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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.

Purpose Work that is considered appropriate for only one gender by the indigenous culture is explored. The focus is on the operational issues that accrue due to the combination of what is deemed appropriate treatment to, and activities of, women. Global differences in the operational sub-categories of business location, layout, the implementation of process improvement programs, shift scheduling, operational compliance, the strategic capability of volume flexibility, and other issues are explored. The paper aims to discuss these issues. Design/methodology/approach The literature from the disparate fields of women’s studies, anthropology, law, developmental economics, and management are synthesized. Findings There are extreme differences internationally in the viability of operational practices involving shift work, facility location, and other production issues. Particularly, research involving the implementation of quality management programs may be compromised due to gender effects. Practical implications A large number of practical issues are discussed. The viability and wisdom of many operational practices being copied from different cultures is addressed. Originality/value This work is a synthesis of the same subjects from widely disparate intellectual domains. The author informs management scholars and managers from unusual sources in medicine, women’s studies, anthropology, developmental economics, and law.

Our setting is the secure data destruction industry in which company trucks travel to distant customer sites to pick up outdated documents containing sensitive information (e.g. social security numbers). Outdated documents are stored in locked containers to be shredded, rather than simply recycled, by the secure data destruction service provider. Currently, no client site inventory level information is available. Due to the lack of inventory information, nearly a quarter of client site visits are wasted as there is no inventory to pick up. Another quarter of client site visits arrive too late. To improve operational efficiency, the firm invented an electronic device to make the containers at customer sites a part of the “Internet of Things” - the containers would be able to remotely transmit inventory level information, thus providing the opportunity for just-in-time inventory pickup. Utilizing this information requires a re-structuring of industry operations from fixed scheduled pickups to dynamic routing. Through heuristic creation and robust testing, we examine the conditions in which the Internet of Things application under consideration would be useful or yield minimal gains. Ultimately, the firm decided not to implement the application as the costs of maintenance and the operational re-structuring outweigh potential gains. Our study has implications for firms that are considering to explore Internet of Things to improve their operations.

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.

Variable correlation is important for many operations research models. Many inventory, revenue management, and queuing models presume uncorrelated demand between products, market segments, or time periods. The specific model applied, or the resulting policies of a model, can differ drastically depending on variable correlation. Having missing data are a common problem for the real world application of operations research models. This work is at the junction of the two topics of correlation and missing data. We propose a test of independence between two variables when data are missing. The typical method for determining correlation with missing data ignores all data pairs in which one point is missing. The test presented here incorporates all data. The test can be applied when both variables are continuous, when both are discrete, or when one variable is discrete and the other is continuous. The test makes no assumptions about the distribution of the two variables, and thus it can be used to extend application of non-parametric rank tests, such as Spearman's rank correlation, to the case where data are missing. An example is shown where failure to incorporate the incomplete data yields incorrect policies.

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.

Case Teaching Note: Interested Instructors please see the Instructor Materials page for access to the restricted materials. To maintain the integrity and usefulness of cases published in ITE, unapproved distribution of the case teaching notes and other restricted materials to any other party is prohibited.

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.

PurposePrior surveys have shown that national culture is a leading cause of problems in offshoring services. The research question posed in this paper centers on how and through what specific decisions national culture affects operational implementation in offshore facilities.Design/methodology/approachA particular US service process offshored to Barbados and the Dominican Republic is studied. Ethnographic worker observations are combined with archival sources and executive interviews.FindingsA culture clash caused a number of operational dilemmas for a major US airline offshoring the same processes to two Caribbean nations. The offshoring was a success at one site, a failure at another. But, even at the successful site, un‐intuitive operational adaptations had to be made to accommodate cultural differences. Specifically, detailed here are decisions or results seen on country selection, location selection within a country, quality program implementation, and shift work that had strong cultural inputs.Research limitations/implicationsAny case study may be limited to the specific case. However, broader implications are that operations management decisions may be more highly dependent on national culture than previously thought.Practical implicationsManagement – especially US management – continue to make service offshoring decisions ignoring or minimizing the inevitable cultural conflict. This work provides tangible examples of decisions affected by culture.Originality/valueConcrete, specific examples are provided for the difficulties national culture created in a specific case. Methods used to circumvent these difficulties are shown. By this specific example, the general case is posited that culture must be considered in operations decisions that may seem devoid of cultural content.

