assume that future demand is unknown but can be described by a stochastic process or a probability distribution. Demand is however often difficult to characterize, especially in new or nonstationary markets. In this talk, I develop robust formulations for the capacity allocation problem in revenue management using the maximin and the minimax regret criteria under general polyhedral uncertainty sets. Our analysis reveals that the minimax regret controls perform very well on average despite their worst-case focus, and outperform the traditional controls when demand is correlated or censored. In particular, on real large-scale problem sets, the minimax regret approach outperforms by up to 2% the traditional heuristics. Our models are scalable to solve practical problems because they combine efficient (exact or heuristic) solution methods with very modest data requirements.

Joint work with Georgia Perakis.

"Approximations for Markov Perfect Industry Dynamics"
Talk by Gabriel Y. Weintraub, Columbia
Wednesday, May 27th, 2009

1:00-2:00 PM, room 561

Dynamic oligopoly models are used in industrial organization and the management sciences to analyze diverse dynamic phenomena such as investments in R&D, advertising, or capacity, the entry and exit of firms, learning-by-doing, and dynamic pricing. The applicability of these models has been severely limited, however, by the curse of dimensionality involved in the Markov perfect equilibrium (MPE) computation. In previous work, we introduced oblivious equilibrium (OE); a new solution concept for approximating MPE that alleviates the curse of dimensionality. In this work we introduce several important extensions to OE. First, in order to capture short-run transitional dynamics that may result, for example, from shocks or policy changes, we develop a nonstationary version of OE. A great advantage of nonstationary OE (NOE) is that they are much easier to compute than MPE. We present an asymptotic result that provides a theoretical justification for the use of NOE as an approximation. We also present algorithms for bounding approximation error for each problem instance. We report results from computational case studies that serve to assess the accuracy of our approximation and to illustrate applications. Our results suggest that our method greatly increase the set of dynamic oligopoly models that can be analyzed computationally. Second, we extend the definition of OE, originally proposed for models with only firm-specific idiosyncratic random shocks, to accommodate models with aggregate random shocks. This extension is important when analyzing the dynamic effects of industry-wide business cycles. We also discuss extensions of our methods to concentrated industries.

(This is joint work with C. Lanier Benkard, Przemyslaw Jeziorski, and Benjamin Van Roy)

"Inventory Rationing for a System with Heterogeneous Customer Classes"
Talk by Alan Scheller-Wolf, CMU
Wednesday, June 3rd, 2009

12:00-1:00 PM, room 561

Many retailers find it useful to partition customers into multiple classes based on certain characteristics. We consider the case in which customers are primarily distinguished by whether they are willing to wait for backordered demand. A firm that faces demand from customers that are differentiated in this way may want to adopt an inventory management policy that takes advantage of this differentiation. We propose doing so by imposing a critical level (CL) policy: When inventory is at or below the critical level demand from those customers that are willing to wait is backordered, while demand from customers unwilling to wait will still be served as long as there is any inventory available. This policy reserves inventory for possible future demands from impatient customers by having other, patient, customers wait.

We consider a system that operates a continuous review replenishment policy, in which a base stock policy is used for replenishments. Demands as well as lead times are stochastic. We model the system as a continuous time Markov Chain. We develop an efficient solution procedure, based on decomposition and aggregation techniques, to determine the average infinite horizon performance of a given CL policy. Our procedure is precise to an arbitrary level of accuracy, and thus we use it as a basis for an efficient algorithm to determine the optimal CL policy parameters.

We use our algorithm in a numerical study to compare the cost of the optimal CL policy to the globally optimal state-dependent policy along with two alternative, more naive, policies. The CL policy is slightly over 2% from optimal, whereas the alternative policies are 7% and 27% from optimal. We also study the sensitivity of our policy to the coefficient of variation of the lead time distribution, and find that the optimal CL policy is fairly insensitive, which is not the case for the globally optimal policy.

Fall 2008

 Dimitris Bertsimas MIT September 24th Terrence August UCSD September 29th Kristin Fridgeirsdottir LBS October 6th Charles Corbett UCLA October 20th Ramandeep Randhawa UT, Austin October 27th Costis Maglaras Columbia November 3rd Ed Kaplan Yale School of Management November 10th Gerard Cachon Wharton November 17th Michael Harrison Stanford December 1st

"Progress, Perspectives, and Opportunities"
Talk by Dimitris Bertsimas, MIT
September 24th, 2008

In recent years the availability of massive amounts of electronically available data involving millions of people and the development of new data mining algorithms present an exciting new opportunity for Operations Research to have a significant impact in health care. We discuss our research efforts in assessing the risk of patients, their quality of care and also propose a new data based assessment for cancer research. We further discuss further research directions.

"Let the Pirates Patch? An Economic Analysis of Software Security Patch Restrictions"
Talk by Terrence August, UCSD
September 29, 2008

We study the question of whether a software vendor should allow users of unlicensed (pirated) copies of a software product to apply security patches. We present a joint model of network software security and software piracy and contrast two policies that a software vendor can enforce: (i) restriction of security patches only to legitimate users or (ii) provision of access to security patches to all users whether their copies are licensed or not. We find that when the software security risk is high and the piracy enforcement level is low, or when tendency for piracy in the consumer population is high, it is optimal for the vendor to restrict unlicensed users from applying security patches. When piracy tendency in the consumer population is low, applying software security patch restrictions is optimal for the vendor only when the piracy enforcement level is high. If patching costs are sufficiently low, however, an unrestricted patch release policy maximizes vendor profits. We also show that the vendor can use security patch restrictions as a substitute to investment in software security, and this effect can significantly reduce welfare. Furthermore, in certain cases, increased piracy enforcement levels can actually hurt vendor profits. We also show that governments can increase social surplus and intellectual property protection simultaneously by increasing piracy enforcement and utilizing the strategic interaction of piracy patch restrictions and network security. Finally, we demonstrate that, although unrestricted patching can maximize welfare when the piracy enforcement level is low, contrary to what one might expect, when the piracy enforcement level is high, restricting security patches only to licensed users can be socially optimal
.

Talk by Kristin Fridgeirsdottir, LBS
October 6th, 2008

The Internet is currently the fastest growing advertising medium. Online advertising brings new opportunities and has many different characteristics from advertising in traditional media that support more quantitative decision making. We consider an operational problem of a web publisher that generates revenues by selling advertising space on its website.

