Execution Evaluation and Benchmarking with Data Envelopment Analysis .


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

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Multi-Site Performance Evaluation Multi-site assessment procedure: Data Envelopment Analysis Chapter 15 - Performance Evaluation and Benchmarking with Data Envelopment Analysis 1

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Multi-Site Services Franchised Owned Midas (brake/suppressor repair) 2,237 345 Budget Rent-A-Car 2,574 401 Management spotters/ 570 45 deals experts (official pursuit firms) McDonald " s 15,000 5,000 Barclay " s Bank 2,700 (total – approx.) Chapter 15 - Performance Evaluation and Benchmarking with Data Envelopment Analysis 2

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Multi-Site Services Franchised Owned Novus windshield repair 1,885 18 Subway (sandwiches) 10,890 0 Century 21 Real Estate Corp. 6,094 0 Re/Max International (genuine estate) 2,509 0 Uniglobe (travel agents) 1,129 0 Chapter 15 - Performance Evaluation and Benchmarking with Data Envelopment Analysis 3

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Performance Evaluation Purposes Evaluation units - workers Resource Allocation justify faculty/capital cost control unit conclusion Classification acknowledgment/remunerate distinguishing proof Chapter 15 - Performance Evaluation and Benchmarking with Data Envelopment Analysis 4

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Performance Evaluation Measures Profit Sales volume Contribution edge Customer benefit Market share Methods Negotiated objectives Outputs (ignoring inputs accessible) Chapter 15 - Performance Evaluation and Benchmarking with Data Envelopment Analysis 5

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Data Envelopment Analysis (DEA) Use – productivity assessment for multi-site benefit firms Conditions for utilize: Results uncertainty Results estimation contrariness Service unit similitude Chapter 15 - Performance Evaluation and Benchmarking with Data Envelopment Analysis 6

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Advantages of DEA Output Single number Most great straight blend of yields/contributions to unit contrasted with the yields/contributions of every other unit Advantages Data lessening Objectivity Environmental change reaction Doesn\'t compensate sand-stowing Doesn\'t rebuff predominant entertainers Chapter 15 - Performance Evaluation and Benchmarking with Data Envelopment Analysis 7

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Applications of DEA Non-benefit Education, social insurance, military, open lodging, transportation, office area (superconducting supercollider) For-benefit Banking, retail, mining, horticulture Users ( " Frontier Analyst " programming by Banxia) AMEC Offshore Development, Ameritech, Banca Populare diMilano, Bank of Scotland, Boston Consulting Group, British Gas Transco, CalEnergy Company Inc., Carlson Marketing Group… Chapter 15 - Performance Evaluation and Benchmarking with Data Envelopment Analysis 8

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DEA in Retail Banking Parkan, C. (1994), "Operational Competitiveness Ratings of Production Units," Managerial and Decision Economics , 15, 201‑221. Minister, J. (1994), "How to Discount Environmental Effects in DEA: An Application to Bank Branches," Working Paper, Universidad de Alicante, Alicante, Spain. Move, Y. also, B. Golany (1993), "Elective 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), "Enhancing the Productivity of Service Businesses," Sloan Management Review , 11‑22. Sherman, H. what\'s more, F. Gold (1985), "Bank Branch Operating Efficiency," Journal of Banking and Finance , 9, 297‑315. Sherman, H. what\'s more, G. Ladino (1995), "Overseeing Bank Productivity Using Data Envelopment Analysis (DEA)", Interfaces , 25, 2, 60‑73. 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. Pursue, 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 . what\'s more, B. Howcroft (1994), " Relative productivity 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. also, P. Jaska (1995), " Interpreting Inefficiency Ratings: an Application of Bank Branch Operating Efficiencies, " Managerial and Decision Economics , 16, 7‑14. Part 15 - Performance Evaluation and Benchmarking with Data Envelopment Analysis 9

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Structure of DEA Models Efficiency = Outputs/Inputs Efficiency rating from 0 (most exceedingly bad) to 1 (best) Non-straight programming model Maximize Outputs/Inputs of a particular administration unit s.t. Yields/Inputs  1 for each administration unit No from the earlier weighting of yields or sources of info accepted Chapter 15 - Performance Evaluation and Benchmarking with Data Envelopment Analysis 10

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Structure of DEA Model Linear model constants: yields, inputs factors: yield weights, input weights Analyze units each one in turn Maximize Outputs i x Output weight (particular unit j) s.t. [(outputs i x yield weight)/(inputs i x input weight)  1] ( yields i x yield weight) – (inputs i x input weight)  0 for every other unit Inputs j x input weight = 1 for particular unit j Chapter 15 - Performance Evaluation and Benchmarking with Data Envelopment Analysis 11

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DEA Example Problem Data Chapter 15 - Performance Evaluation and Benchmarking with Data Envelopment Analysis 12

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DEA Example Problem Graph 35 30 25 20 15 10 5 HCU B A C HCU D B D Deposits E 0 5 10 15 20 25 30 Loans Chapter 15 - Performance Evaluation and Benchmarking with Data Envelopment Analysis 13

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DEA Example Problem Data Chapter 15 - Performance Evaluation and Benchmarking with Data Envelopment Analysis 14

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DEA Example Problem Maximize 15 credit weight + 25 store weight s.t. 10 advance weight + 31 store weight – 100 sources of info  0 15 advance weight + 25 store weight – 100 information sources  0 20 credit weight + 30 store weight – 100 data sources  0 23 advance weight + 23 store weight – 100 sources of info  0 30 advance weight + 20 store weight – 100 information sources  0 100 information sources = 1 Chapter 15 - Performance Evaluation and Benchmarking with Data Envelopment Analysis 15

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DEA Example Problem Variables (weights): Loans = 0.00313 Deposits = 0.03125 Breakdown of effectiveness: Loans = 0.00313 x 15 = 0.05 Deposits = 0.03125 x 25 = 0.78 Reference set: An and C Chapter 15 - Performance Evaluation and Benchmarking with Data Envelopment Analysis 16

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

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Model Adaptations Bounding Variable Weights Example: at most 70% of aggregate productivity from advances Maximize 15 advance weight + 25 store weight s.t. 10 advance weight + 31 store weight – 100 sources of info  0 15 credit weight + 25 store weight – 100 data sources  0 20 advance weight + 30 store weight – 100 sources of info  0 23 advance weight + 23 store weight – 100 sources of info  0 30 advance weight + 20 store weight – 100 sources of info  0 100 sources of info = 1 15 advance weight/(15 advance weight + 25 store weight)  0.7 Rearranging terms 4.5 advance weight – 17.5 store weight  0 Chapter 15 - Performance Evaluation and Benchmarking with Data Envelopment Analysis 18

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Linear Programming on Excel 1 st time through: Tools, Solver Target cell (target work) D28 [tab] By evolving cells (factors) C23:J23 [tab] Subject to… Add C23:J23 ≥ 0) K9:K18  0 K21 = 1 Options, Assume Linear Model Solve After 1 st time Copy fitting data down, Tools, Solver, Solve Chapter 15 - Performance Evaluation and Benchmarking with Data Envelopment Analysis 19

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