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Proper performance measurement is an important issue in library operational management. A data envelopment analysis (DEA) model is applied to evaluate the relative operational efficiency of 25 U.S. private research-university library members of the Association of Research Libraries (ARL). Operations of each library decision-making unit are considered as a production process using four resource input and four service output variables. The model results are analyzed and compared with the efficient group and a peer group by using a t-test. The model provides decision-makers with more accurate information to implement better library services with appropriate resource allocation.

An examination of changing an inventory policy of a major manufacturing organisation and its impact on the cost structure of the organisation is presented. A classic cost confrontation between set‐up costs and inventory costs are examined in the study. The results reveal that the unique nature of the manufacturing organisation favours short‐run production scheduling over a proposed long‐run production scheduling policy. This article also presents the application of a decision support system (DSS) to aid in production scheduling. The applications reveals that improved scheduling and a reduction in scheduling time and effort can be achieved by using the DSS over the manufacturing organisations′ manual systems.

Data Envelopment Analysis (DEA) is a nonparametric mathematical programming technique used to measure the relative efficiency of the production organization's operations. This paper presents the theoretical measures of the railway systems, along with the bootstrap DEA analysis. A DEA model is applied to evaluate the relative efficiency of railway operations of 29 UIC (Union Internationale des Chemins de fer) countries, based on the data obtained from the International UIC publications. The bootstrap DEA analysis provides information (bias estimates) on the sensitivity of the DEA efficiency index to the sampling variations. The model results are analyzed and evaluated in terms of their relative operational performance efficiency. The model results facilitate an organization's decision-making by providing valuable information.

A dual transportation analysis is considered as a strategic matter for plant facility expansion/contraction decision making in manufacturing operations. The primal-dual problem is presented in a generalized mathematical form. A practical technique of generating the dual solution is illustrated with a plant facility expansion/contraction example as a tutorial. Demand forecasting is performed based on the time series data with seasonal variation adjustments. The dual solution helps facilitate operations decision making by providing useful information.

An appropriate assessment of sustainability in venture business is an important managerial and investment decision making. Data envelopment analysis (DEA) is utilized for sustainability assessment for venture business firms’ performance. Venture business firms are primary decision-making units (DMUs). Required information for this study is collected from Korea Listed Companies Association (KLCA) database. The proposed DEA model incorporates multiple inputs and outputs to assess the relative operational efficiency of the DMUs, identifying the best performance group among the peer venture business firms. The proposed model provides decision-makers with more accurate information for strategic insights to make better investment decisions in the competitive business environment.

Public schools in the United States continue their struggle with the divergent goals of improving performance and reducing spending. For almost a decade, they have been challenged to comply with the federal No Child Left Behind Act (NCLB). In many local districts, those goals have been pursued with the reality of funding reductions, and the problem now exacerbated by budget shortfalls due to the global economic crisis. In the present situation, solutions based on efficiency and economy are worthy of renewed examination.

This chapter employs data envelopment analysis (DEA) in a large-scale study of 447 public school districts in the State of Missouri. It develops a baseline DEA model to measure district efficiencies. Then it classifies districts using a relative wealth variable (rich and poor) and attempts to determine the degree to which that classification changes the baseline model.

The study concludes that using a relative wealth variable in the analysis produces more robust results than the baseline model. It further demonstrates that funding allocation decisions may be improved by including a relative wealth variable in the decision-making processes.

The purpose of this paper is to present a methodology to assist enterprise decision makers (DMs) to select from a number of processes during Business Process Reengineering (BPR) according to organizational objectives. Indeed, after the identification and classification of process and illustration of the organizational objectives and criteria, the effect of each process on each objective and criterion is calculated to select the most appropriate processes for reengineering purposes.

The proposed methodology uses fuzzy quality function deployment (QFD) technique to convert the qualitative data (DM’s opinion) to quantitative ones and then calculates the effects of each process on the organizational objectives and criteria. Then, by using the result of fuzzy QFD, the amount of satisfaction of each process according to each criterion is calculated. By combining this data with other effective variables in BPR projects such as “cost” and “time,” a multi-objective goal programming (GP) model is formulated and solved to identify the most appropriate business processes.