The web publisher faces the problems of pricing and managing capacity of the advertising space with the objective of maximizing the revenues generated. The advertisers approach the web publisher, request their ad to be displayed to a certain number of visitors to the website, and are charged according to the so-called pay-per-impression pricing scheme. We suggest a queueing model for the operation of the web publisher considering the uncertainty of both the demand (the advertisers) and the supply (the visitors) with the advertising slots acting as servers. We consider two cases: i) the advertisers are willing to wait before their advertising campaign is started; ii) the advertisers are not willing to wait. For the first case we show that the resulting multi-server queueing model has the same properties as a known single server queueing model. We derive an approximation for the waiting time that performs significantly better than existing ones. For the second case we derive a closed-form solution of the probability distribution for the number of advertisers in the system, which enables us to fully characterize the steady-state properties of the system. In both cases, the queueing models developed bring some new distinctive features and we compare them to the corresponding models in the literature.

Having characterized the operation of the web publisher, we study its revenue maximization problem and determine the optimal advertising price. We provide managerial insights such that from an operational point of view the optimal price should be higher when advertisers request more impressions, which goes against the quantity discount common in practice. Finally, we extend our models to incorporate multiple types of advertisers.

"Evidence of Biases in the Adoption of Energy Efficiency Initiatives by Small and Medium Sized Firms"
Talk by Charles Corbett, UCLA
October 20th, 2008
This study finds evidence of biases in the adoption of energy efficiency initiatives. We identify the biases using field level data on over 100,000 recommendations made to more than 13,000 small and medium sized firms. Managers are observed to be myopic in evaluating energy efficiency initiatives. They are influenced by initial costs instead of overall returns and they use high investment hurdle rates when evaluating such initiatives. A probit instrumental variables model is used to find that the adoption of a recommendation depends not only on the economic drivers and the characteristics of a recommendation but also on the sequence in which the recommendations are presented: adoption rates are higher for those initiatives appearing early on in a list of recommendations. Further, theory predicts that adoption rates will fall when decision makers are provided a large number of recommendations, however we find that adoption is not influenced by the number of recommendations provided. The study draws implications for enhancing adoption of energy efficiency initiatives and for other decision contexts where a collection of process improvement recommendations are made to firms. This study highlights previously unobserved decision biases in the OM literature. Additionally, the study uses field level data to highlight behavioral issues and thus differs from the majority of behavioral operations literature which uses experiments.

"Capacity Planning in Service Systems with Arrival Rate Uncertainty: Safety Staffing Principles Revisited "
Talk by
Ramandeep Randhawa, UT Austin
October 27th, 2008
We study a capacity sizing problem in service systems with uncertain arrival rates; telephone call centers are canonical examples of such systems. The objective is to choose a staffing level that minimizes the sum of personnel costs and abandonment/waiting time costs.  We formulate a simple fluid analogue, which is in essence a newsvendor problem, and demonstrate that the solution it prescribes performs remarkably well. In particular, the gap between the performance of the optimal staffing level and that of our proposed prescription is independent of the "size" of the system, i.e., it  remains bounded as the system size (demand volume) increases. This stands in contrast to the more conventional theory that applies when arrival rates are known, and commonly used rules-of-thumb predicated on it. Specifically,  in that setting the difference between the optimal performance and that of the fluid solution diverges at a rate proportional to square-root of the size of the system. One manifestation of this is the celebrated square root safety staffing principle that dates back to work of Erlang, which augments solutions of the deterministic analysis with additional servers of order square root the volume of demand. In our work, we establish that this type of prescription is needed only when arrival rates are suitably "predictable."

"Dynamic pricing with financial milestones: feedback pricing policies"
Talk by Costis Maglaras, Columbia
November 3rd, 2008

We study a revenue maximization problem for a seller that is subject to a set of financial and sales milestone constraints. The goal is to choose a pricing policy that satisfies these constraints over time in a revenue maximizing manner, and the focus is in settings with limited or no market information. The motivating application is pricing of large scale real estate projects.

"Estimating HIV incidence in the United States"
Talk by Ed Kaplan, Yale School of Management
November 10th, 2008

Prior estimates of HIV incidence in the United States derived from assumptions that, though not unreasonable at the time first employed, have become increasingly untenable.  For years, the CDC reported 40,000 new HIV infections annually in the US.  We developed new probability models and statistical procedures to estimate the chance that a newly-infected person will be detected as recently infected.  Together with the development of an HIV test that detects recent infection, these procedures have enabled the CDC to estimate annual HIV incidence in the United States from HIV/AIDS surveillance data.

"Drivers of Finished Goods Inventory in the U.S. Automobile Industry"
Talk by Gerard Cachon, Wharton
November 17th 2008

Automobile manufacturers in the U.S. supply chain exhibit significant
differences in their days-of-supply of finished vehicles (average inventory divided by average daily sales rate). For example, from 1995 to 2004, Toyota consistently carried approximately 30 fewer days-of-supply than General Motors. This suggests that Toyota’s well documented advantage in manufacturing efficiency, product design and upstream supply chain management extends to their finished-goods inventory in their downstream supply chain from their assembly plants to their dealerships. Our objective in this research is to measure for this industry the effect of several factors on inventory holdings. We find that two factors, the number of dealerships in a manufacturer’s distribution network and a manufacturer’s production flexibility, explain essentially all of the difference in finished goods inventory between Toyota and three other makes, Chrysler, Ford and General Motors.

"Dynamic pricing to learn and earn in a logit model context"
Talk by Michael Harrison, Stanford
December 1st, 2008

Motivated by applications in financial services, we consider the following customized pricing problem. A seller of some good or service (like auto loans or small business loans) confronts a sequence of potential customers numbered 1, 2, …, T. These customers are drawn at random from a population characterized by logit parameters a and b, where b > 0: if the seller offers price p, the probability of a successful sale is

r(p) = 1/(1+e^(a+bp) ) ;

and if the sale is successful, the profit realized by the seller is p(p) = p – c, where c > 0 is known. If the parameters a and b were also known, then the problem of finding a price p* to maximize r(p)p(p) would be simple, and the seller would offer price p* to each of the T customers. We consider the more complicated case where a and b are fixed but initially unknown: given a prior joint distribution for a and b, the decision maker wants to choose a sequence of prices to maximize expected profit earned from the T potential customers. Of course, each price decision involves a trade-off between refined parameter estimation (learning) and expected immediate profit (earning).

