MANAGERIAL ECONOMICS & DECISION SCIENCES; OPERATIONS
Associate Professor of Managerial Economics & Decision Sciences
Service Management
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Economist Intelligence Unit: Executive Briefing: Global dual sourcing strategies - 7/3/2009
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2009 Full-Time and Part-Time MBA Convocation - 6/26/2009
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When designing a sourcing strategy in practice, a key task is to determine the average order rates placed to each source because that affects costs and supplier management. We consider a firm that has access to a responsive near-shore source (e.g., Mexico) and a low-cost offshore source (e.g., China). The firm must determine an inventory sourcing policy to satisfy random demand over time. Unfortunately, the optimal policy is too complex to allow a direct answer to our key question. Therefore, we analyze a tailored base-surge (TBS) sourcing policy that is simple, used in practice, and captures the classic tradeoff between cost and responsiveness. The TBS policy replenishes at a constant rate from the offshore source and produces at the near shore plant only when inventory is below a target. The constant base allocation allows the offshore facility to focus on cost efficiency while the nearshore’s quick response capability is utilized only dynamically to guarantee high service. The research goals are to i) determine the allocation of random demand into base and surge capacity, ii) estimate corresponding working capital requirements, and iii) identify and value the key drivers of dual sourcing. Given that even this simple TBS policy is not amenable to exact analysis, we investigate a Brownian approximation that yields a simple “square-root” formula that is insightful to answer our questions and sufficiently accurate for practice, as is demonstrated with a validation study.
The literature on many-server approximations provides significant simplifications towards the optimal capacity sizing of large-scale monopolists but falls short of providing similar simplifications for a competitive setting in which each firm’s decision is affected by its competitors’ actions. In this paper, we introduce a framework that combines many-server heavy-traffic analysis with the notion of epsilon-Nash equilibrium and apply it to the study of equilibria in a market with multiple large-scale service providers that compete on both prices and response times. In an analogy to fluid and diffusion approximations for queueing systems, we introduce the notions of fluid game and diffusion game. The proposed framework allows us to provide first-order and second-order characterization results for the equilibria in these markets. We use our results to provide insights into the price and service-level choices in the market and, in particular, into the impact of the market scale on the interdependence between these two strategic decisions.
We develop a model for the competitive interactions in service industries where firms cater to multiple customer classes or market segments with the help of shared service facilities or processes, so as to exploit pooling benefits. Different customer classes typically have distinct sensitivities to the price of service as well as the delays encountered. In such settings firms need to determine: (i) the prices charged to all customer classes, (ii) the waiting time standards, i.e. expected steady-state waiting time promised to all classes, (iii) the capacity level and (iv) a priority discipline enabling the firm to meet the promised waiting time standards under the chosen capacity level, all in an integrated planning model which accounts for the impact of the strategic choices of all competing firms. We distinguish between three types of competition: depending upon whether firms compete on the basis of their prices only, waiting time standards only, or, on the basis of price and waiting time standard. We establish in each of the three competition models that a Nash equilibrium exists under minor conditions regarding the demand volumes. We systematically compare the equilibria with those achieved when the firms service each market segment with a dedicated service process.
In many service industries, companies compete with each other on the basis of the waiting time their customers experience, along with the price they charge for their service. A firm's waiting time standard may either be defined in terms of the expected value or a given, for instance 95%, percentile of the steady state waiting time distribution. We investigate how a service industry's competitive behavior depends on the characteristics of the service providers' ueueing systems. We provide a unifying approach to investigate various standard single stage systems covering the spectrum from M/M/1 to general G/GI/s systems, along with open Jackson networks to represent multi-stage service systems. Assuming that the capacity cost is proportional with the service rates we refer to its dependence on (i)the firm's demand rate and (ii) the waiting time standard as the capacity cost function. We show that across the above broad spectrum of queueing models, the capacity cost function belongs to a specific four parameter class of function, either exactly or as a close approximation. We then characterize how this capacity cost function impacts on the equilibrium behavior in the industry. We give separate treatments to the case where the firms compete in terms of (i) prices (only) (ii) their service level or waiting time standard (only), (iii) simultaneously in terms of both prices and service levels. The firms' demand rates are given by a general systems of equations of the prices and waiting time standards in the industry.
