1. Performance Evaluation and Benchmarking with Data Envelopment Analysis Chapter 15

2. Multi-Site Performance Evaluation Multi-site evaluation technique: –Data Envelopment Analysis 1Chapter 15 - Performance Evaluation and Benchmarking with Data Envelopment Analysis

3. Multi-Site Services FranchisedOwned Midas (brake/muffler repair)2,237345 Budget Rent-A-Car2,574401 Management recruiters/57045 sales consultants (executive search firms) McDonald ’ s15,0005,000 Barclay ’ s Bank2,700 (total – approx.) 2Chapter 15 - Performance Evaluation and Benchmarking with Data Envelopment Analysis

4. FranchisedOwned Novus windshield repair1,88518 Subway (sandwiches)10,8900 Century 21 Real Estate Corp.6,0940 Re/Max International (real estate)2,5090 Uniglobe Travel (travel agents)1,1290 Multi-Site Services 3Chapter 15 - Performance Evaluation and Benchmarking with Data Envelopment Analysis

5. Performance Evaluation Purposes –Evaluation units - employees –Resource Allocation rationalize personnel/capital expense control unit closure –Classification recognition/reward identification 4Chapter 15 - Performance Evaluation and Benchmarking with Data Envelopment Analysis

6. Performance Evaluation Measures –Profit –Sales volume –Contribution margin –Customer service –Market share Methods –Negotiated goals –Outputs (neglecting inputs available) 5Chapter 15 - Performance Evaluation and Benchmarking with Data Envelopment Analysis

7. Data Envelopment Analysis (DEA) Use – efficiency evaluation for multi-site service firms Conditions for use: –Results ambiguity –Results measurement incompatibility –Service unit similarity 6Chapter 15 - Performance Evaluation and Benchmarking with Data Envelopment Analysis

8. Advantages of DEA DEA Output –Single number –Most favorable linear combination of outputs/inputs to unit compared to the outputs/inputs of all other units Advantages –Data reduction –Objectivity –Environmental change response –Doesn’t reward sand-bagging –Doesn’t punish superior performers 7Chapter 15 - Performance Evaluation and Benchmarking with Data Envelopment Analysis

9. Applications of DEA Non-profit –Education, health care, armed forces, public housing, transportation, facility location (superconducting supercollider) For-profit –Banking, retail, mining, agriculture Users ( “ Frontier Analyst ” software by Banxia) –AMEC Offshore Development, Ameritech, Banca Populare diMilano, Bank of Scotland, Boston Consulting Group, British Gas Transco, CalEnergy Company Inc., Carlson Marketing Group… 8Chapter 15 - Performance Evaluation and Benchmarking with Data Envelopment Analysis

10. DEA in Retail Banking Al ‑ Faraj, T., A. Alidi and K. Bu ‑ Bshait (1993), “ Evaluation of Bank Branches by Means of Data Envelopment Analysis, ” International Journal of Operations & Production Management, 13, 9, 45 ‑ 52. Athanassopoulos, A. (1997), “ Service Quality and Operating Efficiency Synergies for Management Control in the Provision of Financial Services: Evidence from Greek Bank Branches, ” European Journal of Operational Research, 98, 300-313. Chase, R., G. Northcraft and G. Wolf (1984), “ Designing High-Contact Service Systems: Application to Branches of a Savings and Loan, ” Decision Sciences, 15, 542-555. Drake, L. and B. Howcroft (1994), “ Relative efficiency in the Branch Network of a UK Bank: An Empirical Study, ” Omega, 22, 1, 83 ‑ 90. Giokas, D. (1991), “ Bank Branch Operating Efficiency: A Comparative Application of DEA and the Loglinear Model, ” OMEGA, 19, 6, 549-557. Haag, S. and P. Jaska (1995), “ Interpreting Inefficiency Ratings: an Application of Bank Branch Operating Efficiencies, ” Managerial and Decision Economics, 16, 7 ‑ 14. Parkan, C. (1994), “Operational Competitiveness Ratings of Production Units,” Managerial and Decision Economics, 15, 201 ‑ 221. Pastor, J. (1994), “How to Discount Environmental Effects in DEA: An Application to Bank Branches,” Working Paper, Universidad de Alicante, Alicante, Spain. Roll, Y. and B. Golany (1993), “Alternative Methods of Treating Factor Weights in DEA,” Omega, 21, 1, 99 ‑ 109. Schaffnit, C., D. Rosen and J. Paradi (1997), “Best Practice Analysis of Bank Branches: An Application of DEA in a Large Canadian Bank,” European Journal of Operational Research, 98, 269-289. Sherman, H. (1984), “Improving the Productivity of Service Businesses,” Sloan Management Review, 11 ‑ 22. Sherman, H. and F. Gold (1985), “Bank Branch Operating Efficiency,” Journal of Banking and Finance, 9, 297 ‑ 315. Sherman, H. and G. Ladino (1995), “Managing Bank Productivity Using Data Envelopment Analysis (DEA)”, Interfaces, 25, 2, 60 ‑ 73. 9Chapter 15 - Performance Evaluation and Benchmarking with Data Envelopment Analysis

