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The paper presents a modeling framework that assists management to allocate capital resources to improve the yield and the yield variability in a multistage production system. The results indicate that production stages with low yield and/or high yield variability receive more capital resources for improvement. The applicability of the model is demonstrated in a real-life manufacturing firm in the food-packing industry. The solution of the model provides directions of improving the stages of a multistage production system.

Service quality improvement has become an imperative in today's service firms. In this paper, we present a modeling framework that combines marketing and operations viewpoints for resource allocation. The framework can be used to allocate resources to the different stages of a multistage service system, where the manager's goal is to improve customers' perceptions of service quality, given some budget. Optimal allocation guidelines are provided, and the interplay of three factors on the resulting allocation scheme is captured. These factors are the current level of customers' perceptions of service quality at each stage, the cost of implementing a service quality improvement at each stage, and the importance placed by customers at each stage. Sensitivity analysis to provide additional managerial insights is also performed. We demonstrate the applicability of the modeling framework, using data from a real life health care environment. Model limitations and future research are also discussed.

We develop a framework for combining strategic benchmarking with efficiency benchmarking of the services offered by bank branches. In particular, a cascade of efficiency benchmarking models is developed guided by the service-profit chain. Three models—based on the nonparametric technique of Data Envelopment Analysis—are developed in order to implement the framework in a practical setting: (i) an operational efficiency model, (ii) a service quality efficiency model, and (iii) a profitability efficiency model. The use of the models is illustrated using data from the branches of a commercial bank. Empirical results indicate that we gain superior insights by analyzing simultaneously the design of operations together with the quality of the provided services and profitability, rather than by benchmarking these three dimensions separately. Relationships are also established between operational efficiency and profitability, and between operational efficiency and service quality.

The Bank of Cyprus commissioned a study of the efficiency penalties paid by its branches operating in different environments. Bank staff and faculty members of the University of Cyprus jointly developed a data envelopment analysis (DEA) model that was used (1) to provide benchmarks on the relative efficiency of the bank's branches, (2) to provide guidelines for improvement to management, and (3) to isolate the effects of the environment on branch efficiency. The analysis showed that tourist branches were on average more efficient than urban branches during the peak tourist season. The DEA model was also used to benchmark seasonality effects. The analysis revealed branch resource underutilization, which depends on the season. The bank uses the DEA model developed to establish policy guidelines and to provide support for operational decisions toward performance improvement.

In this paper a theoretical framework to assess the efficiency of the Requirements Capture and Analysis (RCA) process in software development is introduced. Although it is widely recognized that successful implementation of the first stages of the software development process is critical for the overall development process, RCA efficiency assessments have not been given much attention. The presented theoretical framework to assess RCA efficiency follows a production approach to model the early stages of a software project. An approach based on Data Envelopment Analysis that utilizes the proposed framework to isolate the effects of exogenous factors, such as the environment or the type of project, on the project's RCA efficiency is also presented. Finally, the applicability of the methodology through an exploratory empirical study is demonstrated, and managerial implications are discussed.

The paper extends the current literature on peer review journal evaluations by providing a number of insights based on the diversity of Production and Operations Management (POM) research. We provide peer review evaluations for POM research outlets, based on a sampling frame that includes a large number of POM researchers worldwide. More specifically, the paper develops and tests various hypotheses as to whether the perceived quality and relevance of a journal is affected by such factors as: (i) nature of research work (empiricists versus modelers), (ii) society membership, (iii) research productivity, (iv) geographical location, and (v) seniority. Our findings suggest that caution must be exercised when utilizing existing POM journal rankings, as some factors, particularly the difference between empiricists and modelers, may influence journal evaluation. These must be considered when addressing issues such as faculty promotions, tenure, and salary. © 2006 Elsevier B.V. All rights reserved.

This paper identifies factors that promote research productivity of production and operations management (POM) groups of researchers in US business schools. In this study, research productivity of a POM group is defined as the number of articles published per POM professor in a specific period of time. The paper also examines factors that affect research quality, as measured by the number of articles published per POM professor in journals, which have been recognized in the POM literature as an elite set. The results show that three factors increase both the research productivity and the quality of the articles published by professors of a POM group. These factors are (a) the presence of a POM research center, (b) funding received from external sources for research purposes, and (c) better library facilities. Doctoral students do assist in improving research quality and productivity, but they are not the driving force. These results have important implications for establishing policy guidelines for business schools. For example, real-world problems are funded by external sources and have a higher probability of publication. Furthermore, schools could place more emphasis on external funding, as most engineering schools do, since groups receiving external funding are more productive in terms of research.

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Following the evolution and growth of the field of production and operations management (POM), a number of studies have recently appeared which provide insights on the ranking of the research outlets of the field. These studies are US-based and exclude non-US researchers. In this study we present rankings of the publication outlets in the POM discipline as perceived by POM researchers in Europe. Perceived relevance and quality of POM related journals, and differences between the rankings obtained by our study and those obtained by existing studies are identified. An examination of the publication rate of European researchers in five highly ranked journals provides further insights and future research directions.

Over the last few years data envelopment analysis (DEA) has been gaining increasing popularity as a performance assessment methodology. DEA has been successfully applied to bank branch performance evaluation. However, most DEA models which have been developed for bank branch performance assessment do not include service quality as an output. Service quality has been considered by many as the key to gaining competitive advantage and customer loyalty. Develops a DEA model that can be used to provide direction for improvement to branches which do not use their resources in the most efficient way to produce service quality. Focuses on internal customer service quality which is sometimes easier to measure. Presents results from an empirical study undertaken at a bank to demonstrate the applicability of the model.