Boaz Golany

FOUNDATIONS OF DATA ENVELOPMENT ANALYSIS FOR PARETO-KOOPMANS EFFICIENT EMPIRICAL PRODUCTION FUNCTIONS

The construction and analysis of Pareto-efficient frontier production functions by a new Data Envelopment Analysis method is presented in the context of new theoretical characterizations of the inherent structure and capabilities of such empirical production functions. Contrasts and connections with other developments, including solutions of some remaining problems, are made re aspects such as informatics, economies of scale, isotonicity and non-concavity, discretionary and non-discretionary inputs, piecewise linearity, partial derivatives and Cobb-Douglas properties of the functions. Non-Archimedean constructs are nor required.

A developmental study of data envelopment analysis in measuring the efficiency of maintenance units in the U.S. air forces

Abstract : There are four basic questions related to efficiency and capability which are of particular interest to officials in the military services who are interested in better ways of evaluating military capability and efficiency: (1) What level of military capability can the services achieve with available resources? (2) What capability is required, and where are the shortfalls? (3) What resource acquisitions or redistributions are needed to achieve maximum improvement in efficiency and effectiveness? and (4) How can management systems be changed to improve the identification and correction of factors which limit the readiness and efficiency of our military operations? The last question, which differs in its emphasis from the other three, provides an opening to the topics that will be addressed in this report. In particular, reported are results from a study of DEA (Data Envelopment Analysis) as a method for evaluating the efficiency of Air Force Wings--or, more precisely, their maintenance operations--as elements in Numbered Units in the U.S. Air Force.

Controlling Factor Weights in Data Envelopment Analysis

Abstract Data Envelopment Analysis (DEA) is a mathematical programming approach to assessing relative efficiencies within a group of Decision Making Units (DMUs). An important outcome of such an analysis is a set of virtual multipliers or weights accorded to each (input or output) factor taken into account. These sets of weights are, typically, different for each of the participating DMUs. A version of the DEA model is offered where bounds are imposed on weights, thus reducing the variation in the importance accorded to the same factor by the various DMUs. Techniques for locating appropriate bounds are suggested and the notion of a common set of weights is examined. Possible interpretations to differences in efficiency ratings obtained with the various models developed are discussed.

Evaluation of deregulated airline networks using data envelopment analysis combined with principal component analysis with an application to Western Europe

Abstract US experience shows that deregulation of the airline industry leads to the formation of hub-and-spoke (HS) airline networks. Viewing potential HS networks as decision-making units, we use data envelopment analysis (DEA) to select the most efficient networks configurations from the many that are possible in the deregulated European Union airline market. To overcome the difficulties that DEA encounters when there is an excessive number of inputs or outputs, we employ principal component analysis (PCA) to aggregate certain, clustered data, whilst ensuring very similar results to those achieved under the original DEA model. The DEA–PCA formulation is then illustrated with real-world data gathered from the West European air transportation industry.

Constructing and evaluating balanced portfolios of R&D projects with interactions: A DEA based methodology

We propose and demonstrate a methodology for the construction and analysis of efficient, effective and balanced portfolios of R&D projects with interactions. The methodology is based on an extended data envelopment analysis (DEA) model that quantifies some the qualitative concepts embedded in the balanced scorecard (BSC) approach. The methodology includes a resource allocation scheme, an evaluation of individual projects, screening of projects based on their relative values and on portfolio requirements, and finally a construction and evaluation of portfolios. The DEA–BSC model is employed in two versions, first to evaluate individual R&D projects, and then to evaluate alternative R&D portfolios. To generate portfolio alternatives, we apply a branch-and-bound algorithm, and use an accumulation function that accounts for possible interactions among projects. The entire methodology is illustrated via an example in the context of a governmental agency charged with selecting technological projects. � 2005 Elsevier B.V. All rights reserved.

Alternate methods of treating factor weights in DEA

Provisions for controlling factor weights constitute a significant extension of the data envelopment analysis (DEA) methodology, as an effective tool for measuring efficiency. This paper suggests a conceptual framework for the treatment of factor weights in DEA. First, the paper proposes general guidelines for setting bounds on factor weights. Then, it develops and presents alternative methods to limit the range within which these factor weights are allowed to vary. All of these methods involve additional information which is entered into the analysis in the form of constraints, bounds or different objective functions. Finally, the implications of the various approaches is discussed.

Consistent weights for judgements matrices of the relative importance of alternatives

We prove that the only solution satisfying consistency axioms for the problem of retrieving weights from inconsistent judgements matrices whose entries are the relative importance ratios of alternatives is the geometric mean.

A Data Envelopment Analysis of the Operational Efficiency of Bank Branches

We performed a multiperiod data envelopment analysis (DEA) study of the efficiencies of selected branches of a large US bank (which we will call Big Bank) over six consecutive quarters (second quarter of 1992 to the third quarter of 1993). We developed a number of innovative application tools, including budgeting and target-setting modules, within a DEA framework that we subsequently customized for Big Bank. Within the performance-evaluation framework, we developed the ability to identify different potential groupings of branches that supported the multiple user views of the system. We also developed procedures to evaluate trends over time and differences in performance across user-defined aggregations of branches. We paid attention to the interface with the end users and, in particular, developed presentation tools to make the outcomes of the analysis available to managers at different levels of the bank.

ERP modeling: a comprehensive approach

We present a generic reverse engineering process, aimed at developing a model that captures the available alternatives at different application levels of an Enterprise Resource Planning (ERP) system. Such a model is needed when ERP systems are aligned with the needs of the enterprise in which they are implemented. In order to support the ERP implementation process, the model should describe the entire scope of the ERP systems functionality and the alternative business processes it supports, as well as the interdependencies among them. We analyze the desired properties a modeling language should satisfy to be applied in constructing an ERP system modal. This analysis, which follows the Cooperative Requirements Engineering With Scenarios classification framework in its adapted ERP modeling form, results in a set of criteria for evaluating modeling languages for this purpose. Using these criteria, we evaluate the Object-Process modeling Methodology and apply it for generating a detailed ERP system model. The generic process and detailed criteria we develop can serve for comprehensive ERP modeling, as well as for obtaining a model of other process-supportive off-the-shelf systems that are of generic and configurable nature.

Ahp Rank Reversal, Normalization and Aggregation Rules

AbstractWe analyze the Belton and Gear rank reversal problem within an axiomatic framework for deriving consistent weight ratios from pairwise ratio matrices and aggregating weights and ratio matrices. We show that rank reversal in the Analytic Hierarchy Process (AHP) is avoided when the output of the process is properly redefined as a weight-ratio matrix (rather than a normalized-weight vector) and multiplicative procedures – the geometric mean and the weighted-geometric-mean aggregation rule – which preserve the underlying mathematical structures are used.