11. Structure of DEA Models Efficiency = Outputs/Inputs Efficiency rating from 0 (worst) to 1 (best) Non-linear programming model Maximize Outputs/Inputs of a specific service unit s.t. Outputs/Inputs  1 for every service unit No a priori weighting of outputs or inputs assumed 10Chapter 15 - Performance Evaluation and Benchmarking with Data Envelopment Analysis

12. Structure of DEA Model Linear model –constants: outputs, inputs variables: output weights, input weights Analyze units one at a time Maximize Outputs i x Output weight (specific unit j) s.t. [(outputs i x output weight)/(inputs i x input weight)  1] (outputs i x output weight) – (inputs i x input weight)  0 for all other units Inputs j x input weight = 1 for specific unit j 11Chapter 15 - Performance Evaluation and Benchmarking with Data Envelopment Analysis

13. DEA Example Problem Data BranchInputsLoansDeposits A100$10$31 B1001525 C1002030 D10023 E1003020 12Chapter 15 - Performance Evaluation and Benchmarking with Data Envelopment Analysis

14. DEA Example Problem Graph 35 30 25 20 15 10 5 051015202530 A B C D E HCU B HCU D Deposits Loans 13Chapter 15 - Performance Evaluation and Benchmarking with Data Envelopment Analysis

15. DEA Example Problem Data BranchLoansDepositsEfficiency A$10$311 B15250.83 C20301 D23 0.92 E30201 14Chapter 15 - Performance Evaluation and Benchmarking with Data Envelopment Analysis

16. DEA Example Problem Maximize 15 loan weight + 25 deposit weight s.t. 10 loan weight + 31 deposit weight – 100 inputs  0 15 loan weight + 25 deposit weight – 100 inputs  0 20 loan weight + 30 deposit weight – 100 inputs  0 23 loan weight + 23 deposit weight – 100 inputs  0 30 loan weight + 20 deposit weight – 100 inputs  0 100 inputs = 1 15Chapter 15 - Performance Evaluation and Benchmarking with Data Envelopment Analysis

17. BranchLoansDepositsEfficiencySlackShadow Price A$10$3100.16 B15250.83.170 C203000.67 D23.210 E3020.280 Variables (weights): Loans = 0.00313 Deposits = 0.03125 Breakdown of efficiency: Loans = 0.00313 x 15 = 0.05 Deposits = 0.03125 x 25 = 0.78 Reference set: A and C 16Chapter 15 - Performance Evaluation and Benchmarking with Data Envelopment Analysis DEA Example Problem

18. Modeling Considerations Strategic Link Variable number rule: –Observations > 2x(outputs + inputs) Unit Similarity: Scales economies/diseconomies 17Chapter 15 - Performance Evaluation and Benchmarking with Data Envelopment Analysis

19. Model Adaptations Bounding Variable Weights –Example: at most 70% of total efficiency from loans Maximize 15 loan weight + 25 deposit weight s.t. 10 loan weight + 31 deposit weight – 100 inputs  0 15 loan weight + 25 deposit weight – 100 inputs  0 20 loan weight + 30 deposit weight – 100 inputs  0 23 loan weight + 23 deposit weight – 100 inputs  0 30 loan weight + 20 deposit weight – 100 inputs  0 100 inputs = 1 15 loan weight/ (15 loan weight + 25 deposit weight)  0.7 Rearranging terms 4.5 loan weight – 17.5 deposit weight  0 18Chapter 15 - Performance Evaluation and Benchmarking with Data Envelopment Analysis

20. Linear Programming on Excel 1 st time through: Tools, Solver Target cell (objective function) D28 [tab] By changing cells (variables) C23:J23 [tab] Subject to… AddC23:J23 ≥ 0) K9:K18  0 K21 = 1 Options, Assume Linear Model Solve After 1 st time Copy appropriate information down, Tools, Solver, Solve 19Chapter 15 - Performance Evaluation and Benchmarking with Data Envelopment Analysis