Fall 2007 - Spring 2008

 Xuanming Su UC Berkeley September 26th Yehuda Bassok USC October 10th Robert Freund MIT October 17th Oguzhan Alagoz University of Wisconsin November 14th Terry Taylor UC Berkeley November 28th Kamalini Ramdas Virginia April 9th Georgia Perakis MIT April 16th Eric Johnson Dartmouth April 30th Ludo Van der Heyden INSEAD May 21st

"Bounded Rationality in Newsvendor Models"
Talk by Xuanming Su, UC Berkeley
September 26th, 2007

Many theoretical models adopt a normative approach and assume that decision-makers are perfect optimizers. In contrast, this paper takes a descriptive approach and considers bounded rationality, in the sense that decision-makers are prone to errors and biases. Our decision model builds upon the quantal choice model: while the best decision need not always be made, better decisions are made more often. We apply this framework to the classic newsvendor model and characterize the ordering decisions made by a boundedly rational decision-maker. We identify systematic biases and offer insight into when over-ordering and under-ordering may occur. We also investigate the impact of these biases on several other inventory settings that have traditionally been studied using the newsvendor model as a building block, such as supply chain contracting, the bullwhip effect, and inventory pooling. We find that incorporating decision noise and optimization error yields results that are consistent with some anomalies highlighted by recent experimental findings.

"Inventory Assortment and Substitution Problems"
Talk by Yehuda Bassok, USC
October 10th, 2007

We consider general substitution problem, in which consumers can choose one of N variants. We start with a choice model that ranks the preference of each consumer. The preferences of each of the consumers are not known to the retailer and thus, he/she must assume that the demand for each variant is random. We are able to calculate the retailer’s optimal stocking policy. We show that the role of safety stock is to hedge against the uncertainty in the market size but not the uncertainty in the demand for each of the variants. We then move the describe competition between retailer. Again, we are able to characterize the equilibrium inventory levels and assortments. When competition is considered safety stock may be carried even if market size is known. But, in most practical cases if market size is known but consumer choice is random no safety stock is carried with a very high probability.

"Randomized Methods for Solving Convex Problems:  Some Theory and Some Computational Experience"
Talk by Robert M. Freund, MIT
October 17th, 2007

In contrast to conventional continuous optimization algorithms whose iterates are computed and analyzed deterministically, randomized methods rely on stochastic processes and random number/vector generation as part of the algorithm and/or its analysis. Whereas randomization in algorithms has been a part of research in discrete optimization for at least the last 20 years, randomization has played at most a minor role in algorithms for continuous convex optimization, at least until recently. This talk will focus on two recent randomization-based algorithms for convex optimization: a method by Bertsimas and Vempala based on cuts at the center of mass, and a new method by Belloni and Freund that “pre-conditions” a standard interior-point algorithm using random walks. For the latter, we report very promising computational results on medium-sized conic problems.

"Optimal Policies for the Acceptance of Living- and Cadaveric-Donor Livers"
Talk by Oguzhan Alagoz UC Berkeley
November 14th, 2007
The talk is based on the papers: "Determining the Acceptance of Cadaveric Livers Using an Implicit Model of the Waiting List" and "The Optimal Timing of Living-Donor Liver Transplantation"

Transplantation is the only viable therapy for end-stage liver diseases (ESLD) such as hepatitis B. In the United States, patients with ESLD are placed on a waiting list. When organs become available, they are offered to the patients on this waiting list. This study focuses on the decision problem faced by these patients: which offer to accept and which to refuse? A recent analysis of liver transplant data indicates that 60% of all livers offered to patients for transplantation are declined.

We formulate this problem as a discrete-time Markov decision process (MDP). We analyze three MDP models, each representing a different situation. The Living-Donor-Only Model considers the problem of optimal timing of living-donor liver transplantation, which is accomplished by removing an entire lobe of a living donor's liver and implanting it into the recipient. The Cadaveric-Donor-Only Model considers the problem of accepting/refusing a cadaveric liver offer when the patient is on the waiting list but has no available living donor. The Living-and-Cadaveric-Donor Model is the most general model, which combines the first two models, in that the patient is both listed on the waiting list and also has an available living donor. The patient can accept the cadaveric liver offer, decline the cadaveric liver offer and use the living-donor liver, or decline both and continue to wait.

We derive structural properties of all three models, including several sets of conditions that ensure the existence of intuitively structured policies such as control-limit policies. The computational experiments use clinical data, and show that the optimal policy is typically of control-limit type.

"Incentives for Retailer Forecasting: Rebates versus Returns"
Talk by Terry Taylor, UC Berkeley
November 28th 2007
Abstract: This paper studies a manufacturer that sells to a newsvendor retailer who can improve the quality of her demand information by exerting costly forecasting effort. In such a setting, contracts play two roles: providing incentives to influence the retailer's forecasting decision, and eliciting information obtained by forecasting to inform production decisions. We focus on two forms of contracts that are widely used in such settings and are mirror images of one another: a rebates contract which compensates the retailer for the units she sells to end consumers, and a returns contract which compensates the retailer for the units that are unsold. We characterize the optimal rebates contract, the optimal returns contract, and the manufacturer's preferred contractual form. We show that the retailer, manufacturer and total system may benefit from the retailer having inferior forecasting technology. (Joint work with Wenqiang Xiao.)

"An Empirical Investigation into the Tradeoffs that Impact On-Time Performance in the Airline Industry"
Talk by Kamalini Ramdas, Virginia
April 9th 2008

We investigate the tradeoff between aircraft capacity utilization and on-time performance, a key measure of airline quality. Building on prior theory and empirical work we expect that airlines that are close to their productivity or asset frontiers would face steeper tradeoffs between utilization and performance, than those that are further away.
We test this idea using a detailed 10-year airline industry data set, drawing on queuing theory to disentangle the confounding effects of variance in travel time and capacity flexibility along an aircraft's route. We find that greater aircraft utilization results in higher delays, with this effect being worse for airlines that are close to their asset frontiers in terms of already being at high levels of aircraft utilization. Also, we find that the negative effect of utilization on delays is greater for aircraft that face higher variability in travel time along their routes, and is lower for aircraft on routes with higher capacity flexibility - in terms of the ability to substitute in a different aircraft for a particular flight than the one that was originally scheduled. Additionally, we examine how load factor, a measure of how full an airline's flights are and therefore a key revenue driver, affects on-time performance. Our analysis enables us to explain differences in on-time performance across airlines as a function of key operational variables, and to provide insight on how airlines can better manage their on-time performance levels and aircraft utilization.

"Profit Loss and Loss of Efficiency due to Competition"
Talk by Georgia Perakis, MIT
April 16th 2008

We consider an oligopoly setting where more than two firms are competing on products that are gross-substitutes or complements. We study the profit loss due to competition (i.e., comparison of the total profit in the industry between centralized and decentralized settings) for Bertrand (price-setting) competition and for Cournot (quantity-setting) competition.  Our goal is to understand how the presence of competition affects the overall profit as well as the total surplus in the industry and what the key drivers of the inefficiencies that arise due to competition are.

