Measurement of multiple sites in service firms with data envelopment analysis

Production and Operations Management, Fall 1999 by Metters, Richard C, Frei, Frances X, Vargas, Vicente A

MEASUREMENT OF MULTIPLE SITES IN SERVICE FIRMS WITH DATA ENVELOPMENT ANALYSIS*

Data envelopment analysis (DFA) has become an increasingly popular method to measure performance for service firms with Multiple SiteS. DEA is superior to many traditional methods for firms that have multiple goals. The Promise Of DEA is that the complex, multiobjective problem of performance measurement can be reduced to a single number. Unfortunately, the practice Of DEA often belies the

promise. Misconceptions concerning the purpose and implementation Of DEA can cause DEA applications to be less than successful. Here, the technique is explained, and a guide to the implementation Of DEA is proposed, utilizing DEA studies of retail bank branches.

(SERVICE OPERATIONS; DATA ENVELOPMENT ANALYSIS; PRODUCTIVITY)

1. Introduction

One distinguishing characteristic of service versus manufacturing firms is the number of physical sites that constitute a single firm. Multinational manufacturing giants in the largest manufacturing enterprises, such as the auto, steel, chemical, or paper industries, may have scores, perhaps a hundred or so manufacturing/assembly plants in a single firm. In contrast, the leaders in many service industries routinely have multiple hundreds or thousands of brick and mortar sites where services are created. The largest restaurant chain has over 20,000 sites; the largest banks and retail stores have over 3,000. The leaders in lesser known industries, such as repair services, cleaning services, and personnel services, have multiple thousands of sites.

The sheer volume and associated geographic dispersion of sites creates managerial difficulties. Unlike many centralized back-offices or large manufacturing plants, it is no longer possible to "manage by walking around." Managerial "gut feel" and subjective performance measures cease to be useful when the evaluator is rarely physically present at the unit being evaluated.

For these geographically disperse units, many of the seemingly objective performance measures used by many firms also have severe drawbacks (Achabal, Heineke, and McIntyre 1984). Accounting profit, or the associated unit ROA or ROE, is a common output measure. However, individual unit profits can be highly dependent on decisions that are uncontrollable by the unit, such as pricing, product mix, and trade area competitive and economic factors (Kamakura, Lenartowicz, and Ratchfrordm 1996). Further, other outputs are also typically important, such as market share, customer service, cost containment, or gross sales growth, among many others (Good 1984). Myopic focus on unit profitability can induce unwanted behavior. It can be a simple matter for a rarely seen, remote service unit to "brand shirk" by having fewer personnel relative to other units of the firm, which can increase accounting profit of that unit but lower customer service levels, which could affect future system-wide sales.

In this paper we focus on the retail banking industry as an example service industry with such problems. There is considerable debate in the banking practitioner literature as to both how to construct bank branch profitability statements and their worth in evaluating performance, with many practitioners disregarding a branch ROE or ROA as meaningless (Schultz and Chelst 1994; Pihl and Whitmyer 1994; Thygerson 1991; Witzeling 1991). Even if accounting profit could be accurately measured, branches within a given banking system have differing missions that would preclude considering profit alone (Sherman and Ladino 1995; Oral, Kettani, and Yolalan 1992).

Even if the all the differing performance measures accurately assess the performance of a unit, distortions can arise in implementing those measures. Outputs are often assessed in one of three ways: comparing to unit goals, comparing with results from a prior time period, or by a gross comparison of outputs between units. All of these methods have serious flaws. Unit goals are frequently set by negotiation. Comparing results to negotiated goals can reward the good goal negotiators, rather than the good performers. Basing performance evaluation on prior time period results, for example, a goal of "last year plus 10%," encourages "sandbagging," or purposely not overly exceeding a goal in the current time period so that the next time period goal will not be as strenuous (noted empirically by Lovell and Pastor 1997, p. 292). If comparisons between units are made merely on output levels alone, managers of units with superior locations will appear to be superior, regardless of actual ability.

Another problem can arise in combining many disparate measures of success into an overall assessment. How much market share growth should be traded-off for each point of customer satisfaction? Should accounting profit constitute 30% or 50% of the overall evaluation score?

Data envelopment analysis (DEA) is a technique that shows promise as a possible solution for many of the problems listed above. Formally, DEA is a linear programming technique for measuring the relative efficiency of decision making units (DMUS) where each DMu has a multitude of desired outputs or needed inputs. In practical terms, one use Of DEA is as a measurement tool for multisite organizations when a single overall measure, such as accounting profit, is not sufficient. DEA combines numerous relevant outputs and inputs into a single number that represents productivity, or "efficiency."