Harrah's Cherokee Casino & Hotel is an unusual example of the use of revenue-management (RM) techniques. Typical RM installations yield revenue improvements of between 3 and 7 percent. The Harrah chain has seen 15-percent improvements, with Harrah's Cherokee Casino & Hotel as the largest beneficiary—although it does not serve alcohol or have traditional table games. In addition, the RM techniques that the Cherokee uses, such as its pricing decisions and customer-segmentation rules, are different from those used in RM applications in other industries.

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.

Services are now a larger portion of the economy than manufacturing for every nation on Earth, and services are an overwhelming portion of Western economies. While decision-making research has begun responding to this change, much of the scholarly work still addresses manufacturing issues. Particularly revealing is the field of operations management (OM), in which the proportion of manuscripts dedicated to services has been estimated at 3%, 6%, and 7.5% by various authors. We investigate several possible reasons for the neglect of services in research, including the difficulty in defining services, viewing services as derivative activities, a lack of defined processes, a lack of scale in services, and the effect of variability on service performance. We argue that times have changed, and none of these reasons is valid anymore. We sound the warning that failure to emphasize services in our research and teaching may signal the decline of the discipline. We note the proportion of OM faculty in business schools has shrunk in the past 10 years. Finally, we examine a selection of service research agendas and note several directions for high-impact, innovative research to revitalize the decision sciences. With practitioners joining the call for more research in services, the academic community has an exciting opportunity to embrace services and reshape its future.

Multi-channel retailing—selling through multiple, distinct channels—has been a part of the retail industry as long as there have been main street merchants selling through catalogs. Since the mid-1990s, however, multi-channel retailing has increased dramatically due to traditional retailers selling over the Internet. This trend presents considerable operational challenges because Internet and traditional retail have vastly different demand drivers, product variety issues, optimal inventory configurations, cost structures, supply chain structures, and delivery mechanisms. Consequently, the optimal supply chain configuration for Internet delivery differs considerably from the optimal supply chain configuration for a retail store structure, so designing a supply chain system to serve both channels well is difficult. Accordingly, a set of strategic choices and trade-offs must be made. Here, we present some strategic alternatives.

We analyze a periodic-review inventory model where the decision maker can buy from either of two suppliers. With the first supplier, the buyer incurs a high variable cost but negligible fixed cost; with the second supplier, the buyer incurs a lower variable cost but a substantial fixed cost. Consequently, ordering costs are piecewise linear and concave. We show that a reduced form of generalized (s, S) policy is optimal for both finite and (discounted) infinite-horizon problems, provided that the demand density is log-concave. This condition on the distribution is much less restrictive than in previous models. In particular, it applies to the normal, truncated normal, gamma, and beta distributions, which were previously excluded. We concentrate on the situation in which sales are lost, but explain how the policy, cost assumptions, and proofs can be altered for the case where excess demand is backordered. In the lost sales case, the optimal policy will have one of three possible forms: a base stock policy for purchasing exclusively at the high variable cost (HVC) supplier; an (sLVC, SLVC) policy for buying exclusively from the low variable cost (LVC) supplier; or a hybrid (s, SHVC, SLVC) policy for buying from both suppliers.

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.

In theory, it is a simple proposition: Make customers wait longer, and fewer of them will come back. But actual practice is complicated. Marketing develops a new product, service, affinity plan, and so on. This new marketing initiative causes changes in operational processes that increase customer service times. When waiting lines form, a small increase in service times for each customer magnifies into a significant increase in waiting time for the customer at the end of the line. The increase in waiting times causes a reduction in customer loyalty, which leads to lower customer retention, and hence, repurchases. Consequently, the marketing initiative has costs as well as benefits. Blockbuster, Inc. has developed a model that combines operational process analysis, waiting line simulation, real versus perceived waiting times, a customer loyalty model, and a financial model to find the bottom-line impact from operational changes of new marketing programs.

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.

