Schedule Recovery: Unplanned Absences in Service Operations*

The U.S. service sector loses 2.3% of all scheduled labor hours to unplanned absences, but in some industries, the total cost of unplanned absences approaches 20% of payroll expense. The principal reasons for unscheduled absences (personal illness and family issues) are unlikely to abate anytime soon. Despite this, most labor scheduling systems continue to assume perfect attendance. This oversight masks an important but rarely addressed issue in services management: how to recover from short‐notice, short‐term reductions in planned capacity. In this article, we model optimal responses to unplanned employee absences in multi‐server queueing systems that provide discrete, pay‐per‐use services for impatient customers. Our goal is to assess the performance of alternate absence recovery strategies under various staffing and scheduling regimes. We accomplish this by first developing optimal labor schedules for hypothetical service environments with unreliable workers. We then simulate unplanned employee absences, apply an absence recovery model, and compute system profits. Our absence recovery model utilizes recovery strategies such as holdover overtime, call‐ins, and temporary workers. We find that holdover overtime is an effective absence recovery strategy provided sufficient reserve capacity (maximum allowable work hours minus scheduled hours) exists. Otherwise, less precise and more costly absence recovery methods such as call‐ins and temporary help service workers may be needed. We also find that choices for initial staffing and scheduling policies, such as planned overtime and absence anticipation, significantly influence the likelihood of successful absence recovery. To predict the effectiveness of absence recovery policies under alternate staffing/scheduling strategies and operating environments, we propose an index based on initial capacity reserves.     

Time‐Staged Overtime Staffing for Services with Updated Forecasts and Availabilities

This article develops a framework for staffing in a service environment when multiple opportunities exist for prescheduling overtime prior to the start of a shift. Demand forecasts improve as the shift approaches, while the availability of workers to be scheduled for overtime decreases. First, a single‐shift model is developed and used in computational studies to evaluate the benefits of time‐staged overtime staffing, which include slightly lower costs and significant reductions in unscheduled overtime and outside agents. A multishift model is then developed to consider constraints on consecutive hours worked and minimum rest intervals between shifts. A multishift computational study shows how the benefits of time‐staged overtime staffing depend on problem characteristics when interactions between shifts are considered. The article discusses how single‐shift and multishift models relate to each other and alternative ways the models may be used in practice, including decentralized open shift management and centralized overtime scheduling.     

A Load‐based and Due‐date‐oriented Approach to Order Review/Release in Job Shops*

In this paper, a new order review/release (ORR) method is proposed for shop floor control systems. The proposed method utilizes both job due date and shop load information to improve the effectiveness of the ORR function in production systems. The performance of the new method is compared to those of a few well‐known ORR methods under four experimental conditions. The results of extensive simulation experiments indicate that the proposed method is superior with respect to the mean absolute deviation measure. In general, it is also better than existing methods for the other performance measures. Furthermore, we show that the proposed method is more robust to variations in system load and processing times than the other ORR methods examined.     

Effective Design of Products/Services: An Approach Based on Integration of Marketing and Operations Management Decisions*

This paper presents an integrated framework for designing profit‐maximizing products/ services, which can also be produced at reasonable operating difficulty levels. Operating difficulty is represented as a function of product and process attributes, and measures a firm's relative ease or difficulty in meeting customer demand patterns under specified operating conditions. Earlier optimum product design procedures have not considered. operational difficulty. We show that optimum profit, market share, cost, and product profiles are dependent on operating difficulty level. Empirical results from the pizza delivery industry demonstrate the value of the proposed Effective Product/Service Design approach.     

An Integrated Decision Methodology for Designing and Operating an Air‐Express Courier's Distribution Network*

DHL, an international air‐express courier, has been operating in Hong Kong for many years. In 1998, the new international airport located at a site considerably distant from the old location opened in Hong Kong (HK). Other airport‐related infrastructure facilities have also been developed or are being developed, resulting in major changes in transport structure as well as a shift in customer demand. In this paper a multiyear distribution network is designed for DHL(HK) using an integrated network design methodology, which consists of a macro model and a micro model. The macro model, a mixed 0–1 LP, determines in an aggregate manner the least‐cost distribution network. The micro model, a simulation, evaluates the operational viability and efficacy of the network according to its service coverage and service reliability. We also illustrate how coverage and reliability can be improved via the integrated use of the two models. Extensive discussion on relevant planning and operational issues of an air‐express courier are included. The methodology has been successfully implemented at DHL(HK). It has been used to design the network, to test strategic decisions, and to update the network.     

Decision Sciences Research in China: A Critical Review and Research Agenda—Foundations and Overview*

This article focuses on decision sciences research in China, providing an overview of current research and developing a foundation for future China‐based research. China provides a unique research opportunity for decision sciences researchers, owing to its recent history, rapid economic development, and strong national culture. We examine recent economic reforms and their impact on the development of research questions in the decision sciences, as well as discuss characteristics of the diverse regions in China and their potential as sites for various types of research. We provide a brief overview of recent China‐based research on decision sciences issues relating to national culture, supply chain management, quality management, production planning and control, operations strategy, and new product development and discuss some of the unique methodological challenges inherent in China‐based research. We conclude by looking forward to emerging research opportunities in China.