In his 2003 book Moneyball: The Art of Winning an Unfair Game, Michael Lewis described how the Oakland Athletics baseball team used statistical analysis to identify undervalued players.1 One lesson from the baseball world of “moneyball” is that we can’t always trust our intuition about how employees will perform. Savvy business managers know that their intuition can often be misleading, if not downright incorrect. And just as sports teams have increasingly relied on rigorous quantitative analyses, so have many businesses.
In particular, a growing number of service businesses have been investigating the use of a sophisticated linear programming technique called DEA, or data envelopment analysis. (In this article, we use the term “balanced benchmarking” to denote DEA.) The technique enables companies to benchmark and locate best practices that are not visible through other commonly used management methodologies. (See “The Basics of Balanced Benchmarking.”)
When it was first introduced in the 1980s,2 balanced benchmarking was an academic tool for measuring and managing the relative efficiency of peer organizations. Balanced benchmarking required the adaptation of various computer programs, so its use in the 1980s was limited to a small group of academics and practitioners with linear programming expertise. Early users were able to apply and generate results from balanced benchmarking that demonstrated its effectiveness, but its inaccessibility limited its independent adoption and application by managers. However, shortly after 2000, balanced-benchmarking algorithms were adapted for Excel software — making it accessible to users with little or no knowledge of linear programming.3
Balanced benchmarking is unique both in its ability to identify paths to improve productivity and in its value as a complement to other analytic techniques. Balanced benchmarking simultaneously considers the multiple resources used to generate multiple services, along with the quality of the services provided. For example, bank branches can use six or more types of resources and provide 20 or more types of services, all of which are considered with balanced benchmarking.
1. M. Lewis, “Moneyball: The Art of Winning an Unfair Game” (New York and London: W.W. Norton & Co., 2003).
2. A. Charnes, W.W. Cooper and E. Rhodes, “Measuring the Efficiency of Decision Making Units,” European Journal of Operational Research 2, no. 6 (November 1978): 429-444.
3. To apply balanced benchmarking, visit www.deafrontier.net and also refer to J. Zhu, “Quantitative Models for Performance Evaluation and Benchmarking: Data Envelopment Analysis With Spreadsheets,” 2nd ed. (Boston: Springer, 2009), chap. 1.
4. D. Grewal, M. Levy, A. Mehrotra and A. Sharma, “Planning Merchandising Decisions to Account for Regional and Product Assortment Differences,” Journal of Retailing 75, no. 3 (autumn 1999): 405-424.
5. K.R. Sharma, P.S. Leung, H. Chen and A. Peterson, “Economic Efficiency and Optimum Stocking Densities in Fish Polyculture: An Application of Data Envelopment Analysis (DEA) to Chinese Fish Farms,” Aquaculture 180, nos. 3-4 (November 1999): 207-221.
6. G.N. Gregoriou, K. Sedzro and J. Zhu, “Hedge Fund Performance Appraisal Using Data Envelopment Analysis,” European Journal of Operational Research 164, no. 2 (July 16, 2005): 555-571.
7. B. Golany, Y. Roll and D. Rybak, “Measuring Efficiency of Power Plants in Israel by Data Envelopment Analysis,” IEEE Transactions on Engineering Management 41, no. 3 (August 1994): 291-301.
8. K.W. Einolf, “Is Winning Everything? A Data Envelopment Analysis of Major League Baseball and the National Football League,” Journal of Sports Economics 5, no. 2 (May 2004): 127-151.
9. T.R. Sexton, A.M. Leiken, A.H. Nolan, S. Liss, A. Hogan and R.H. Silkman, “Evaluating Managerial Efficiency of Veterans Administration Medical Centers Using Data Envelopment Analysis,” Medical Care 27, no. 12 (December 1989): 1175-1188.
10. H.D. Sherman and J. Zhu, “Service Productivity Management: Improving Service Performance Using Data Envelopment Analysis (DEA)” (Boston: Springer, 2006).
11. R.S. Kaplan and D.P. Norton, “The Balanced Scorecard: Translating Strategy Into Action” (Boston: Harvard Business Press, 1996).
i. The efficiency score of 85.7% represents the potential fraction of resources that the inefficient unit can use to become as efficient as a combination of the best practice units. Essentially, the 85.7% rating means the unit can reduce resources by 14.3% [100%−85.7%].
ii. H.D. Sherman, “Improving the Productivity of Service Business,” Sloan Management Review 25, no. 3 (spring 1984): 11-23; Sherman and Zhu, “Service Productivity Management,” chap. 2; and Zhu, “Quantitative Models.”