Customer Loyalty and Supplier Strategies for Quality Competition

We analyze a model of consumer response to variation in product or service quality. The model, developed in [4], provides closed-form expressions that characterize short-term and long-term measures of customer loyalty to a supplier. In this paper, we develop sensitivity analyses that offer a rich characterization of how factors - such as the underlying quality levels of the suppliers, the customer's ability to distinguish between good and bad suppliers, and the customer's prior beliefs regarding the suppliers affect both short-term and long-term loyalty. We also use the expressions to develop simple normative models for suppliers that wish to develop effective quality strategies.

Customer Learning and Loyalty When Quality is Uncertain

A consumer has repeated contacts with a set of product or service providers. Each visit to a supplier yields the consumer some randomly distributed utility. The suppliers' utility distributions are unknown to the consumer, and to decide which supplier to visit, she uses a myopic variant of the decision rule used by a classical, utility-maximizing Bayesian. This rule is designed to be roughly consistent with empirical findings regarding individual choice under uncertainty. For this model, we develop closed-form expressions that characterize both short-term and long-term measures of customer loyalty to a supplier. These results offer a rich picture of how consumer discrimination and prior beliefs interact with the level of quality actually offered by suppliers to determine customer loyalty.

A Single-Server Queue with Markov Modulated Service Times

We study an M/MMPP/1 queuing system, where the arrival process is Poisson and service requirements are Markov modulated. When the Markov Chain modulating service times has two states, we show that the distribution of the number-in-system is a superposition of two matrix-geometric series and provide a simple algorithm for computing the rate and coefficient matrices. These results hold for both finite and infinite waiting space systems and extend results obtained in Neuts [5] and Naoumov [4]. Numerical comparisons between the performance of the M/MMPP/1 system and its M/G/1 analogue lead us to make the conjecture that the M/MMPP/1 system performs better if and only if the total switching probabilities between the two states satisfy a simple condition. We give an intuitive argument to support this conjecture.

Managing Learning and Turnover in Employee Staffing

We study the employee staffing problem in a service organization that uses employee service capacities to meet random, non-stationary service requirements. The employees experience learning and turnover on the job, and we develop a Markov Decision Process (MDP) model that explicitly represents the stochastic nature of these effects. Theoretical results are developed that show the optimal hiring policy is of a state-dependent "hire-up-to" type, similar to the inventory "order-up-to" policy. This holds for discounted-costs MDP's under both finite and infinite planning horizons. We also develop structural properties of the optimal policy to facilitate computation of the optimal hiring numbers. For two important special cases of the general model, we prove the optimality of a myopic policy under both stationary and stochastically increasing service requirements. Moreover, we show that in these two cases, when service requirements are k-periodic, it is sufficient to solve a k-period MDP problem with appropriate end-of-horizon cost function. When general, non-stationary service requirements are present, we prove the existence of a one-sided "smoothing effect" of the optimal hire-up-to-levels. Numerical results show that the use of state-dependent hire-up-to policies may offer significant cost savings over simpler hiring policies. In particular, our results show that when employee capacity increase due to learning is substantial and flexible incremental capacity (overtime) is tight, a fully state-dependent policy out-performs a policy that hires only on the basis of the total number of employees in the system. Our problem formulation and results suggest natural connections to the classic results in inventory literature. We also discuss many of the connections and distinctions in the paper.

Urban Pastures: A Computational Approach to Identify the Barriers of Segregation

Urban Sociology is concerned with identifying the relationship between the built environment and the organization of residents. In recent years, computational methods have offered new techniques to measure segregation, including using road networks to measure marginalized communities\u27 institutional and social isolation. This paper contributes to existing computational and urban inequality scholarship by exploring how the ease of mobility along city roads determines community barriers in Atlanta, GA. I use graph partitioning to separate Atlanta’s road network into isolated chunks of intersections and residential roads, which I call urban pastures. Urban pastures are social communities contained to residential road networks because movement outside of a pasture requires the need to use larger roads. Urban pastures fences citizens into homogenous communities. The urban pastures of atlanta have little

Securing the Containerized Supply Chain: Analysis of Government Incentives for Private Investment

To mitigate the threat that terrorists smuggle weapons of mass destruction into the United States through maritime containers, the U.S. Bureau of Customs and Border Protection (CBP) inspects containers upon entry to domestic ports. Inspection-driven congestion is costly, and CBP provides incentives to firms to improve security upstream in the supply chain, thereby reducing the inspection burden at U.S. ports. We perform an economic analysis of this incentive program, called Customs-Trade Partnership Against Terrorism (C-TPAT), modeling in a game-theoretic framework the strategic interaction between CBP, trading firms, and terrorists. Our equilibrium results highlight the possibility that a properly run program can efficiently shift some of CBP\u27s security burden to private industry. These results also suggest that CBP may have the opportunity to use strategic delay as an incentive for firms to join. Analysis of comparative statics shows that, with increasing capacity, membership in C-TPAT systematically declines

Economic Analysis of Simulation Selection Problems

Ranking and selection procedures are standard methods for selecting the best of a finite number of simulated design alternatives based on a desired level of statistical evidence for correct selection. But the link between statistical significance and financial significance is indirect, and there has been little or no research into it. This paper presents a new approach to the simulation selection problem, one that maximizes the expected net present value of decisions made when using stochastic simulation. We provide a framework for answering these managerial questions: When does a proposed system design, whose performance is unknown, merit the time and money needed to develop a simulation to infer its performance? For how long should the simulation analysis continue before a design is approved or rejected? We frame the simulation selection problem as a “stoppable” version of a Bayesian bandit problem that treats the ability to simulate as a real option prior to project implementation. For a single proposed system, we solve a free boundary problem for a heat equation that approximates the solution to a dynamic program that finds optimal simulation project stopping times and that answers the managerial questions. For multiple proposed systems, we extend previous Bayesian selection procedures to account for discounting and simulation-tool development costs

Markov Decision Problems Where Means Bound Variances

We identify a rich class of finite-horizon Markov decision problems (MDPs) for which the variance of the optimal total reward can be bounded by a simple linear function of its expected value. The class is characterized by three natural properties: reward nonnegativity and boundedness, existence of a do-nothing action, and optimal action monotonicity. These properties are commonly present and typically easy to check. Implications of the class properties and of the variance bound are illustrated by examples of MDPs from operations research, operations management, financial engineering, and combinatorial optimization