Our research to date suggests that for gross substitutes the "market power" of each firm (in terms of how much they can each affect the total demand in the market with their decisions) play an important role. On the other hand, for complement products, the number of firms competing in the market and the number of products produced by each firm also play a role.  To achieve this we develop bounds on how bad the total profit in the industry can become due to competition. We further discuss a setting where each firm is selling several products and is faced with a variety of constraints on the prices or quantities of the products it offers. We provide general bounds that are independent of the constraints of the game and as a result apply to a large class of settings. Our results for example, apply to competitive settings where firms sell various versions of the same product line and want to make sure the prices between each version of the product does not vary by a lot.

Furthermore, we consider more general measures of efficiency such as the total surplus in the market. We further generalize our results to classes of nonlinear demand functions.
(joint work with A. Farahat and J. Kluberg)

"Inadvertent Disclosure—Information Risk and Governance in the Financial Supply Chain"
Talk by Eric Johnson, Dartmouth
April 30th 2008

Abstract: Firms face many different types of information security risk. Inadvertent disclosure of sensitive business information represents one of the largest classes of recent security breaches. We examine a specific instance of this problem – inadvertent disclosures through peer-to-peer file-sharing networks. We characterize the extent of the security risk for a group of large financial institutions using a direct analysis of leaked documents. We also characterize the threat of loss by examining search patterns in peer-to-peer networks. Our analysis demonstrates both a substantial threat and vulnerability for large financial firms. We find a statistically significant link between leakage and firm employment base.  Further, we address information governance, which is an underlying factor in inadvertent disclosure. We propose a governance structure based on controls and incentives, where employees’ self-interested behavior can result in firm-optimal use of information. Using a game-theoretic approach, we show that an incentives-based policy with escalation can control both overentitlement and underentitlement while maintaining the flexibility needed in dynamic business environments.

"A Model of Fair Process and Its Limits"
Talk by Ludo Van der Heyden, INSEAD
May 21st 2008
Fair process research has shown that people care not only about outcomes, but also about the process that produces these outcomes. For a decision process to be seen as fair, the people affected must have the opportunity to give input and possibly to influence the decision, and the decision process and rationale must be transparent and clear. Existing research has shown empirically that fair process enhances both employee motivation and performance in execution.

In this talk, we review the fair process literature and present a more operational definition of fair process, that is motivated both by the literature on fair process and that on decision making.  We present empirical evidence that supports this definition in hte framework of a study of innovation practices in 15 German manufacturing sites.

We conclude with presenting an analytical model of fair process in a principal-agent (i.e., manager-employee) context, rooted in psychological preferences for autonomy and fairness. This model addresses the question as to why fair process is so often violated in practice. The associated paper breaks new ground by analytically examining the subtle trade-offs involved.
paper #1 paper#2

Spring 2007

 Jeremie Gallien MIT March 28th Candace Yano U of C at Berkeley April 11th Pinar Keskinocak Georgia Tech April 25th Phil Lederer University of Rochester May 16th

View past seminars

"Inventory Management of a Fast-Fashion Retail Network"
Talk by Jeremie Gallien, MIT
March 28, 2007 - 2:15 PM, Room 561

Fast-fashion retailers (e.g. Zara, H&M) have met some success responding to volatile demand trends through frequent introductions of new garments produced in small series. An important associated operational problem is the allocation over time of a limited amount of inventory across all stores in their network. I will present stochastic and deterministic models developed in collaboration with Zara to address this challenge, then discuss the implementation and impact of this work.

"Incorporating Risk Considerations into Inventory Models and Supply Contracts"
Talk by Candace Yano, U of C at Berkeley
April 11, 2007 - 1:30 PM, Room 561

When retailers purchase goods, they often make decisions about purchase quantities or enter into supply contracts that aim to mitigate their risk. Very little of the research literature on inventory models and supply contracts explicitly considers risk, however. In this talk, we discuss two procurement models in which the decision-makers are concerned about risk. The first model considers a buyer and supplier, both of whom are risk averse but have some tolerance for risk. We show that several popular contract forms do not take full advantage of both parties tolerance for risk and therefore cannot optimize supply chain profits. We then propose a contract structure that overcomes these limitations.

In the second model, we consider a retailer who buys and then sells a product. The retailer is concerned about meeting corporate profit targets and this is the cause of his risk aversion. We show that under the standard accrual method of accounting, optimal inventory policies that properly account for his risk aversion have a relatively simple structure, but under the cash-basis method of accounting, optimal inventory policies may be extremely complicated and may have unintended consequences.

(Various parts of this research were done in collaboration with Shiming Deng of Oracle, Inc., Houmin Yan of the Chinese University of Hong Kong, and Hanqin Zhang of the Chinese Academy of Sciences.)

"Coordination of Marketing and Production for Price and Leadtime Decisions" and "Centralized vs. Decentralized Competition for Price and Lead-time Sensitive Demand"
Talk by Pinar Keskinocak, Georgia Tech
April 25, 2007 - 1:30 PM, Room 561

We study decentralized price and leadtime decisions made by the marketing and production departments, respectively, where the customers are sensitive both to the quoted price and the lead time.

First, we consider a single firm (in a monopoly setting) and analyze the inefficiencies that are due to the decentralization of price and leadtime decisions. In the decentralized setting, the total demand generated is larger, leadtimes are longer, quoted prices are lower, and the firm profits are lower as compared to the centralized setting. We show that coordination can be achieved using a transfer price contract with bonus payments. We also provide insights on the sensitivity of the optimal decisions with respect to market characteristics, sequence of decisions, and the firm’s capacity level.

Next, we extend our analysis to a competitive setting. We study two firms that compete based on their price and lead-time decisions in a common market. We explore the impact of the decentralization under competition comparing three scenarios: (i) Both firms are centralized, (ii) only one firm is centralized, (iii) both firms are decentralized. We find that under intense price competition, firms may suffer from a decentralized strategy, particularly under high flexibility induced by high capacity, where revenue based sales incentives motivate sales/marketing for more aggressive price cuts resulting in eroding margins. On the other hand, when price competition in the market is less intense than lead-time competition, a decentralized decision making strategy may dominate a centralized decision making strategy.

This is joint work with Pelin Pekgun and Paul Griffin.

"Complementarity in Improvement Programs"
Talk by Phil Lederer, University of Rochester
May 16th, 2007 - 1:30 PM, Jacobs Room 165

During the past two decades firms have adopted many types of functional improvement programs. In operations programs such as TQM, MRP, JIT have been adopted to reduce cost, increase quality and improve customer service. In marketing, marketing research programs have been used to better understand customer tastes. In accounting, ABC, and other programs to determine more accurate product costs have been implemented. Although many of these programs have been studied there has been little work on the joint effects (if any) between these activities. Indeed, the broadest claim of the continuous improvement movement is that all improvement activities are complementary, meaning that there are increasing gains to improvement programs. Papers such as Milgrom and Roberts (1990) seem to imply that most modern manufacturing innovations are complementary to each other.

