Richard C. Morey

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.

Evaluating the administrative efficiency of courts

In addition to being held accountable for judicial decision, courts, like other public agencies, can and should be evaluated in terms of their administrative efficiency. This paper illustrates how courts can be evaluated in terms of their relative administrative efficiency, using a new approach--data envelopment analysis (DEA)--first proposed by Charnes et al. [1]. The DEA is based upon the economic notion of Pareto optimality which states that a given decision making unit (DMU) is inefficient if some other DMU, or some combination of other DMUs, can produce at least the same amounts of all outputs with less of some resource input and not more of any other resource. Conversely a DMU is said to be efficient if the above is not possible. Charnes et al. [1] generalized the usual input/output ratio measure of efficiency for a given unit in terms of a fractional linear program with fractional constraints. In the case of courts, the efficiency of any particular court is calculated by forming the ratio of a weighted sum of outputs to a weighted sum of inputs, where the weights for both outputs and inputs are to be selected in a manner that calculates the Pareto-Koopmans efficiency of the court. This paper reviews the DEA method and illustrates its application to a data base for 100 criminal superior courts in North Carolina.

Evaluating a hotel GM's performance: a case study in benchmarking.

Because every market is different, evaluating a general managers performance is complex. By controlling for market differences with a linear programming model, a chain can develop benchmarks for a...

Sensitivity and Stability Analysis in DEA

Abstract : Sensitivity analyses may be regarded as a mathematical programming counterpart of significance testing in statistics since each is concerned with examining allowable ranges of variation in the data. In statistical analysis, this may take the form of examining ranges of possible chi sq values obtained from the data relative to a fitted function from and hypothesized class of statistical distributions. In linear programming, it may take the form of ascertaining ranges within which data may be varied without requiring a change in the set of vectors that constitute an optimum basis. Additional keywords: Reprints.

Estimating and improving the quality of information in a MIS

Most discussions of MISs assume that the information in the records is error-free although it is recognized that errors exist. These errors occur because of delays in processing times, lengthy correction times, and, overly or insufficiently stringent data edits. In order to enable the user to implement data edits and correction procedures tailored to the degree of accuracy needed, this paper presents functional relationships between three common measures of data quality. The MIS addressed is one where records in a MIS are updated as changes occur to the record, e.g., a manpower planning MIS where the changes may relate to a servicemans rank or skills. Since each of the updating transactions may contain an error, the transactions are subjected to various screens before the stored records are changed. Some of the transactions including some that are correct, are rejected; these are reviewed manually and corrected as necessary. In the meantime, the record is out of date and in error. Some of the transactions that were not rejected also lead to errors. The result is that at any given time the MIS record may contain errors. For each of several error control mechanisms, we show how to forecast the level of improvement in the accuracy of the MIS record if these options are implemented.

THE TRADE-OFF BETWEEN HOSPITAL COST AND QUALITY OF CARE : AN EXPLORATORY EMPIRICAL ANALYSIS

The debate concerning quality of care in hospitals, its “value” and affordability, is increasingly of concern to providers, consumers, and purchasers in the United States and elsewhere. We undertook an exploratory study to estimate the impact on hospital-wide costs if quality-of-care levels were varied. To do so, we obtained costs and service output data regarding 300 U.S. hospitals, representing approximately a 5% cross section of all hospitals operating in 1983; both inpatient and outpatient services were included. The quality-of-care measure used for the exploratory analysis was the ratio of actual deaths in the hospital for the year in question to the forecasted number of deaths for the hospital; the hospital mortality forecaster had earlier (and elsewhere) been built from analyses of 6 million discharge abstracts, and took into account each hospitals actual individual admissions, including key patient descriptors for each admission. Such adjusted death rates have increasingly been used as potential indicators of quality, with recent research lending support for the viability of that linkage. The authors then utilized the economic construct of allocative efficiency relying on “best practices” concepts and peer groupings, built using the “envelopment” philosophy of Data Envelopment Analysis and Pareto efficiency. These analytical techniques estimated the efficiently delivered costs required to meet prespecified levels of quality of care. The marginal additional cost per each death deferred in 1983 was estimated to be approximately

Estimating service level impacts from changes in cycle count, buffer stock, or corrective action

29,000 (in 1990 dollars) for the average efficient hospital. Also, over a feasible range, a 1% increase in the level of quality of care delivered was estimated to increase hospital cost by an average of 1.34%. This estimated elasticity of quality on cost also increased with the number of beds in the hospital.

A goal-programming method of stochastic allocative data envelopment analysis

Abstract Inventory managers, tasked with providing adequate levels of material support, need to be able to gain insights, in a computationally easy manner, to the magnitude of improvements in protection levels against stockouts that can be gleaned from the three mechanisms available to them—more buffer stock, more physical inventories, or more corrective action to eliminate or reduce the causes for the errors arising in the first place. Armed with quantitative conservative estimates of the types of improvements available from any of these three mechanisms or combinations of the mechanisms, and the relative cost of each, the manager is is a better position to select a cost-effective, remedial course of action.

ESTIMATING THE HOSPITAL-WIDE COST DIFFERENTIALS WARRANTED FOR TEACHING HOSPITALS : AN ALTERNATIVE TO REGRESSION APPROACHES

Abstract Allocative Data Envelopment Analysis (ADEA) is a version of Data Envelopment Analysis (DEA) which measures relative efficiency for a group of similar operating units with known input prices. By using the actual input values, ADEA provides information to managers on the minimum cost method of operation for each unit. A major criticism of DEA methods is that they are deterministic and have no means of allowing for uncertainty. This paper applies the goal-programming approach, introduced by Banker (1991), to allocative efficiency and develops the Stochastic ADEA model. A two-stage solution method is introduced, which is needed because of the existence of alternate optimal solutions regarding which units are found to be significantly inefficient. We propose that identifying the significantly inefficient units is most useful to managers because it best facilitates improved efficiency. The concept of a minimum frontier is introduced and used to define the significantly inefficient units. We also show how bounds can be imposed which allow the ambiguity of the noise/inefficiency trade-off to be eliminated from the objective function. The use of bounds also allows the identification of the significantly inefficient unit based on the amount of uncertainty present for each operating unit. As a result of optimizing cost, this method has the important advantage of being ideally suited for multiple outputs.

Reimbursement rate setting for Medicaid prescription drugs based on relative efficiencies

Under Medicares Prospective Payment System, teaching hospitals receive additional reimbursements, vis-à-vis nonteaching hospitals, for both “direct” teaching expenses and for “indirect” expenses. They totaled