Arthur V. Hill

Research Opportunities in Service Process Design

Abstract This paper presents an overview of the new issues and research opportunities related to four service operations design topics—the design of retail and e-tail service processes, design of service processes involving waiting lines and workforce staffing, service design for manufacturing, and re-engineering service processes. All four topics are motivated by new technologies (particularly web-based technologies) and require a multi-disciplinary approach to research. For each topic, the paper presents an overview of the topic, the relevant frameworks, and a discussion of the research opportunities.

An algorithm for the traveling salesman problem with pickup and delivery customers

Abstract The paper extends the branch and bound algorithm of Little, Murty, Sweeney, and Karel to the traveling salesman problem with pickup and delivery customers, where each pickup customer is required to be visited before its associated delivery customer. The problems considered include single and multiple vehicle cases as well as infinite and finite capacity cases. Computational results are reported.

A preliminary investigation of the relationships between employee motivation/vision, service learning, and perceived service quality

Abstract Most experts agree that a learning organization whose employees have a clear vision of the importance of service quality and are motivated to provide that quality will achieve superior service quality. We develop a theoretical framework and conduct a cross-sectional empirical study to investigate the inter-relationships among these constructs. The results indicate that higher levels of both employees’ motivation/vision and organizational learning positively affect perceived service quality. Additionally, employees’ motivation/vision was found to mediate the relationship between organizational learning and perceived service quality. These results highlight the importance of employees’ motivation/vision in both the service process and the learning process.

A Methodology for Constructing Collective Causal Maps

This article develops a new approach for constructing causal maps called the Collective Causal Mapping Methodology (CCMM). This methodology collects information asynchronously from a group of dispersed and diverse subject-matter experts via Web technologies. Through three rounds of data collection, analysis, mapping, and interpretation, CCMM constructs a parsimonious collective causal map. The article illustrates the CCMM by constructing a causal map as a teaching tool for the field of operations management. Causal maps are an essential tool for managers who seek to improve complex systems in the areas of quality, strategy, and information systems. These causal maps are known by many names, including Ishikawa (fishbone) diagrams, cause-and-effect diagrams, impact wheels, issue trees, strategy maps, and risk-assessment mapping tools. Causal maps can be used by managers to focus attention on the root causes of a problem, find critical control points, guide risk management and risk mitigation efforts, formulate and communicate strategy, and teach the fundamental causal relationships in a complex system. Only two basic methods for creating causal maps are available to managers today-brainstorming and interviews. However, these methods are limited, particularly when the subject-matter experts cannot easily meet in the same place at the same time. Managers working with complex systems across large, geographically dispersed organizations can employ the CCMM presented here to efficiently and effectively construct causal maps to facilitate improving their systems.

A pricing model for clearing end-of-season retail inventory

The problem of setting prices for clearing retail inventories of fashion goods is a difficult task that is further exacerbated by the fact that markdowns enacted near the end of the selling season have a smaller impact on demand. In this article, we present discrete-time models for setting clearance prices in such an environment. When demand is deterministic, we compute optimal prices and show that decreasing reservation prices lead to declining optimal prices. When demand is stochastic and arbitrarily correlated across planning periods, we obtain bounds on the optimal expected revenue and on optimal prices. We also develop a heuristic procedure for finding near-optimal prices and test its accuracy through numerical experiments. These experiments reveal new insights for practitioners. For example, the penalty for choosing clearance price once and keeping it unchanged for the remainder of the selling season is found to be small when either the mean reservation prices do not change appreciably over time or when they drop sharply after the first period.

Modeling the Effects of a Service Guarantee on Perceived Service Quality Using Alternating Conditional Expectations (ACE)

This paper addresses the dearth of empirical research on the relationship between service guarantee and perceived service quality (PSQ). In particular, we examine the moderating effects of a service guarantee on PSQ. While a recent study provided empirical evidence that service quality is affected by service guarantee and employee variables such as employee motivation/vision and learning through service failure, the nature and form of the relationships between these variables remain unclear. Knowledge of these relationships can assist service managers to allocate resources more judiciously, avoid pitfalls, and establish more realistic expectations. Data was obtained from employees and customers of a multinational hotel chain that has implemented a service guarantee program in 89 of its hotels in America and Canada. As the employee variables could affect performance in a non-linear fashion, we relaxed the assumption of model linearity by using the Alternating Conditional Expectations (ACE) algorithm to arrive at a better-fitting, non-linear regression model for PSQ. Our findings indicate the existence of significant non-linear relationships between PSQ and its determinant variables. The ACE model also revealed that service guarantee interacts with the employee variables to affect PSQ in a non-linear fashion. The non-linear relationships present new insights into the management of service guarantees and PSQ. Explanations and managerial implications of our results are presented and discussed.