This research studies the impact of combinations of improvement activities on firm performance. We study three types of improvement programs: process improvement, marketing research and cost estimation. Process improvements lower the variable cost of production, increases product quality, or cuts lead time, marketing research programs help us to identify segments and price accordingly, and cost estimation programs allow better cost estimation for pricing and control. We show that improvement programs may be complements or substitutes. This means that improvement programs can increase or decrease the desirability of the other programs. In particular, we show conditions that imply that marketing research and cost estimation programs are substitutes to each other. One of these conditions is that production technology displays increasing returns to scale, a characteristic found in queuing and inventory driven production systems. A model of a production system with queuing demonstrates the results. Further, our work demonstrates that process improvement programs reduce losses due to decentralized control of firms. The implication is that process improvement programs encourage decentralized organization of other improvement efforts.

Fall 2006

 Vishal Gaur NYU September 27 Awi Federgruen Columbia October 18 Tava Olsen Washington-St. Louis October 25 Ramesh Johari Stanford November 1 Ioana Popescu INSEAD November 9 Ozalp Ozer Stanford January 17

"Inventory Turnover Performance in the U.S. Retail Sector"
Talk by Vishal Gaur, NYU
September 27, 1:30 PM, Room 561

We report results from published and ongoing empirical studies in inventory turnover in the U.S. retail sector, employing financial data for about 400 public-listed retailers across ten retail segments for the years 1985-2004. In the first part of this research, we develop benchmarks for inventory turns for different types of retailers, and show the correlation of inventory turns with other performance measures such as gross margin and capital intensity. We also show the effects of demand uncertainty, sales growth rate, and size on inventory turns. In the second part of this research, we show the effect of inventory turnover performance of retailers on their financial performance, measured by their stock returns. Using a case study, we also discuss possible reasons why inventory turnover may be a predictor of financial performance.

"Safeguarding Strategic Supplies: Planning for Disaster"
Talk by Awi Federgruen, Columbia
October 18, 1:00 PM, Room 561

Standard supply chain management texts discuss the benefits of consolidating the set of suppliers in the chain. These benefits include economies of scale in the production costs as well as statistical economies of scale due to the pooling of demand risks. Recently, many corporations and governments, alike, have recognized a variety of risks associated with external disruptions of the supply process. These provide a powerful argument against (maximal) consolidation. Such disruptions may arise because of “natural” disasters, e.g. fires in production plants or the need to shut down a facility because of violations of quality regulations or standards. Disruptions may also occur because of labor strikes, or planned acts of sabotage, resulting from terror attacks among others. While these disruptions may be rare, their consequences can be catastrophic for an individual firm as well as for a region or a country as a whole.
We analyze a planning model for a firm or public organization which needs to cover uncertain demand for a given item by procuring supplies from multiple sources. The necessity to employ multiple suppliers arises from the fact that when an order is placed with any of the suppliers, only a random fraction of the order size is useable. The model considers a single demand season, with a given demand distribution, where all supplies need to be ordered simultaneously before the start of the season. The suppliers differ from each other in terms of their yield distributions, their procurement costs and capacity levels.
The planning model determines which of the potential suppliers are to be retained and what size order is to be placed with each. We consider two versions of the planning model: in the first, (SCM), the orders must be such that the available supply of useable units covers the random demand during the season with (at least) a given probability. In the second version of the model, (TCM), the orders are determined so as to minimize the aggregate of procurement costs and end-of-the season inventory and shortage costs. In the classical inventory model with a single, fully reliable, supplier, these two models are known to be equivalent, but the equivalency breaks down under multiple suppliers with unreliable yields.
Determining the optimal set of suppliers, the aggregate order and its allocation among the suppliers, on the basis of the exact shortfall distribution, is prohibitively difficult. We have therefore developed two approximations for the shortfall distribution. While both approximations are shown to be highly accurate, the first, based on a Large Deviations Technique (LDT), has the advantage of resulting in a rigorous upper bound for the required total order. The second approximation is based on a Central Limit Theorem (CLT) and is shown to be asymptotically accurate, while the order quantities determined by this method are asymptotically optimal, as the number of suppliers grows. Most importantly, this CLT-based approximation permits many important qualitative insights.
Based on the CLT-approximation, we develop, for both the (SCM) and (TCM), a highly efficient procedure which generates the optimal set of suppliers as well as the optimal orders to be assigned to each. Most importantly, these procedures generate a variety of important qualitative insights, for example, regarding which sets of suppliers allow for a feasible solution, both when they have ample supply, and when they are capacitated.

"Service Level Agreements in Call Centers: Perils and Prescriptions" (pdf/Kellogg ID required)
Talk by Tava Olsen, Washington-St. Louis
October 25, 1:30 PM, Room 561

A call center with both contract and non-contract customers was giving priority to the contract customers only in off-peak hours, precisely when having priority was least important. Using asymptotic analysis we show why this is indeed rational behavior on the part of the call center and what the implications are for customers. We then suggest other contracts that do not result in this type of undesirable behavior from a contract customer’s perspective. We compare the performance of the different contracts in terms of mean, variance, and outer percentiles of delay for both customer types using both numerical and asymptotic heavy-traffic analyses.

"Investment and Market Structure in Congestible Services"
Talk by Ramesh Johari, Stanford
November 1, 1:30 PM, Room 561

We consider investment and market structure in a model of congestion-sensitive service provision. Our starting point is a simple model of network routing that has received a great deal of attention in the engineering community, the so-called "selfish routing" model. A continuum of users wish to send data from source to destination, and can choose from several parallel routes. Each route is owned by an independent network provider that sets a price per unit flow along the route. A user's overall disutility is measured as the sum of price and congestion experienced along the chosen route. In contrast to previous work, we consider a model where providers can invest in their routes, to minimize the impact of this congestion externality.
We investigate this model through the Nash equilibria of the pricing and investment game played by providers. We find that returns to investment and the timing of strategic decisions are critical determinants of the outcome of the game. For a broad range of models for which (1) providers choose prices and investments simultaneously, and (2) the model exhibits nonincreasing returns to investment, we show that if a pure strategy Nash equilibrium exists, it is unique, symmetric, and efficient; we also establish conditions for existence of pure strategy Nash equilibrium in special cases. This result does not hold if either (1) or (2) are violated, and we discuss these scenarios as well. We also investigate several extensions, including modeling the entry of providers into the market. We will emphasize the implications of our results for key issues in telecommunications, including wireless Internet service penetration and the viability of source-directed routing.
This is joint work with Gabriel Weintraub and Ben Van Roy.