We analyze a general market for an industry of competing service facilities. Firms differentiate themselves by their price levels and the waiting time their customers experience, as well as different attributes not determined directly through competition. Our model therefore assumes that the expected demand experienced by a given firm may depend on all of the industry's price levels as well as a (steady state) waiting time standard, which each of the firms announces and commits itself to by proper adjustment of its capacity level. We focus primarily on a separable specification, which, in addition is linear in the prices. (Alternative non-separable or non-linear specifications are discussed in the concluding section.) We define a firm's service level as the difference between an upper bound benchmark for the waiting time standard, and the firm's actual waiting time standard. Different types of competition and resulting equilibrium behavior may arise, depending on the industry dynamics through which the firms select their strategic choices. In one case, firms may initially select their waiting time standards, followed by a selection of their prices in a second stage (Service Level First). Alternatively, the sequence of strategic choices may be reversed (Price First) or as a third alternative, the firms may make their choices simultaneously (Simultaneous Competition). We model each of the service facilities as a single server M/M/1 queueing facility, which receives a given firm specific price for each customer served. Each firm incurs a given cost per customer served as well as cost per unit of time proportional to its adopted capacity level.
An econometric methodology is developed for nonparametric estimation of concave production technologies. The methodology, based on the principle of maximum likelihood, uses entropic distance and convex programming techniques to estimate production functions.Empirical applications are presented to demonstrate the feasibility of the methodology in small and large data sets.
The buffer allocation problem (BAP) is a well-known difficult problem in the design of production lines. We present a stochastic algorithm for solving the BAP, based on the cross-entropy method, a new paradigm for stochastic optimization. The algorithm involves the following iterative steps: (a) the generation of buffer allocations according to a certain random mechanism, followed by (b) the modification of this mechanism on the basis of cross-entropy minimization. Through various numerical experiments we demonstrate the efficiency of the proposed algorithm and show that the method can quickly generate (near-)optimal buffer allocations for fairly large production lines.
Delay announcements informing customers about anticipated service delays are prevalent in service-oriented systems. How to use delay announcements to manage the service system in an efficient manner is a complex problem which depends on both the dynamics of the underlying queueing system and on the customer behavior.We examine this problem of information communication by considering a model in which both the firm and the customers act strategically: the firm in choosing its delay announcement while anticipating customer response, and the customers in interpreting these announcements and in making the decision about when to join the system and when to balk. We characterize the equilibrium language that emerges between the service provider and her customers. The analysis of the emerging equilibria provides new and interesting insights into customer-firm information sharing. We show that even though the information provided to customers is non-verifiable and non-credible, it improves the profits of the firm and the expected utility of the customers. Further, the information could be as simple as “High Congestion”/“Low Congestion” announcements, or could be as detailed as the true state of the system. We also show that firms may choose to shade some of the truth by using intentional vagueness to lure customers.
In many industries, firms consider the option of outsourcing an important service process associated with the goods or services they bring to the market. Often, competing firms outsource this service process to one or more common service suppliers. When they outsource to a common service provider, this gives rise to a service supply chain. We develop analytical models to characterize the benefits and disadvantages of outsourcing in service industries in which the retailers compete with each other in terms of the price they charge and/or the waiting time expectations and standards which they adopt and sometime advertise. To assess the benefits of outsourcing strategies, we give concrete answers to the following questions: (i) if a service supply chain wants to operate at maximum efficiency, what type of payment schemes does the service provider need to offer the retailers to coordinate the chain? (ii) Given optimally coordinating payment schemes for the outsourced service, when are firms better off if all of them choose to outsource rather than perform the service in-house? In addition, when will a service supply chain in which firms choose to outsource to a common provider be stable in the sense that no firm has an incentive to unilaterally abandon the chain and provide in-house service instead? How do the answers to these questions depend on the type of competition, the intensity of the competition, the number of firms in the industry and the sales volume of the firms? (iii) How do the answers to the questions raised in (i) and (ii) depend on whether the outside supplier pools the service processes or not and whether it is able to operate at lower cost rates than the service retailers themselves?
Many service providers use delay announcements to inform customers of anticipated delays. However, this information is usually not provided immediately, but rather after a short period of time (spent either waiting or occupied by the system). The focus of this paper is on the impact of this postponement on the ability of the firm to communicate non-verifiable congestion information to its customers as well as on the profits and utilities for the firm and the customers respectively. We show that this postponement can actually help the firm create credibility and augment the equilibrium language. However, in other settings this delay can also detract the equilibrium language. Further, we show that whenever credibility is created it improves not only the profit for the firm, but also the customers’ overall utility.
Recent times have witnessed the emergence of large-scale, web-based service marketplaces where many small service providers compete among themselves on catering to customers with diverse needs. Customers who frequent these marketplaces seek quick resolutions and thus usually trade-off prices with waiting times. The main goal of the paper is to discuss the role of the moderating firm in facilitating information gathering, operational efficiency, and communication among agents. Surprisingly, operational efficiency may be detrimental to the overall efficiency of the marketplace. Further, we show that to reap the "expected" gains of operational efficiency, the moderating firm may need to complement the operational efficiency by enabling communication among its agents. The study emphasizes the large-scale of such marketplaces and the impact it has on the outcomes.