Reducing vendor delivery uncertainties in a JIT environment

Abstract Just-in-time (JIT) manufacturing systems have found wide acceptance in the automotive industry as well as in other industries such as electronic assembly. One of the key features of JIT systems is frequent reliable deliveries from local vendors. This article considers two major points related to managing inbound transportation from these local vendors. The first point is that vendor delivery uncertainties can be reduced if the JIT manufacturer takes over the responsibility for inbound logistics from all JIT vendors. In other words, we are suggesting that the manufacturing firm (vendee) pick up raw materials and purchased parts from its vendors rather than having the vendors deliver. The second major point is that a simple economic analysis and a computer-based decision support system (like that we propose in this article) could be used to help schedule the JIT pickups. The article begins with a description of why deliveries are critical in the JIT environment and why vendee pickup from local vendors can be a better alternative than vendor delivery. When a manufacturer manages its own inbound local deliveries it benefits from more timely information on upsets, reduced transportation costs, reduced transaction costs, and most importantly, reduced uncertainty in deliveries. We then turn to a model for how to schedule JIT pickups. This model addresses the following “JIT Vendor Pickup Problem”: For a given number of vehicles with limited capacity, find a one-week JIT vendor pickup schedule that will minimize the total incremental carrying, travel, and pickup costs subject to the constraint that the vendee does not run out of any item from any vendor. In the above problem definition, the demand rates for all items are assumed to be constant over the short term. This model can be imbedded in a computer-based decision support system to facilitate the daily scheduling of the pickups from the vendors. In the conclusion, we deal with the expected benefits to the vendee for managing its own incoming JIT pickups and then propose a model that could be used to implement this concept.

A decision support system for the courier vehicle scheduling problem

The timely collection and transportation of checks from branches to the operations center is a primary concern to bank managers. Transit checks need to be cleared quickly to maximize available funds to the banks cash managers. However, management must not over-commit resources, such as drivers and vehicles, to improve the movement of checks from branch banks to the operations center. Bank managers must make difficult decisions regarding courier fleet size, routing, and scheduling in order to balance these conflicting goals and to minimize overall system costs. We present a micro-computer based decision support system (DSS) that helps bank managers make good decisions for their vehicle courier service. This system, known as Chexpedite, helps establish the correct number of vehicles, the appropriate collection routes, the timing of the routes, and a view of the check flow pattern into the operations center (back office check processing center). The system has been implemented at a number of banks, indicating its value to bank management. In this paper, the logic of Chexpedite and its database, optimization, and simulation capabilities are presented. A case study application demonstrates the system. The final section concludes with a discussion of Chexpedites strengths and weaknesses.

A Model for Optimal Delivery Time Guarantees

This article formulates a model for finding the optimal delivery time performance guarantee. The expected profit model is solved to find a closed-form expression for the optimal delivery time promise. The simple, yet powerful model gives new insights into performance service guarantees in general and delivery time guarantees in particular.

Learning from a service guarantee quasi experiment

The authors analyze data from a service guarantee program implemented by a midpriced hotel chain. Using a multisite regression discontinuity quasi-experimental design developed over 16 months, they control for unobserved heterogeneity among guests and treatments across hotels and develop Bayesian posterior estimates of the varying program effect for each hotel. The results contribute to theory and practice. First, they provide new insights into how service guarantee programs operate in the field. Specifically, the guarantee was more effective at hotels with a better prior service history and an easier-to-serve guest population, which is consistent with signaling arguments but does not comport with the incentive argument that guarantees actually improve service quality. Second, the results offer managers better decision rules. Specifically, the authors devise program continuation rules that are sensitive to both observed and unobserved differences across sites. In addition, they devise policies to reward hotels that exceed site-specific expectations. By controlling for observed and unobserved differences across sites, the authors show that these policies potentially reward even hotels with negative net program effects, which is useful in reducing the organizational stigma of failure. Finally, the authors identify sites that should be targeted for future program rollout by computing the odds of succeeding.