"Revenue Management Models in Media Broadcasting"
November 9, 1:30 PM, Room 561

An important challenge faced by media broadcasting companies is how to allocate limited advertising space across multiple clients and markets (upfront/scatter) in order to maximize profits. We develop stylized optimization models of inventory allocation under audience uncertainty. At the strategic planning level, we provide simple solutions for upfront market allocation and contracting for multiple clients. In a dynamic setting, we investigate make-goods allocation during the scatter market, under reversible and irreversible commitment regimes. Our results hold under general performance metrics, and bring out interesting parallels with standard inventory and revenue management frameworks.

"Competing on Time: A Framework for New Product Introduction Decision in the High Technology Industry"
Talk by Ozalp Ozer, NYU
January 17, 1:30 PM, Room 561

In this presentation, we will outline the challenges and uncertainties associated with bringing a new product to market. To do so, we will focus on a major global high-technology company located in the Bay Area and discuss their challenges related to new product introductions (NPI). The high technology industry is characterized by lightning speed in technology innovation, intense competition and relentless price erosion. It is, therefore, critical to bring the new product to the market at the right time to ensure profitability.

We will present our OR based modeling framework that is used to help a major global high-technology company make effective time-to-market decisions. Our model solves the problem in two nested phases: a design phase and a mass production phase. The design phase is modeled as an optimal stopping problem where decision to "enter or not" is made. The solution of the design stage affects the mass production phase. This second phase is modeled as a stochastic production control problem where production decisions are made. We will characterize an optimal policy for market timing, an optimal policy for production decisions and how and why they are amenable for implementation. We will also discuss the techniques used to solve this large-scale stochastic dynamic program, including how structural results enabled us to improve computational efficiency.

Finally, we will discuss how this project and the resulting software enabled various functional areas, such as Finance, Manufacturing, Marketing and R&D within the firm to communicate and jointly address this strategic question. If time permits, we will share our perspectives on the challenges and key success factors of working at the university/industry boundary

Spring 2006
The following visited NU in Spring 2006 for the Kellogg Operations Seminar series. Please click on a date for more information about a particular talk.

 David Yao Columbia April 7 Bert De Reyck and Janne Gustafsson London Business School April 14 Marty Reiman Bell Labs May 3 Assaf Zeevi Columbia May 10 Victor DeMiguel London Business School June 7

Please check individual listings for locations of the talks.

"Dynamic Resource Control in a Stochastic Network: Limiting Regimes and Asymptotic Optimality"
Talk by David Yao, Columbia
April 7, 12:05 PM, Jacobs Rm. 561

We study a class of stochastic networks with concurrent occupancy of resources, which, in turn, are shared among jobs. For example, streaming a video on the Internet requires bandwidth from all the links that connect the source of the video to its destination; and the capacity of each link is shared, according to a certain protocol, among all source-destination connections
that involve this link. Another example is a multi-leg flight on an airline reservation system: to book the flight, seats on all legs must be committed simultaneously. We focus on a class of dynamic resource control on this
type of networks, where the link capacities are allocated among the job classes, in each state of the network, according to the solution to a
utility maximization problem. We derive fluid and diffusion limits of the network under this type of (myopic) control policy. Furthermore, we identify a cost function that is minimized in the diffusion regime, thereby justifying the asymptotic optimality of the control. (Joint work with Hengqing Ye.)
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"Valuing Risky Projects using Mixed Asset Portfolio Selection Models"
Talk by Bert De Reyck, London Business School;
and Janne Gustafsson, Cheyne Capital Management Limited
April 14, 11 AM, Jacobs Rm. 561

We examine the valuation of projects in a setting where an investor can invest in a portfolio of private projects as well as in securities in financial markets, but where exact replication of project cash flows in financial markets is not necessarily possible. We consider both single-period and multi-period models, and develop an inverse optimization procedure for valuing projects in this setting. We show that the valuation procedure exhibits several important analytical properties, for example, that project values for a mean-variance investor converge towards prices given by the capital asset pricing model. We also conduct several numerical experiments.
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"An Asymptotically-Optimal Dynamic Admission Policy for a Revenue Management Problem"
Talk by Marty Reiman, Bell Labs
May 3, 4:00 PM, Jacobs Rm. 166

We consider the following canonical revenue management problem, which has been analyzed in the context of airline seat inventory control and has applications to other service industries and supply chain management. There are several resource types (legs), each of which has a fixed capacity (number of seats). There are several customer classes (routes), each with an associated arrival process, price and resource consumption vector. The aim is to make dynamic accept/reject decisions at customer arrival epochs to maximize the total expected revenue obtained over the finite horizon [0,T] subject to not exceeding the capacity of any of the resources.
We introduce a control policy motivated by fluid and diffusion limits (as the resource capacities and arrival rates grow large). Our control policy makes an initial resource allocation decision based on solving a linear program (LP). The solution of the LP yields the fraction of arrivals of each class to accept. We then form a ‘trigger function’ based on the difference between the actual and expected number of accepted customers of each class. When this trigger function exceeds a pre-set threshold a reoptimization is performed: An LP involving the remaining resource capacities and remaining time is solved. The solution of this LP is then used to control admissions over the remainder of the horizon. We show that this policy is asymptotically optimal on diffusion scale, a property that is not shared by other approaches such as booking limits and bid price control.
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"Blind Nonparametric Revenue Management"
Talk by Assaf Zeevi, Columbia
May 10, 12:00 PM, Jacobs Rm. 561

In most revenue management studies one assumes the decision maker knows the manner in which consumers react to prices. The most typical way to express this fact is to assume the demand function is known, and it is just as common to posit that it admits a simple parametric structure. So what happens if none of this holds?
To investigate this question we consider a general class of network revenue management problems, where the objective is to price multiple products so as to maximize expected revenues over a finite sales horizon. The decision maker observes realized demand over time, but is otherwise `blind'' to the underlying demand function which maps prices into the instantaneous demand rate. Few structural assumptions are made with regard to the demand function, in particular, it need not admit any parametric representation. We introduce a general method for solving such blind revenue management problems which involves the classical trade off between exploration and exploitation. To evaluate the performance of the proposed method we compare the revenues it generates to those corresponding to the optimal dynamic pricing policy that knows the demand function a priori. While that may seem as a lofty benchmark, we prove that as the sales volume grows large the revenue loss is guaranteed to be small. A more loose interpretation might run as follows: in problems that involve high sales volume, the value of “full information” (or penalty for “blind” decision making) is not as significant as one might guess.
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"Portfolio Selection with Robust Estimates of Risk"
Talk by Victor DeMiguel, London Business School
June 7, 12:00 PM, Jacobs Rm. 561

