Konstantinos P. Triantis

A Mathematical Programming Approach for Measuring Technical Efficiency in a Fuzzy Environment

A three stage approach is proposed to measure technical efficiency in a fuzzy environment. This approach uses the traditional data envelopment analysis framework and then merges concepts developed in fuzzy parametric programming (Carlsson and Korhonen, 1986). In the first stage, vague input and output variables are expressed in terms of their risk-free and impossible bounds and a membership function. This membership function represents the degree to which a production scenario is plausible. In the second stage, conventional efficiency measurement models (Banker, Charnes and Cooper, 1984; Deprins, Simar and Tulkens, 1984) are re-formulated in terms of the risk-free and impossible bounds and the membership function for each of the fuzzy input and output variables. In the third stage, technical efficiency scores are computed for different values of the membership function so as to identify uniquely sensitive decision making units. The approach is illustrated in the context of a preprint and packaging manufacturing line which inserts commercial pamphlets into newspapers.

Assessing Aggregate Cost Efficiency And The Related Policy Implications For Greek Local Municipalities

The assessment of local government performance is a major issue for a number of industrial countries since local municipalities are assuming increasingly more responsibility in terms of providing e...

Manufacturing performance measurement and target setting: A data envelopment analysis approach

Manufacturing decision makers have to deal with a large number of reports and metrics for evaluating the performance of manufacturing systems. Since the metrics provide different and at times conflicting assessments, it is hard for the manufacturing decision makers to track and improve overall manufacturing system performance. This research presents a data envelopment analysis (DEA) based approach for performance measurement and target setting of manufacturing systems. The approach is applied to two different manufacturing environments. The performance peer groups identified using DEA are utilized to set performance targets and to guide performance improvement efforts. The DEA scores are checked against past process modifications that led to identified performance changes. Limitations of the DEA based approach are presented when considering measures that are influenced by factors outside of the control of the manufacturing decision makers. The potential of a DEA based generic performance measurement approach for manufacturing systems is provided.

Dominance-based measurement of productive and environmental performance for manufacturing

Abstract The concept of efficiency measurement is based on the definition of a frontier that envelopes observed production plans. The effect of pollution prevention and environmental compliance on productive efficiency is typically studied by considering pollution as not freely disposable (i.e., there is a cost incurred to dispose of pollution) or by assigning shadow prices to pollution outputs. However, the frontier along with the required technological assumptions needed for its definition may be replaced with the concept of pairwise dominance. With data from a manufacturing facility, the use of pairwise dominance allows one to consider a wide spectrum of inputs and outputs. Furthermore, the approach of benchmark correspondence is augmented so as to consider environmental performance. Pairwise dominance is applied to segregate production plans into sets according to their relative environmental and productive efficiency performance. These sets in conjunction with appropriately identified reference production plans are used to define distance-based measures of efficiency and environmental performance. Pollution prevention activities of a printed circuit board manufacturing facility motivated the development of the reported analytical framework.

Large-scale data envelopment analysis (DEA) implementation: a strategic performance management approach

We present one of the first large-scale implementations of data envelopment analysis (DEA) at the heart of a permanent performance management system in its third year of operation. The system evaluates more than 1000 field unit operations devoted to disaster relief, emergency communications, and life-saving skills training. The following research objectives were accomplished: (a) advanced a conceptual model for measuring performance in the nonprofit sector; (b) adapted a DEA formulation to account for differences in the operational environment of the field units, and included service quality, and effectiveness measures alongside traditional efficiency measures; and (c) created from scratch data collection (service quality and outcome achievement survey instruments) and report generation tools necessary for the deployment of evaluation results to the field in an user-friendly format for managers. While the suitability of DEA for real-life performance measurement is demonstrated, challenges of a DEA implementation are also discussed.

Data Envelopment Analysis as a Decision-Making Tool for Transportation Professionals

Data envelopment analysis (DEA) is a mathematical method based on production theory and the principles of linear programming. It enables one to assess how efficiently a firm, organization, agency, or such other unit uses the resources available (inputs) to generate a set of outputs relative to other units in the data set. Recent papers by different writers present different applications of DEA in the transportation engineering domain. All of these papers are published in transportation journals. These papers are mainly aimed at addressing the transportation-related issues and thus do not focus too much on the DEA concept itself. It can be asserted that DEA is very likely to be used more and more in the transportation engineering domain. Given this, there is a need for the transportation professionals to fully understand the DEA concept. It is essential for such a community to identify cases where the application of this innovative and powerful method can be useful to help the decision-making process, to accurately apply DEA in a particular setting, to derive meaningful conclusions from the obtained results, and to acknowledge the limitations of DEA in certain cases so as to approach the results with caution. The purpose of this paper is to illustrate to the civil engineering, more specifically to the transportation engineering community the use of this powerful approach in performing comparative performance measurement. Within this context, this paper will address a transportation-related problem by using the DEA approach. Different from the other papers containing transportation-related DEA applications (as mentioned above), this paper will discuss, in detail, the steps that need to be taken to generate the DEA model and solve it.

