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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

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Outline

IntroductionAdoption of fuzzy logicDefuzzificationCase exampleConclusion

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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.

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Planned Performance

Standards

ActualPerformance

Monitor and Compare

AdjustmentAnd

Investigation

inputinput

input

Supplier monitoring system

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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.

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Delivery time Standard

More than 4 days early(<4) Fail

4 days early(4) Acceptable

3 days early(3) Acceptable

2 days early(2) Acceptable

1 days early(1) Fine

Promised delivery day Fine

1 days late (-1) Fine

2 days late (-2) Acceptable

More than 2 days late(>-2) Fail

Delivery time measurement

7-2

0.1

0.5

1.0

-4 -1-3 0 1 2 3 4Days

Degree of Membership

Adoption of fuzzy logic

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Quality value(defect percentage) Standard<1 Fine

2 Acceptable

3 Acceptable

4 Acceptable

5 Acceptable>5 Fail

Quality measurement

91 2 3 4

1.0

0.5

5

0.45

Degree of Membership

Quality(percentage)

Adoption of fuzzy logic

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Recordscomplated Weighted method

1 Delivery time of Record1*100%

2 Delivery time of Record1*75%+delivery time of Record2*25%

3 Delivery time of Record1*50%+delivery time of Record2*30%+delivery time of Record3*20%

4 Delivery time of Record1*40%+delivery time of Record2*30%+delivery time of Record3*20%+delivery time of Record4*10%

Weight average for supplier defect rate assessment

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Recordscomplated Weighted method

1 Defect percentage of Record1*100%

2 Defect percentage of Record1*75%+defect percentage of Record2*25%

3 Defect percentage of Record1*50%+defect percentage of Record2*30%+defect percentage of Record3*20%

4 Defect percentage of Record1*40%+defect percentage of Record2*30%+defect percentage of Record3*20%+defect percentage of Record4*10%

Weight average for supplier defect rate assessment

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Delivery time Quality Quantity

Fail Fail Substantial decrease (SD)

Fail Acceptable Considerable decrease (CD)

Fail Fine Some decrease (SMD)

Acceptable Fail Considerable decrease (CD)

Acceptable Acceptable Some decrease (SMD)

Acceptable Fine Little decrease (LD)

Fail Fail Some decrease (SMD)

Fail Acceptable Little decrease (LD)

Fail Fine No change (NC)

Change of next order quantity

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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

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No. Defect rate(%) Delivery time (days)

1 5 -2

2 3 -2

3 2 -1

4 0 -2

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

151 2 3 4

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%

16-2

0.1

0.5

1.0

-4 -1-3 0 1 2 3 4Days

Degree of Membership

-1.8

Adoption of fuzzy logicweighted average of delivery time=

(-2)*0.4+(-2)*0.3+(-1)*0.2+(-2)*0.1= -1.8

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Delivery time Quality Quantity

Fail Fail Substantial decrease (SD)

Fail Acceptable Considerable decrease (CD)

Fail Fine Some decrease (SMD)

Acceptable Fail Considerable decrease (CD)

Acceptable Acceptable Some decrease (SMD)

Acceptable Fine Little decrease (LD)

Fail Fail Some decrease (SMD)

Fail Acceptable Little decrease (LD)

Fail Fine No change (NC)

Change of next order quantity

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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

1.0SD CD SMD LD NC

SDSubstantial Decrease

CDConsiderable Decrease

SMDSome Decrease

LDLittle Decrease

NCNo Change

Degree of Membership

Order Quantity Change Rate

COG=-0.225

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Next order quantity

Next order quantity = quantity +(average quantity)(order quantity change rate)

Average order quantity = 10000 unitsQuantity 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

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ConclusionThe 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.

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