The document discusses using data envelopment analysis (DEA) to benchmark logistics performance in Belgian manufacturing companies. DEA is used to identify efficient companies and output targets by establishing an efficient frontier. Two tests were conducted using different input and output variables to benchmark companies' general performance and logistics performance. The results identified the relative efficiency of companies and potential areas for improvement. Visual information can also be provided to companies to benchmark their performance against peers.

1. Using Data Envelopment Analysis to benchmark logistic performance in Belgian manufacturing companies Sofie Van Volsem, Hendrik Van Landeghem Department of Industrial Management, Ghent University, Belgium (sofie.vanvolsem@ugent.be)

2. Aggregate information Multi-Input Multi-Output DEA Efficient Frontier Efficient Input and Output target Extending classical Output / Input rate Hypothesis: Input/Output relationship is assumed

3. Identification Best-Practice Frontier S. Van Volsem INEFFICIENT

4. Type of Frontier VRS: Variable Return to Scale. Removing the constraint With the constraint: ∑ λ j = 1 With the constraint: ∑ λ j ≤ 1 With the constraint: ∑ λ j ≥ 1 NDRS: Non-Decreasing Return to Scale. CRS: Constant Return to Scale. NIRS: Non-Increasing Return to Scale. VRS: Variable Return to Scale CRS: Constant Return to Scale NIRS: Non-Increasing Return to Scale NDRS: Non-Decreasing Return to Scale CIE39, Troyes, 6-8 July 2009 S. Van Volsem

5. Input-oriented approach Output-oriented approach Two alternative approaches 1,5 D’ D’ 3,5 CIE39, Troyes, July 6, 2009

6. VRS Envelopment Model Input-Oriented Output-Oriented S. Van Volsem CIE39, Troyes, July 6, 2009

7. Slacks S. Van Volsem

8. Undesirable Measures Model Undesirable Outputs may be present: outputs we wish to decrease Internal processes and activities % BP LEAN % BP INV % BP CIM % BP LOGPLAN % Perfect Orders Days of Inventory Flexibility S. Van Volsem CIE39, Troyes, July 6, 2009 Days needed to increase productivity by 20%

9. Undesirable Measures Model Output-Oriented Model with Undesirables Measures: and where Efficient target for DMU 0 CIE39, Troyes, July 6, 2009

10. DEA Models in Spreadsheets Joe Zhu “ Quantitative Models for Performance Evaluation and Benchmarking” 2003 Excel Spreadsheets Solver Visual Basic + S. Van Volsem CIE39, Troyes, July 6, 2009

11. Benchmarking applied on logistics Case 80 Belgium companies Best Practices Data General Best Practice rate Human Resources, Assets, Design, quality control, Inventory,… Best Practices related with logistics Logistic plan (LOGPLAN) Inventory (INV) Lean (LEAN) Monitorizaton (CIM) S. Van Volsem CIE39, Troyes, July 6, 2009

12. General BP adoption

13. Internal processes and activities % BP LEAN % BP INV % BP CIM % BP LOGPLAN ROA Cash Cycle Time Added Value TEST 1: Multi-input / Multi-output – general output % BP LEAN % BP INV % BP CIM % BP LOGPLAN TEST 2: Multi-input / Multi-output – logistics output TESTS % Perfect Orders Days of Inventory Flexibility Internal processes and activities S. Van Volsem CIE39, Troyes, July 6, 2009

14. Internal processes and activities % BP LEAN % BP INV % BP CIM % BP LOGPLAN Results Test 1 : general output Results: ROA Cash Cycle Time Added Value CIE39, Troyes, July 6, 2009 DMU No. Output-Oriented Efficiency 1 1,37078 2 1,19180 4 1,10572 6 1,58600 10 1,09636 11 1,33319 12 1,07831 14 1,18974 19 1,06008

15. % BP LEAN % BP INV % BP CIM % BP LOGPLAN % Perfect Orders Days of Inventory Flexibility Results Test 2 : logistics output Results: Internal processes and activities CIE39, Troyes, July 6, 2009 DMU No. Output-Oriented Efficiency 2 1,00204 5 1,00199 11 1,04211 14 1,18687

16. What can we offer to companies? A benchmarking tool Identification of Efficient Frontier Relative efficiency in the set of companies Visual information For this, we only need aggregate data => low information cost S. Van Volsem

17. Visual information display Efficient company 52,2 % BP CIM 52,9 % BP INV 36,4 % BP LEAN 28,6 % BP LOGPLAN COMPANY ID_DAT_TIM: 720304081839 96 days Inv. Company Position: 1/19 18 days Prod. 94% P. O. Efficient S. Van Volsem

18. Visual information display Non-efficient company 47,8 % BP CIM 64,7 % BP INV 27,3 % BP LEAN 39,3% BP LOGPLAN COMPANY ID_DAT_TIM: 3130501241053 40 days Inv. 17 days 80 days Prod. 3 days 50% P. O. 77,5 % 96% 58% 55% COMPANY POSITION: 19/19 S. Van Volsem

19. Visual information display Relative position in the whole set Possible improvement 23 Companies EFFICIENT YOUR COMPANY 20 / 23 33 % S. Van Volsem

20. Conclusions: Data Envelopment Analysis as a benchmarking tool Identification of efficient companies and output efficient targets No information about how to improve internal performance to reach the efficiency Relationship Input-Output is assumed Quality of a DEA benchmarking does highly depend on the available data Visual information display available S. Van Volsem