1. 1 Methodology for monitoring supply chain performance: a fuzzy logic approach Source : Logistics Information Management Volume 15 ． Number 4 ． 2002 ． pp. 271-280 Author : H.C.W Lau, Wan Kai Pang and Christina W.Y. Wong Speaker : 曾偉育 Member : s9114638 曾偉育 s9114624 王仁群

2. 2 Outline  Introduction  Adoption of fuzzy logic  Defuzzification  Case example  Conclusion

3. 3 Introduction  In this paper, fuzzy logic principles is recommended to monitor the supply chain performance by evaluating the ongoing delivery time and product quality,and performing adjustment in order quantity based on the performance.

4. 4 Planned Performance Standards Actual Performance Monitor and Compare Adjustment And Investigation input Supplier monitoring system

5. 5 Cost function for supply chain management  Three kinds of costs for supply chain management.  Total cost = variable cost + fixed costs +unprecedented costs.  The unprecedented costs are difficult to measure using the traditional quantitative approach.

6. 6 Delivery time measurement

7. 7 -2 0.1 0.5 1.0 -4-30 1 234 Days Degree of Membership Adoption of fuzzy logic

8. 8 Quality measurement

9. 9 1234 1.0 0.5 5 0.45 Degree of Membership Quality(percentage) Adoption of fuzzy logic

10. 10 Weight average for supplier defect rate assessment

11. 11 Weight average for supplier defect rate assessment

12. 12 Change of next order quantity

13. 13 Defuzzification  Defuzzification is the process of reducing a fuzzy set to a single point.  There are several methods of performing defuzzification, the gravity method is the most common one.  Next order quantity = quantity +(average quantity) × (order quantity change rate) The output of Defuzzification

14. 14 Recommend the ordering quantity of next order : weighted average for defect rate= 5%*0.4+3%*0.3+2%*0.2+0%*0.1=3.3% weighted average of delivery time= (-2)*0.4+(-2)*0.3+(-1)*0.2+(-2)*0.1= -1.8 Case example

15. 15 1234 1.0 0.5 5 3.3% 0.45 Degree of Membership Quality(percentage) Adoption of fuzzy logic weighted average for defect rate= 5%*0.4+3%*0.3+2%*0.2+0%*0.1=3.3%

16. 16 -2 0.1 0.5 1.0 -4-30 1 234 Days Degree of Membership -1.8 Adoption of fuzzy logic weighted average of delivery time= (-2)*0.4+(-2)*0.3+(-1)*0.2+(-2)*0.1= -1.8

17. 17 Change of next order quantity

18. 18 Fuzzy pattern of order quantity change rate 0 -0.6-0.5-0.4-0.3-0.2-0.1 0.1 0.2 0.3 0.6 0.5 0.8 0.7 0.4 0 0.9 1.0 SDCDSMDLDNC SD  Substantial Decrease CD  Considerable Decrease SMD  Some Decrease LD  Little Decrease NC  No Change Degree of Membership Order Quantity Change Rate COG=-0.225

19. 19 Next order quantity  Next order quantity = quantity +(average quantity)(order quantity change rate) Average order quantity = 10000 units Quantity needed for the coming production is 20000 units  Next order quantity = 20000 + 10000(-0.225) = 17750 units  Company will complete the order by choosing another supplier to order the rest of the 2250 units to complete the order

20. 20 Conclusion  The methodology of using fuzzy logic in monitoring a supply chain partners’s performance and provide a suggestion on the next order quantity.  Developing fuzzy rules require experience from field expert, experimental results and theoretical derivation.

21. 21 thanks