Rajiv D. Banker

Efficiency analysis for exogenously fixed inputs and outputs

We evaluate, by means of mathematical programming formulations, the relative technical and scale efficiencies of decision making units DMUs when some of the inputs or outputs are exogenously fixed and beyond the discretionary control of DMU managers. This approach further develops the work on efficiency evaluation and on estimation of efficient production frontiers known as data envelopment analysis DEA. We also employ the model to provide efficient input and output targets for DMU managers in a way that specifically accounts for the fixed nature of some of the inputs or outputs. We illustrate the approach, using real data, for a network of fast food restaurants.

Estimating most productive scale size using data envelopment analysis

Abstract The relation between the most productive scale size (mpss) for paparticular input and output mixes and returns to scale for multiple-inputs multiple-outputs situations is explicitly developed. This relation is then employed to extend the applications of Data Envelopment Analysis (DEA) introduced by Charnes, Cooper and Rhodes (CCR) to the estimation of most productive scale sizes for convex production possibility sets. It is then shown that in addition to productive inefficiencies at the actual scale size, the CCR efficiency measure also reflects any inefficiencies due to divergence from the most productive scale size. Two illustrations of the practical applications of these results to the estimation of most productive scale sizes and returns to scale for hospitals and stem-electric generation plants are also provided emphasize the advantage of this method in examining specific segments of the efficient production surface.

Sensitivity, Precision, And Linear Aggregation Of Signals For Performance Evaluation

Several accounting and other signals are generally available for the construction of a managerial performance evaluation measure on which an optimal compensation contract is based. The demand for aggregation in evaluating managerial performance arises because reporting all the basic transactions and other nonfinancial information about performance is costly and impracticable (see Ashton [1982], Casey [1978], and Holmstrom and Milgrom [1987]). We identify necessary and sufficient conditions on the joint density function of the signals under which linear aggregation, a simple and commonly employed way to construct a performance evaluation measure, is optimal. This characterization suggests that the linear form of aggregation is optimal for a large class of situations. Focusing on performance measures that are linear aggregates enables us to determine the relative weights on the individual signals in the optimal linear aggregate, since these weights are invariant for all realizations of the signals. We interpret these weights in terms of statistical characteristics (sensitivity and precision) of the joint distribution of the signals.

Estimation of returns to scale using data envelopment analysis

Abstract Generalization of the measure of returns-to-scale from a single number to an interval permits extension of the concept to DEA data domains with multiple inputs and multiple outputs. The key new approach is a partition of the optimal frontier into three parts corresponding, respectively to increasing, constant, and decreasing returns to scale. These parts are characterized in terms of optimal primal solutions, and optimal dual solutions for both the original Charnes, Cooper, Rhodes model (1978) and the later Banker, Charnes, Cooper model (1984) and relying on concepts developed by R.D. Banker (1984) and R.M. Thrall (1988).

Evaluating Contextual Variables Affecting Productivity Using Data Envelopment Analysis

A DEA-based stochastic frontier estimation framework is presented to evaluate contextual variables affecting productivity that allows for both one-sided inefficiency deviations as well as two-sided random noise. Conditions are identified under which a two-stage procedure consisting of DEA followed by ordinary least squares (OLS) regression analysis yields consistent estimators of the impact of contextual variables. Conditions are also identified under which DEA in the first stage followed by maximum likelihood estimation (MLE) in the second stage yields consistent estimators of the impact of contextual variables. This requires the contextual variables to be independent of the input variables, but the contextual variables may be correlated with each other. Monte Carlo simulations are carried out to compare the performance of our two-stage approach with one-stage and two-stage parametric approaches. Simulation results indicate that DEA-based procedures with OLS, maximum likelihood, or even Tobit estimation in the second stage perform as well as the best of the parametric methods in the estimation of the impact of contextual variables on productivity. Simulation results also indicate that DEA-based procedures perform better than parametric methods in the estimation of individual decision-making unit (DMU) productivity. Overall, the results establish DEA as a nonparametric stochastic frontier estimation (SFE) methodology.

Are Selling, General, and Administrative Costs "Sticky"?

A fundamental assumption in cost accounting is that the relation between costs and volume is symmetric for volume increases and decreases. In this study, we investigate whether costs are “sticky”—that is, whether costs increase more when activity rises than they decrease when activity falls by an equivalent amount. We find, for 7,629 firms over 20 years, that selling, general, and administrative (SG&A) costs increase on average 0.55% per 1% increase in sales but decrease only 0.35% per 1% decrease in sales. Our analysis compares the traditional model of cost behavior in which costs move proportionately with changes in activity with an alternative model in which sticky costs occur because managers deliberately adjust the resources committed to activities. We test hypotheses about the properties of sticky costs and how the degree of stickiness of SG&A costs varies with firm circumstances.

Software complexity and maintenance costs

While the link between the difficulty in understanding computer software and the cost of maintaining it is appealing, prior empirical evidence linking software complexity to software maintenance costs is relatively weak [21]. Many of the attempts to link software complexity to maintainability are based on experiments involving small pieces of code, or are based on analysis of software written by students. Such evidence is valuable, but several researchers have noted that such results must be applied cautiously to the large-scale commercial application systems that account for most software maintenance expenditures [13,17]

Impact of Work Teams on Manufacturing Performance: A Longitudinal Field Study

We report the results of a longitudinal field study examining the impact of work teams on manufacturing performance. An electromechanical assembly plant was our research site from April 1992 throug...

Hypothesis tests using data envelopment analysis

A substantial body of recent work has opened the way to exploring the statistical properties of DEA estimators of production frontiers and related efficiency measures. The purpose of this paper is to survey several possibilities that have been pursued, and to present them in a unified framework. These include the development of statistics to test hypotheses about the characteristics of the production frontier, such as returns to scale, input substitutability, and model specification, and also about variation in efficiencies relative to the production frontier.

Plant information systems, manufacturing capabilities, and plant performance

Firms have been investing over