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Paper 1: Integrated Bank Performance Assessment (Yang)

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Paper 1: Integrated Bank Performance Assessment (Yang)

  1. 1. Hybrid Minimax Reference Point – DEA Approach for Integrated Bank Performance Assessment and Planning Jian-Bo Yang Professor of Decision and System Sciences Director of Decision and Cognitive Sciences Research Centre Manchester Business School The University of Manchester, UK Tel: +44 161 200 3427 Fax: +44 161 200 3505 Email: [email_address] Website: www.personal.mbs.ac.uk/jbyang
  2. 2. Outline of the Presentation <ul><li>Performance assessment and planning </li></ul><ul><li>Background for bank branch performance </li></ul><ul><li>Integrated minimax reference point – DEA </li></ul><ul><li>Results and analysis </li></ul><ul><li>Graphical interpretation and analysis </li></ul><ul><li>Technical efficiency score </li></ul><ul><li>Concluding remarks </li></ul>
  3. 3. Integrated Performance Assessment and Planning <ul><li>Performance assessment </li></ul><ul><ul><li>Performance measurement – management control </li></ul></ul><ul><ul><li>Fairness, objectivity and equity </li></ul></ul><ul><ul><li>Data envelopment analysis (DEA) </li></ul></ul><ul><li>Performance planning </li></ul><ul><ul><li>Performance target setting – management planning </li></ul></ul><ul><ul><li>decision makers’ preferences considered </li></ul></ul><ul><ul><li>Multiple objective linear programming </li></ul></ul>
  4. 4. Background for Bank Branch Performance Assessment <ul><li>A major international bank in the UK </li></ul><ul><ul><li>Headquarter in London – Performance Director </li></ul></ul><ul><ul><li>Hundreds of branches overall the country </li></ul></ul><ul><ul><li>14 branches involved in Greater Manchester area </li></ul></ul><ul><li>Performance planning and control </li></ul><ul><ul><li>Improve the bank’s business performances </li></ul></ul><ul><ul><li>Take into account Director’s & branch managers’ preferences </li></ul></ul><ul><ul><li>Data recorded in the bank’s performance database </li></ul></ul>
  5. 5. Performance Measure <ul><li>Two outputs selected by the bank </li></ul><ul><ul><li>Customer service – Number of customers who rate the branch service as being satisfied </li></ul></ul><ul><ul><li>Commercial income – Generated by relationship managers from selling mortgages, bank loans, insurance, and investment products </li></ul></ul><ul><li>Five outputs selected by the bank </li></ul><ul><ul><li>Business reviews – Number of reviews for business clients completed and their effectiveness ratio </li></ul></ul><ul><ul><li>Contacts – Number of customer contact promises generated and the percentage that is already fulfilled </li></ul></ul><ul><ul><li>Registrations – Number of Internet and telephone banking accounts opened and activated for the customers </li></ul></ul><ul><ul><li>Key performance indicators – Saving and lending balances of the customer accounts </li></ul></ul><ul><ul><li>Future value added – Amount of leads being converted into sales in the ensuing months: commercial start-ups, account switches, etc </li></ul></ul>
  6. 6. Performance Data of the Bank
  7. 7. Management Control and Planning Output-Oriented CCR Dual Model
  8. 8. Efficiency Score by CCR Model
  9. 9. Management Control and Planning Output-Oriented CCR Dual Model B C A D E O Output 2 Output 1 E 1 Efficiency Score for the observed DMU: ( Decision Making Unit ) C 1 A 1 MPS
  10. 10. Management Control and Planning by The Reference Point Models
  11. 11. Management Control and Planning Equivalence of CCR Dual and Super-Ideal Point Models Special weight Super-ideal point Ideal value
  12. 12. Management Control and Planning Illustration of the Super-Ideal Point Model B A D E O Output 2 Output 1 C E 1
  13. 13. Efficiency Score Generated by Super-Ideal Point Model
  14. 14. Management Control and Planning Ideal Point Model to Get MPS
  15. 15. Payoff Table for Maximum Outputs of all Branches
  16. 16. Management Control and Planning Ideal Point Model for Target Setting ( MPS ) E 1 B A D E O Output 2 Output 1 C Gradient projection Normal vector Utility gradient
  17. 17. Management Control and Planning Interactive Tradeoff Analysis Process Normal vector Gradient projection (proportional to utility gradient) Marginal rate of substitution Optimal indifference tradeoff Weight update equation Indifference tradeoff (Required from DM)
  18. 18. Indifference Trade-off Direction for University Branch Trade-off information provided by decision maker Trade-off direction given by the projection of indifference trade-offs i.e.: commercial income should be improved at the expense of customer service to improve the utility of the University branch manager
  19. 19. Indifference Trade-off Step Size for University Branch Trade-off step size chosen by decision maker, assuming that minimum lower bound for customer service is 96.00 0 = 0.62
  20. 20. New Efficient Solution – Target for University Branch 0.117, 0, 0, 0.296, 0, 0, 0, 0.429, 0, 0, 0, 0.163, 0, 0 0.775, 0.757 Proportion of efficient DMUs as benchmark for University Branch New target outputs for University Branch New improvement basis – Normal vector
  21. 21. DEA and MPS Target Values for University Branch
  22. 22. DEA and MPS Target Values for All Branches in Greater Manchester
  23. 23. Group MPS Target Values for All Branches in Greater Manchester
  24. 24. Identify Local MPS based on Group MPS Shortest Distance Model for LMPS
  25. 25. Graphical Interpretation of the Shortest Distance Model for LMPS E B A D O Output 2 Output 1 C E 1 A 1 B 1 Group MPS generated using Group MCDA techniques
  26. 26. Management Control and Planning Weights and LMPS of University branch
  27. 27. Management Control and Planning Generating LMPS for all DMU
  28. 28. Management Control and Planning Target Setting and Interpretation University Branch – Target Setting and Resource Allocation
  29. 29. Management Control and Planning Graphic Explanation of Data Envelop 0 20 40 60 80 100 120 140 160 180 0 20 40 60 80 100 120 1 23 26 25 7 6 24 19 22, 20, 21 15 11 10 12 16 4 9 14 17 18 13 8 5 3 2 m.c. minimax contour ( m.c. )
  30. 30. Management Control and Planning Graphic Explanation of Target Setting = 0.877 Technical efficiency score 130 135 140 145 150 155 160 70 75 80 85 90 95 100 105 25 24 19 22 20 21 15 7 7’ m.c.
  31. 31. Management Control and Planning Technical Efficiency Score 0 0.892 0.892 14 0.339 0.618 0.957 13 0.035 0.957 0.992 11 0 0.956 0.956 10 0.018 0.877 0.895 7 0.054 0.688 0.742 5 0.179 0.636 0.815 2 Over estimation Inefficient DMU
  32. 32. Concluding Remarks <ul><li>Performance assessment and planning </li></ul><ul><li>Background for bank branch performance </li></ul><ul><li>Integrated minimax reference point – DEA </li></ul><ul><li>Results and analysis </li></ul><ul><li>Graphical interpretation and analysis </li></ul><ul><li>Technical efficiency score </li></ul>

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