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A partial answer is provided to the question “what are the most research productive business school Operations Management departments?” Among the many research rankings published, this research question appears to be unique. Other efforts in Operations Management rank either business schools or entire universities. Our criterion for ordering departments is also unique: publications only by faculty currently employed by the department. Other rankings do not consider the number or title of faculty, thereby providing an advantage to larger and more experienced faculty. This ranking is adjusted for both number of faculty and years of experience.

We consider serving moving customers that follow a structured route through a system. Demand for resources by these customers occurs during their time in the system and is met at discrete points. Demand can be probabilistic and multiple service attempts may be desired as customers move through the system. The decision of interest is how to meet demand with mobile servers. We develop a model to structure the problem, answer the research question, and determine when a server should move between locations as well as how long the server should stay in any one location.

An important difference between both manufacturing and wholesaling vs. retail is the information available concerning inventory. Typically, far less information characterizes retail. Here, an extreme environment of information shortfall is examined. The environment is technically termed "unattended points of sale," but colloquially called vending machines. Once inventory is loaded into a machine, information on demand and inventory level is not observed until the scheduled reloading date. Technological advances and business process changes have drawn attention to the value of information (VOI) in retail inventory in many venues. Moreover, technology is now available that allows unattended points of sale to report inventory information. Capturing the value of this information requires changes in current business practice. We demonstrate the value of capturing information analytically in an environment with restrictive demand assumptions. Experiments in an environment with realistic demand assumptions and parameter values show that the VOI depends greatly on operating characteristics and can range from negligible effects to increasing profitability 30% or more in actual practice.

The problem of interest is a one product, uncapacitated master production schedule (MPS) in which decisions are made under rolling planning horizons. Demand is stochastic and time varying, and effectiveness is measured by inventory holding, production setup, and backorder costs. Typically, in both the research literature and the business practice the stochastic nature of the problem is modeled in an ad hoc fashion. The stochastic MPS problem is usually solved by adding safety stock to production quantities obtained from a deterministic lot-sizing algorithm. Here, the stochastic nature of the problem is explicitly considered, as an optimal algorithm for solving the static probabilistic dynamic lot-sizing problem is adapted to rolling planning horizons. The resulting algorithm is found to dominate traditional approaches over a wide variety of experimental factors, reducing total costs by an average of 16% over traditional methods.

This special issue is dedicated to Operations Management (OM) in Asia. A requirement for the special issue articles is that they have content related to the effects of national culture on OM. Here, the OM literature is combined with work from Anthropology and Women's Studies to provide a wide view of the effects of various Asian cultures on OM. The basic premise is that OM decisions may need to take culture into account: some OM practices are altered or precluded by culture, while others are more effective in some cultures than others. Numerous examples are provided involving quality management, shift scheduling, revenue management, facility location, layout, supply chain strategies, and other areas.

We consider a single product, single level, stochastic master production scheduling (Mps) model where decisions are made under rolling planning horizons. Outcomes of interest are cost, service level, and schedule stability. The subject of this research is the Mps control system: the method used in determining the amount of stock planned for production in each time period. Typically, Mps control systems utilize a single buffer stock. Here, two Mps dual-buffer stock systems are developed and tested by simulation. We extend the data envelopment analysis (dea) methodology to aid in the evaluation of the simulation results, where Dea serves to increase the scope of the experimental design. Results indicate that the dual-buffer control systems outperform existing policies.

Data envelopment analysis (dea) has become an increasingly popular method to measure performance for service firms with multiple sites. DEA is superior to many traditional methods for firms that have multiple goals. The promise of DEA is that the complex, multi-objective problem of performance measurement can be reduced to a single number. Unfortunately, the practice of DEA often belies the promise. Misconceptions concerning the purpose and implementation of DEA can cause DEA applications to be less than successful. Here, the technique is explained, and a guide to the implementation of DEA is proposed, utilizing DEA studies of retail bank branches.

Theories on the life cycle of firms combined with stage model theories of information technology adoption indicate that start-up firms may have different needs than mature firms. In particular, start-up firms’ need for simplicity, development speed, adaptability, transparency and clarity often trump cost minimisation in the characteristics looked for in production planning tools. Here, a case study is presented of a start-up firm with geographically distant suppliers and geographically dispersed warehouses and customers. Production planning needed to move from Post-it notes to a formal system. A spreadsheet-based production planning system was custom built that allowed the firm to decide on purchases and have dynamically adjusted available-to-promise information to help the sales force.