The predictive value of a common measure of efficiency and the robustness of the data envelopment analysis (DEA) technique is examined when statistical noise is present in the data. Inferences are drawn from a hypothetical example regarding the potential limitations of the efficiency measure and pitfalls in both the single- and multi-stage applications of DEA. We propose a simple procedure to investigate the robustness of DEA results. The procedure maintains the relative computational simplicity of DEA and is easy to apply and interpret. Using this procedure, we examine the robustness of the results reported in two published DEA studies and find that, indeed, pitfalls occur in practical applications. We conclude with recommendations for researchers applying the technique and implications for managers.

This paper develops an order-up-to S inventory model that is designed to handle multiple items, resource constraints, lags in delivery, and lost sales without sacrificing computational simplicity. Mild conditions are shown to ensure that the expected average holding cost and the expected average shortage cost are separable convex functions of the order-up-to levels. We develop nonparametric estimates of these costs and use them in conjunction with linear programming to produce what is termed the “LP policy.” The LP policy has two major advantages over traditional methods: first, it can be computed in complex environments such as the one described above; and second, it does not require an explicit functional form of demand, something that is difficult to specify accurately in practice. In two numerical experiments designed so that optimal policies could be computed, the LP policy fared well, differing from the optimal profit by an average of 2.20% and 1.84%, respectively. These results compare quite favorably with the errors incurred in traditional methods when a correctly specified distribution uses estimated parameters. Our findings support the effectiveness of this mathematical programming technique for approximating complex, real-world inventory control problems.

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.

The MotherLand Air case and game provide students with a first-hand experience in revenue management. The game is conducted in the classroom, and students, who have the opportunity to prepare in advance, work in teams. During the exercise, students make and revise pricing and protection-level decisions over a six-month, predeparture time horizon for two simulated flights. The case provides an introduction to nested reservation systems and overbooking and pricing polices. Case Teaching Note: Interested Instructors please see the Instructor Materials page for access to the restricted materials. To maintain the integrity and usefulness of cases published in ITE, unapproved distribution of the case teaching notes and other restricted materials to any other party is prohibited.

Yield management techniques have been shown to be highly effective in many for-profit firms, led by the airline and hotel industries. Yield management is intended to maximize profits for a capacity-constrained service by price discriminating among customer types for essentially undifferentiated services. The authors extend yield management concepts to the nonprofit sector, where profit maximization no longer is a goal. A general heuristic is presented to assist decision makers in pricing decisions. The technique is demonstrated at a nonprofit child care center that provides discounts to low-income families.

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.

Capacity limitations for manufacturers of seasonally demanded goods create difficult problems for both practitioners and researchers. Empirical data suggest that practitioner response is far from optimal. Optimal solutions, however, are precluded for realistic problems due to computational complexity. Here, the structure of optimal policies is explored and heuristics based on myopic policies are developed. For simple problems, the best heuristic deviates from optimality by an average of 2.5% over a variety of conditions. Heuristics are also compared under more realistic business conditions by simulation.

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

Transportation decisions and production decisions can usually be made separately. Under certain conditions, however, there are important interactions between transportation and production. An example of a combined production and routeing environment exists in the United States Postal Service (USPS). The daily movement and sorting of first class letter mail requires balancing transportation and mail sorting production costs, while considering the time dependent accumulation of mail during the day. The focus of this paper is on the practical application of a combined production/routeing model to a specific USPS division and the use of insights from this model to re-design the operation. A mixed integer linear program using candidate route integer variables is solved to near optimality. Specific routes and production schedules detailed by the model indicate that the division could save up to 5% of their combined production and routeing costs, amounting to $298000 per year. More importantly, the model provides insight on potential heuristics to choose routes under these circumstances.

Services have dominated Western economies for over half a century. Worldwide, services are now the largest economic sector, recently replacing agriculture. Services are now a larger portion of the economy than manufacturing for every nation on Earth. Yet, much of the scholarly work in Operations Management (OM) still addresses manufacturing issues. While Western economies are 70%–85% services, less than 10% of OM research done by Western academics is dedicated to services. Here, we examine some causes for this state of affairs: The attitude that “service = servile”, the rise of supply chain as an organizing paradigm, and the research methods needed for services.