It is well-known that mean-variance portfolios constructed using the sample mean and covariance matrix of asset returns perform poorly out-of-sample due to estimation error. Moreover, it has been demonstrated that estimation error in the sample mean is, for most real-world datasets, much larger than that in the sample covariance matrix. For this reason, recent research has focused on the minimum-variance portfolio, which relies only on estimates of the covariance matrix and thus usually performs better out-of-sample than mean-variance portfolios. But even minimum-variance portfolios are still quite sensitive to estimation error and have unstable weights that fluctuate substantially over time.
Jagannathan and Ma (2003} show that imposing shortselling constraints can help to alleviate this difficulty. In this paper, we explore a different mechanism to combat estimation error. Concretely, we show how to compute the policy that minimizescertain robust estimator of portfolio risk by solving a nonlinear program. We also give an analytical bound on the sensitivity of the resulting portfolio weights to changes in the distributional assumptions. Finally, our out-of-sample numerical results show that the portfolio weights of the proposed policies are more stable than those of the minimum-variance policy and that they usually perform better in terms of Sharpe ratio. Moreover, although the imposition of shortselling constraints does improve the performance of the minimum-variance policy, the proposed robust policies are more stable and usually perform better even in the presence of constraints.
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Winter 2006
The following speakers visited NU in winter 2006 for the Kellogg Operations Seminar series. Please click on a date for more information about a particular talk.

 Mark Lewis Cornell Jan. 18 Michael Lapre Vanderbilt Jan. 25 Vinod Singhal Georgia Tech Feb. 8 Anton Kleywegt Georgia Tech Feb. 15 David Gamarnik MIT Feb. 22 Noah Gans Wharton March 8 Sunil Kumar Stanford March 15

"Resource flexibility in manufacturing systems"
Talk by Mark Lewis, Cornell
Jan. 18, 12 PM, Jacobs Rm. 561

Recent interest in an agile workforce and machine flexibility has lead to a new
wave of challenges in manufacturing systems. Classic questions need to be revisited such as where should flexible machines be allocated? how often should they be utilized? and can they be used to mitigate the challenge of machine failures or worker availability? In this talk we consider each of these problems as they relate to tandem queues. We begin by answering the question of where machines should be allocated and show that the classic
c-\mu-rule from parallel systems applies in the tandem queue setting. We then show that this extends to the case when workers might be available only temporarily. We also show that when there is complete control of the capacity decision this control is monotone in the number of customers in each queue. We conclude (time permitting) by showing that an "almost" monotone switching curve describes the optimal policy when machine reliability is considered.
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"Managing Customer Outrage: Focus Organizational Learning Efforts on Service Failure or Recovery?"
Talk by Michael Lapre, Vanderbilt
Jan. 25, 12 PM, Jacobs Rm. 561

As service failures are inevitable, firms must be prepared to recover from service failures, thereby turning angry, frustrated customers into loyal customers. Despite the compelling economics of customer loyalty, firms continue to struggle with service recovery. Should firms focus organizational learning efforts on reducing service failure or on reducing dissatisfaction with recovery? Drawing from the literatures on organizational learning, learning curves, and marketing, I hypothesize that dissatisfaction with recovery contributes more to the variation in customer outrage across firms than service failure does (H1), that a U-shaped function of operating experience explains more variation in dissatisfaction with recovery than in service failure (H2), and that heterogeneity in organizational learning curves explains more variation in dissatisfaction with recovery than in service failure (H3). The hypotheses are tested with quarterly data for nine major U.S. airlines over 11 years. All three hypotheses are supported. In the context of mishandling baggage, dissatisfaction with recovery explains 88% of the variation in customer outrage, whereas service failure explains only 12%. The empirical results suggest firms should pay more attention to organizational learning curves for service recovery.
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"Excess Inventory and Long-Term Stock Price Performance"
Talk by Vinod Singhal, Georgia Tech
Feb. 8, 12 PM, Jacobs Rm. 586

This paper estimates the long-run stock price effects of excess inventory using nearly 900 excess inventory announcements made by publicly traded firms during 1990-2002. It examines the stock price effects starting one year before through two years after the excess inventory announcement date. Statistically significant abnormal returns are observed during the year before the announcement and on announcement. There is no evidence of statistically significant abnormal return during the two years after the announcement. I estimate that the mean (median) abnormal return due to excess inventory is -37.22% (-27.03%). Negative abnormal returns are observed across industries, calendar time, firm size, and actions taken to deal with excess inventory. The evidence suggests that the stock market partially anticipates excess inventory situations, firms do not recover quickly from the negative effect of excess inventory, and the negative effect of excess inventory is economically and statistically significant.
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"Pricing Dynamics of Competitors Who Ignore Competition"
Talk by Anton Kleywegt, Georgia Tech
Feb. 15, 1:30 PM, Jacobs Rm. 586

A variety of demand models are widely used in revenue management, all of which are known to be inaccurate. We are interested in the dynamic behavior of systems in which revenue managers use these inaccurate models, they make decisions based on the models, observe data, and attempt to refine the models with the observed data. Most of this talk will describe a duopoly in which each seller models demand as a function of the prices of that seller only. That is, the demand models that sellers estimate with data, model the quantity demanded as a function of the prices of the seller, and these models do not include the prices of the other seller. Such simplified models are often used in revenue management practice, even when revenue managers are aware of the competition. We compare the resulting dynamical behavior with the outcomes in other settings, such as the equilibria of well informed competitors, the outcomes of collaborators, and the outcomes when there is asymmetric information.
This is joint work with Tito Homem-de-Mello at Northwestern University and Bill Cooper at the University of Minnesota.
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"Asymptotic Results in Single and Multiclass Type Queueing Networks"
Talk by David Gamarnik, MIT
Feb. 22, 1:30 PM, Jacobs Rm. 561

Stochastic queueing networks have a variety of industrial applications including services, call centers, data and communication networks, manufacturing and more recently business processes.
We will begin with some motivating examples of business workflow processes and the underlying performance analysis issues. Then we will continue by introducing stochastic single class and multiclass queueing networks. The principal question is whether the probability distribution of the queue lengths has exponentially fast decaying tails in steady-state. We establish that for single class queueing networks this is indeed the case. Moreover, we establish that the stationary distribution of the associated reflected diffusion process provides a valid heavy-traffic approximation of the underlying queueing network in steady-state.
The presence of multiclass structure makes the picture rather different. We
present an example of a network where the queue length exhibit an unexpected subexponential behavior. Thus, we show the slow decay of the tails can be a purely network effect. We will discuss the implication of these results for control designs.