In many service industries, companies compete with each other on the basis of the waiting time their customers’ experience, along with other strategic instruments such as the price they charge for their service. The objective of this paper is to conduct, what we believe to be the first, empirical study of an important industry to test whether and to what extent waiting time performance measures impact different firms’ market shares and price decisions.We report on a large scale empirical industrial organization study in which the demand equations for fast-food drive-thru restaurants in Cook County are estimated based on so-called structural estimation methods. Our results confirm the belief expressed by industry experts, that in the fast-food drive-thru industry customers trade off price and waiting time. More interestingly, our estimates indicate that consumers attribute a very high cost to the time they spend waiting.
We address the simultaneous determination of pricing and capacity investment strategies in a multi-period setting under demand uncertainty. In our model a monopolistic firm makes three decisions: capacity investment (or disinvestment), production (inventory), and price, all of which can be specified dynamically as a function of the state of the system. We analyze the optimal joint strategy and investigate the relationships between the main strategic decision variables: price and capacity. We consider models that allow for either bi-directional price changes or models with markdowns only. We show that the optimal capacity adjustment is given by a target interval policy: there is a (state dependent) target interval for each period such that capacity is adjusted ``as little as possible" to bring the available level into this interval. Once the capacity level is determined, we show that if bi-directional price changes are allowed, a modified-base-stock list price is the optimal inventory-pricing decision. In both cases we derive the structure of the optimal policy, and in the latter case we prove that capacity and price are strategic substitutes.
Provision of real-time information by a firm to its customers has become prevalent in recent years in both the service and retail sectors. In this paper, we study a retail operations model where customers are strategic in both their actions and in the way they interpret information, while the retailer is strategic in the way it provides information. This paper focuses on the ability (or the lack thereof) to communicate credibly unverifiable nformation. We develop a game-theoretic framework to study this type of communication and discuss the equilibrium language emerging between the retailer and its customers. We show that for a single-retailer setting, the equilibrium language that emerges carries no information. In this sense, a single-retailer providing information on its own cannot create any credibility with the customers. We explore several remedies so that the firm can credibly disclose availability information to its customers. While in these remedies we show that the firm may be able to reveal complete information, the firm would prefer to shade some information and use intentional vagueness.
In recent times due to the commoditization of goods, many traditional firms often offer services as well. In this paper, we study the role of services apart from being another revenue source and understand how to manage services. We first understand their role in pricing of the underlying good. Next we study the impact of services on good variety offered by the firm. Lastly, we study the impact of services on markets of durable goods with its specific characteristics. We characterize the optimal behavior of the firm and relate that to concepts of bundling and market segmentation. We show that existence of services help firms offer a wider variety of underlying goods. Further, we show that in the market of durable goods, offering service resolve some of key problems such as time inconsistency.
This course counts toward the following majors:Operations.
Operations management is the management of business processes--that is, the management of the recurring activities of a firm. This course aims to familiarize students with the problems and issues confronting operations managers, and to provide the language, concepts, insights and tools to deal with these issues to gain competitive advantage through operations. We examine how different business strategies require different business processes and how different operational capabilities allow and support different strategies to gain competitive advantage. A process view of operations is used to analyze different key operational dimensions such as capacity management, cycle time management, supply chain and logistics management, and quality management. Finally, we connect to recent developments such as lean or world-class manufacturing, just-in-time operations, time-based competition and business re-engineering.
Prerequisite: DECS-433 or DECS-436.
Operations Strategy (OPNS-454-0)
This course counts toward the following majors: Analytical Consulting, Operations.
In this course, students learn how operations strategy can add value by tailoring a set of core principles to a specific business setting. The course provides a framework to formulate an operations strategy and analyze, value, and optimize the key decisions involved in operations strategy. The key evaluation metric is how operations strategy impacts the net present value of the firm. The key decisions studied are choosing competitive operational competencies and benchmarking; capacity expansion, timing, flexibility and location; sourcing and contracting; risk management and operational hedging; revenue management; improvement and learning.
This course builds on the core operations class. Students should also be familiar with the basics of finance, economics and strategy, as the strategic decisions studied in this course require a detailed analysis and understanding of the underlying operations. Thus this course has a greater amount of concreteness and detail than a competitive strategy class, and uses a combination of in-depth case analysis, mini-lectures, presentations and qualitative discussions of other examples. The course is intended for students interested in operations and supply chain management, general management, or management consulting.
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