Operations Management ( OM) research on organizational culture has to change to be able to inform practice. Currently, organizational culture research in OM is largely confined to narrow topical and methodological niches and culture is most frequently used as an explanatory variable in quantitative, survey-based research. We argue that the relegation of culture to this niche is due to self-imposed methodological blinders that hobble the OM field. We then present four research imperatives to reinvigorate organizational culture research within our field. We urge OM scholars to view culture as a dynamic concept that can be influenced, to adopt alternative methods, to use non-traditional data sources, and to rethink assumptions about dependent variables. We also identify gaps in the current knowledge and new research questions for the OM domain. We conclude that the field of OM could greatly expand its understanding of organizational culture and in so doing greatly improve business practice, but that to do so will require a change in the culture of the operations management research community. [ABSTRACT FROM AUTHOR]

Technological advances and changes in supply chain management practices have combined to draw attention to the value of information sharing in inventory replenishment. Academic research has produced seemingly conflicting results due to differences in the type of information that is shared, the supply chain structure, and the selection and parameterization of performance goals. This research provides a framework to help explain apparent differences in the extant literature. Our purpose is to understand what determines the value of information. With this specific view, we establish a set of research questions and suggest directions for future research. We introduce a research framework organized around the current literature and established theory. This framework is then evaluated by collectively classifying and coding results from the literature. Using a split-sample, least squares regression analysis, our results provide tentative empirical evidence that supports the framework, but also indicate that there are additional complex relationships among modeling parameters and assumptions that present opportunities for future research.

A heuristic is developed for a common production/inventory problem characterized by multiple products, stochastic seasonal demand, lost sales, and a constraint on overall production. Heuristics are needed since the calculation of optimal policies is impractical for real-world instances of this problem. The proposed heuristic is compared with those in current use as well as optimal solutions under a variety of conditions. The proposed heuristic is both near optimal and superior to existing heuristics. The heuristic deviated from optimality by an average of 1.7% in testing using dynamic programming as a benchmark. This compares favorably against linear-programming-based heuristics and practitioner heuristics, which deviated from optimality by 4.5 to 10.6%.

We explore the benefits of ‘breaking bulk’ in retail operations. Here, breaking bulk refers to delivering single units from distribution centers to retail outlets rather than the multiple units bundled together by manufacturers termed ‘case-packs’. The focus is largely on the benefits to space management at the retail level, rather than the more obvious reduction in inventory costs. Using data from the grocery industry, results indicate that retail unit profitability can be increased substantially by breaking bulk—but only if current inventory replenishment practices are changed. In essence, breaking bulk allows for either higher product variety within a store or identical variety in smaller stores. This work seeks to quantify the order of magnitude of that benefit.

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.

A potential retail operations strategy is to have a “dense” store. That is, a store that combines high product variety with a small footprint. Retail management desires smaller stores to provide the strategic benefits of convenience and speed to customers, but desires larger stores to provide high product variety. Noting the benefits of smaller, more numerous stores, several retailers well known for their extremely large store size recently have begun experimenting with a small store format. Traditional retail inventory management policies, however, have difficulty combining high variety and small store size. Here, the potential advantages of the dense store type are explored. To facilitate this exploration, inventory policies are developed to help manage small stores by increasing their product density. Results based on grocery industry data indicate that the heuristics compare favorably to optimality and permit the dense store concept to potentially achieve substantial gains compared to current practice.

A fundamental problem for make-to-stock producers of seasonally demanded goods is the allocation of limited production capacity. Three important decisions must be made: (1) how early in the low demand season to produce at full capacity in anticipation of high demand later, (2) the product mix to manufacture when producing in anticipation of later demand, and (3) when capacity is insufficient to make all that is desired, how to allocate capacity among products. Here, simple rules are generated for solving these problems by analysing optimal policies.

Consider multiple companies operating as a serial supply chain. Within this environment, end users form the demand for the last company in the supply chain, but the demand for upstream companies is formed by the companies in the immediate downstream supply chain link. It has been shown that demand seasonality and forecast error can increase as we proceed up the supply chain. These demand distortions, called the “bullwhip” effect, create inefficiencies for upstream firms. This work seeks to identify the magnitude of the problem by establishing an empirical lower bound on the profitability impact of the bullwhip effect. Results indicate that the importance of the bullwhip effect to a firm differs greatly depending on the specific business environment. Given appropriate conditions, however, eliminating the bullwhip effect can increase product profitability by 10–30%.

We consider a production/inventory problem with stochastic seasonal demand. A scarce resource limits production in each time period, and setup time is negligible. Linear and stationary costs are assessed for production, holding inventory, and stock-outs. The calculation of optimal solutions is difficult so heuristics are used. The heuristics used in business practice are shown to cost an average of 30% above optimal policy costs. A superior heuristic is constructed utilizing an analytic approximation for optimal policies that costs an average of 2% over optimal policy costs.

As service has become the predominant part of all advanced economies, increasing attention has been paid to conceptualizing and formulating its mathematical structure. Models that arise from service and the management of service may be broadly group d into three main categories: (1) customer behavior models , that explain how customers react to service, (2) service quality impact models , that address the business consequences of service quality, and (3) normative service models , that prescribe how organizations should organize and manage their service. We briefly discuss the most important models in each category, which arise primarily from the Marketing, Operations, and Operations Research literatures.

Production-inventory systems that experience significant demand variations from period to period often use deterministic lot-sizing techniques to schedule production runs. If demand is uncertain, safety stock is included to buffer against stockouts. In this paper we propose a 'dual-buffer' production scheduling policy and show that it has superior performance characteristics to the single buffer policies currently in use. The second buffer is a simple calculation based on prior work in stochastic inventory theory. Methodologically, data envelopment analysis (DEA) is used to analyze results. This represents an unusual use of DEA