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"Simple Models of Discreet Choice and Their Performance in Bandit Experiments"
Talk by Noah Gans, Wharton
March 8, 12 PM, Jacobs Rm. 561

Recent operations management papers model customers as solving multi-armed bandit problems, positing that consumers use a particular heuristic when choosing among suppliers. These papers then analyze the resulting competition among suppliers and mathematically characterize the equilibrium actions. There remains a question, however, as to whether the original customer models upon which the analyses are built are reasonable representations of actual consumer choice.
In this paper, we empirically investigate how well these choice rules match actual performance as people solve two-armed Bernoulli bandit problems. We find that some of the most analytically tractable models perform best in tests of model fit. We also find that the expected number of consecutive trials of a given supplier is increasing and convex its expected quality level, a result that is consistent with the models' predictions, as well as with loyalty effects described in the popular management literature.
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"Operational Benefits of Subscription Services"
Talk by Sunil Kumar, Stanford
March 15, 12 PM, Jacobs Rm. 561

In this talk we study a monopolistic firm that offers reusable products, or a service, to price and quality-of-service sensitive customers - a rental firm can be thought of as the canonical example. Customers' perception of quality is determined by their likelihood of obtaining the product or service immediately upon request. We study the alternatives of offering either a subscription option or a pay-per-use option from a profit-maximizing perspective. In order to do this we propose a Markovian model of how subscribers generate requests and use a standard Poisson model for the pay-per-use option. In a large market setting, under the assumption of exponential demand, we show that using the
subscription option is more profitable for the firm. Further, via a numerical study, we show that this assumption is not essential for the result to hold. However, we show that it is not necessarily true that the subscription option dominates the pay-per-use option on quality-of-service. The firm is able to manage the trade-off between price and quality-of-service better in the subscription option. Moreover, we show that the social welfare and the consumer surplus can also be higher in the subscription option, indicating that both the firm and the consumers can benefit from the subscription option.

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Fall 2005 Seminars
The following speakers visited NU for the Fall 2005 Kellogg Operations Seminar series. Please click on a date for more information about a particular talk.

 Randy Berry NU Engineering Sept. 21 Feryal Erhun Stanford Oct. 5 Rene Caldentey NYU Oct. 19 Sasha Stolyar Bell Laboratories Oct. 26 Don Eisenstein University of Chicago Nov. 9 Dave Hartvigsen Notre Dame Nov. 30

"Spectrum Sharing Games"
Talk by Randy Berry, NU Engineering Dept.
Sept. 21

In wireless networks a key consideration is how multiple users can share the available spectrum. This is especially true in unlicensed or open bands, where users may be deployed without any centralized frequency planning or control.
In this talk, we describe some simple models for sharing a given spectrum band. We discuss both a case where a "spectrum manager" controls access and a case where there is no manager and users implement a distributed algorithm to manage access. In the first case, we describe auction mechanisms where the users bid for spectrum access. We characterize the resulting equilibria and discuss iterative algorithms for reaching these.
In the second case, we give a distributed algorithm, in which users announce "price" signals that indicate their "cost" of interference. We relate this algorithm to a "fictitious" game, which in certain cases is supermodular. We use this relation to characterize the algorithms convergence. Extensions to multi-channel networks may also be discussed.
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"Managing Demand Uncertainty with Dual Supply Contracts"
Presentation by Feryal Erhun, Stanford
Oct. 5

We consider a single product, dual supply problem under a periodically-reviewed finite planning horizon. The downstream party, manufacturer, receives supply from two upstream parties, local and global suppliers with consecutive leadtimes (i.e., the leadtime of the global supplier is one period longer than that of the local supplier). The suppliers offer complementary contracts in terms of transfer prices and leadtimes; thus, the manufacturer faces a trade-off between the responsive local supplier and the cost-efficient global supplier. We model the manufacturer’s problem in two stages: (i) she first chooses a portfolio of contracts (one from each supplier) and reserves capacity levels (at the prices specified by the contracts) for the whole planning horizon; (ii) she then orders from the suppliers according to the terms of the contracts chosen in the previous stage. In our second-stage problem, we prove that a two-level modified base-stock policy is optimal for a wide range of transfer prices. With various analytical results and numerical analysis, we illustrate how the optimal policy parameters change with respect to problem parameters. A reserve-up-to policy is shown to be optimal for the
manufacturer’s capacity reservation problem. We also develop a methodology that can be used to explain diverse sourcing strategies (such as in-house vs. offshore production) practiced by many companies in various industries.
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"The Martingale Approach to Operational and Financial Hedging"
Talk by Rene Caldentey, NYU
Oct. 19

We consider the problem of maximizing the profits of a corporation when these profits depend in part on movements in the financial markets and/or economic indices. We propose a methodology for the optimal selection of dynamic operating and financial hedging strategies when the decision maker is risk averse or budget constrained.

Risk aversion is imposed through constraints on the feasible policies such as VaR, CVaR and budget constraints, among others. We apply our methodology to some standard operations problems including the popular newsvendor model and a supply chain procurement/inventory problem. We also identify circumstances in which the risk management constraints can effectively be ignored when solving for the optimal operating policy.
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"Maximizing Queueing Network Utility Subject to Stability"
Talk by Sasha Stolyar, Bell Laboratories
Oct. 26 at 11:00 AM
Jacobs Center Rm. 1246
We study a model which accommodates a wide range of seemingly very different resource allocation problems in communication networks. Some examples: utility based congestion control of complex time-varying (wireless) networks, minimizing average power consumption in wireless networks, scheduling in wireless systems subject to power consumption and/or traffic rate constraints.
The model is a controlled queueing network, where controls have dual effect. In addition to determining exogenous customer arrival rates, service rates at the nodes, and (possibly random) routing of customers among the nodes, each control decision produces a certain vector of "commodities." The set of available control choices depends on the underlying random network mode. Network "utility" is a concave function of the vector of long-term average rates at which commodities are produced. The goal is maximize utility while keeping network queues stable. We introduce a very parsimonious dynamic control policy, called Greedy Primal-Dual algorithm, and prove its asymptotic
optimality. Although the model is formulated in terms of a queueing network, the algorithm can be viewed as a dynamic mechanism for solving rather general convex optimization problems.
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"Self-Organizing Cyclic Logistics Systems"
Talk by Don Eisenstein, University of Chicago
Nov. 9 at 1:00 PM
Jacobs Center Rm. G05
A self-organizing system is one in which the actions of decentralized entities combine to elicit stable global behavior. We seek rules that make systems "gravitate" to a balance point after a shock or perturbation.
We review our ideas on self-balancing production lines, and how they can lead to newer models of self-organization of cyclic systems.