A Fuzzy Clustering Approach Used in Evaluating Technical Efficiency Measures in Manufacturing

Comparing analytical approaches is crucial when important policy decisions of corporations or government agencies may be influenced by results that depend on the methodologies certain disciplines customarily use. Technical efficiency can be measured by a full-frontier production function model or by linear programming specifications. By using these modeling approaches observations pertaining to three linerboard manufacturing facilities are classified as efficient, inefficient, scale inefficient, and other. However, observations may or may not be consistently classified into these four groups when employing the two modeling approaches. In order to validate the efficiency designations of the two modeling approaches and to determine the uniqueness of observations, a fuzzy K-means clustering approach that uses a modified hat matrix H* as a similarity or information matrix is employed. This approach permits observations to be allocated to clusters in a fuzzy way by defining a membership function from 0 to 1. As the degree of fuzziness increases, a sensitivity analysis with respect to individual observations belonging to some cluster can be evaluated. At the same time, this fuzzy approach assists the analyst to assess the inconsistencies that can arise when using the mathematical programming and full-frontier modeling approaches of technical efficiency.

Modeling social services performance: a four-stage DEA approach to evaluate fundraising efficiency, capacity building, service quality, and effectiveness in the nonprofit sector

Managers in nonprofit human and social service organizations are increasingly tasked with the design of performance measurement systems in an attempt to monitor aspects of their day-to-day operations. Monitoring key performance areas is said to improve the chances of sustainability and to provide early warnings of managerial and operational problems in tough fundraising environments. The data envelopment analysis (DEA) modeling framework described by this research was created for social service organizations that operate a network of field offices and are spatially distributed but provide similar services under the same managerial format. We define and assess multiple performance dimensions and more specifically, fundraising efficiency, capacity building, service quality and effectiveness (outcome achievement). The performance with respect to each of the four dimensions is represented as a separate stage where a DEA formulation is provided at each stage. Central to each formulation is the need to account for the influence of key environmental socio-economic factors and for the incorporation of the customer’s voice that is obtained through service quality and effectiveness questionnaires. Results from the implementation of the proposed framework in hundreds of field offices of one of the largest nonprofit organizations in the United States, suggest that social service nonprofits have a harder time being efficient in the fundraising area than in any of the other three areas of activity but that efficient fundraising is not a guarantee for efficient and high quality service delivery, nor it is guarantee of client outcome achievement (effectiveness).

The Implications of Using Messy Data to Estimate Production-Frontier-Based Technical Efficiency Measures

An empirical study of efficiency at the plant level, requiring production and financial data, was done using frontier function specifications. It is not evident from the implementation of the production-frontier models that different methodologies will consistently flag the same observations as being efficient or inefficient. As a result, outlier diagnostics for individual observations and for subsets of observations are used to achieve a relative index of influentiality within the spectrum of efficiency. These outlier diagnostic tests consistently flag the same subset of efficient and inefficient observations as the frontier models and additionally clarify ranking discrepancies among the frontier model specifications.

A conceptual framework to evaluate performance of non-profit social service organisations

The objective of this research is to provide a conceptual framework that can be used for the design and implementation of an integrated performance measurement system for non-profit service providers in general and in particular for disaster relief agencies. This conceptual framework contains four main dimensions of performance, i.e., revenue generation, capacity creation, customer satisfaction and outcome achievement. Lastly, a modelling approach that can be used to formulate and analyse the data collected through this system using Data Envelopment Analysis (DEA) (Charnes et al., 1978) is proposed. DEA performance measures will consider the operating environment and elicit quantitative best practices. The conceptual framework, analytical and modelling approaches address a void on the literature given that several programme and performance evaluation initiatives that have been launched in the past do not offer a solution as to how to evaluate different performance dimensions relevant for the sector.