In fact, a quantitative model is presented in which fuzzy QFD and GP methods are combined to help DMs to adopt an appropriate strategy for implementing BPR projects successfully by selecting proper processes for reengineering purposes. In addition, the presented model uses both qualitative and quantitative data and turns them into quantitative ones. An example is also provided to show how the model works.

Following this investigation, other researchers could able to complete the model with more dynamic and local variables to enhance the accuracy of the model.

The introduced model will support organizations and managers to select appropriate processes for BPR; so in practice, the mentioned projects will be more efficient and successful.

The paper study is essential for organizations, because the presented decision-making model is based on fuzzy QFD and GP methods that enable the enterprises to select the business processes during the BPR projects easily. In this paper, a GP model is presented to create a balance between organizational satisfaction level and cost and time of implementing BPR projects considering organizational constraints. The proposed model was applied to a real case and the authors showed that it is an easy-to-use, valid, and powerful tool for implementing BPR projects.

Recognizing the fundamental role of quality as a means to differentiate service organizations, the purpose of this paper is to propose a strategic decision making framework for service organizations, which prioritizes performance improvement strategies that are rooted to customer requirements, organizational goals and constrained by organizational resources.

The proposed framework is realized through the implementation of two stages and four distinct phases mirroring the combination of enhanced quality function deployment (first stage), and zero-one goal programming (second stage). It proposes the utilization of a mix of qualitative and quantitative methods, and the collection of data from multiple sources including customers, middle, and top management.

The application and validation of the proposed framework utilizes information from both customers and employees in the bank services sector. Overall, results from the specific study revealed that a combination of “reengineering” and “expansion” strategies was more appropriate corresponding to customer priorities, organizational goals and effective utilization of available resources.

The paper presents a novel two stage strategic framework for service organizations. It utilizes a balanced mixture of qualitative and quantitative methods in an effort to capture and delineate elusive customer requirements and design characteristics of services, allowing the assessment of different combinations of quality improvement strategies in response to management objectives.

This paper seeks to develop a model for the assignment of employees to workplaces. Assignment methods are of high relevance in practice because employees should be assigned to workplaces according to their competences and preferences to ensure that motivated employees carry out tasks effectively and efficiently.

Two goal programming models are introduced with inputs and valuations using the analytic hierarchy process.

The two goal programming models for the assignment of employees to workplaces, which take into account both employee competences and preferences as well as workplace competence requirements and attributes, seem to be effective in helping to arrive at an optimal assignment decision.

In practice, one major problem is that the input data for the goal programming models are not updated regularly. Thus, the documentation of the competence profiles and the preferences of the employees might be out of date or incomplete.

The development of the two goal programming models which could be applied immediately in practical competence management is what makes the work valuable and addresses a gap in the modelling of personnel assignment methodologies.

Many urban areas of the United States have experienced urban sprawl in the past 60 years. Severe out-migration of relatively wealthier families to ex-urban counties has left relatively poorer families behind. When combined with the recent national conversation about school improvement, this migration has caused significant stress on urban school districts, as indicated by population demographics, revenues, and school performance.

This chapter looks at 22 public school districts in Saint Louis County, Missouri. It first reviews the decision environment for those districts and constructs a relative wealth variable from environmental factors. Then, using data envelopment analysis (DEA), it compares rich districts and poor districts, and attempts to classify the relative efficiencies of those districts. Three DEA models are considered: the baseline CCR-O (Model 1), a CAT-O-C model (Model 2), and a revised CCR-O (Model 3). Using computer software, DEA-Solver, these three model results are compared and analyzed to study the effects of each district's relative wealth on the model results.

The study concludes that adding a relative wealth variable produces more robust model results and suggests that school district decisions may be improved by including a relative wealth variable in their decision-making processes.