reliability assessment on member of supply chain based on ... 15031j12.pdf · metrics are essential...

Abstract— The crude palm oil supply chain is important for

the other supply chains, which is the upstream of

pharmaceutical industry, food industry, and energy industry.

This research is focused on the analysis reliability

Performance by failure model. Performance measures and

metrics are essential for effectiveness and efficiency and

increase the organization competitiveness. This paper applied

the Supply Chain Operation Reference Model (SCOR Model)

for calculating the failure likelihood indexes (FLIs) by

Analytical Hierarchy Process (AHP) and then calculate the

failure index by Fault Tree Analysis (FTA) based on SCOR

performance. Finally, the Failure Index will be converted to

the Reliability Index. The calculation showed 42 values of

failure likelihood indexes (SCOR performance level 2), 10

values of failure indexes (SCOR performance level 1), 5

values of failure indexes (performance attribute) and 2 values

of failure indexes (external failure and internal failure). The

maximum failure indexes of SCOR performance level1 was

Upside Adaptability and maximum failure indexes of SCOR

performance attribute was agility. That are low reliability

index.

Keywords— reliability Performance, Supply Chain

Operation Reference (SCOR), Fault tree Analysis (FTA),

Analytical Hierarchy Process (AHP)

I. INTRODUCTION

HE supply chain performance was become one of the most

frequently discussed topics in business literature.

Performance measurement describes the feedback or

information on activities with respect to meeting customer

expectations. Performance Measurement can be defined as the

process of quantifying the efficiency and effectiveness of

action [1]. Effectiveness refers to the extent to which customer

requirements are met, while efficiency is a measure of how

economically the firm’s resources are utilized when providing

Kittichai Athikulrat , Department of Industrial Engineering. Faculty of

Engineering / King’s Mongkut’s Unversith of Technology North Bangkok, Thailand. Email id: [email protected].

Vichai Rungreanganun,. Department of Industrial Engineering. Faculty of

Engineering / King’s Mongkut’s Unversith of Technology North Bangkok, Thailand. Email id: [email protected].

Sompoap Talabgaew, Department of Teacher Training in Mechanical

Engineer / King Mongkut’s University of Technology North Bangkok, Bangkok Thailand. Email id : [email protected].

a given level of customer satisfaction. The core purpose of a

performance measurement system (PMS) is for quantifying

the efficiency and/or effectiveness of action. Performance

assessment has become a key research area in academia and in

industry. However monitoring and improvement are achieved

when the operations are in process or finished so the

improvements are delayed. Supply chain reliability system

(PMS) is for quantifying the efficiency and/or effectiveness of

action. Performance assessment has become a key research

area in academia and in industry. However monitoring and

improvement were achieved when the operations are in

process or finished so the improvement are delay. Supply

chain reliability that is the performance measurement can be

diagnose before operating start. In order to obtain high

efficiency and effectiveness in supply chain management,

supply chain must have a high reliability as guarantee.

Reliability has now become an important performance

measure for evaluation supply chain Wang Jian and Guohua

Chen [2] and Jin liu [3]. Supply Chain Reliability (SCR) can

be defined as the probability of the chain meeting mission

requirements to provide the required supplies to the critical

transfer points within the system Thomas MU. [4].

To achieve supply chain reliability a number of

researchers have devoted their efforts toward developing

models to describe the elements and activities of a supply

chain. There are some models such as multi stage-supply

chain model [5], economic model[6] deterministic model[7],

simulation model [8] and stochastic model [9]. Varshney et al.

[10] proposed a more general link-capacity model to have

multiple states with different capacities in each state. Jin Liu

[11] presented a relatively new graphical construct called

meta-graph. Ni Wang and Jye-Chyi Lu [12] focused on

developing reliability models for large-size logistics systems

to approximate store location & demand data. Valery .L and

Vladislav V [13] proposed the evaluation of the reliability of

supply chain by failure model.

This article defines the reliability of the supply chain’s

member as the reliability index of enterprise. The impact of its

change on the overall reliability of the supply chain. It

examines the node enterprise in the supply chain, using the

crude palm oil enterprises as the background. The palm oil

industry is vital to many industries. It is an upstream of others

supply chain such as the pharmaceutical industry, food

industry and energy industry in Thailand. The crude palm oil

is a member of the palm oil supply chain. This paper studies

Reliability Assessment on Member of Supply

Chain Based on SCOR Performance and Fault

Tree Analysis

Kittichai Athikulrat, Vichai Rungreanganun, and Sompoap Talabgaew

T

International Journal of Computer Science and Electronics Engineering (IJCSEE) Volume 3, Issue 2 (2015) ISSN 2320–4028 (Online)

107

and assess the reliability index of the crude palm oil

enterprises in Chum Porn province in Thailand.

.

II. RESEARCH METHODOLOGY

Fig.1 Research Methodology

This research methodology was presented as Fig.1 It is

summarized as follows:

Step 1 .serepee f n etceleS : Experts who best understand

the target system are selected. Each expert is required to have

professional experience or knowledge in a relevant field

[14],[15]. In this study, the researcher was interviews the

association of crude palm oil mill to determine the criteria of

selection the experts and apply the Analysis Hierarchy Process

(AHP) to find the expert of crude palm oil mill in Chum Porn

province. The Analysis Hierarchy Process (AHP) developed

by Saaty [16] is a multi-criteria decision method for complex

problems, in which both qualitative and quantitative aspects

are considered. There are many areas where AHP is applied,

such as economics, flexible manufacturing systems and

subjective probability estimation [17]. It has created a

voluminous work of literature by various researchers;

additionally, its mechanism is known to many experts. A

detailed description of this method is given in [18]. AHP is

based on a user’s experience and judgment and its results are

objective [16] and realistic [19]. In general, people are better

at making relative comparisons as opposed to absolute

judgments [20]; the creation of the relative comparisons is the

basic principle behind AHP. An intrinsic and useful by

product of AHP is an index of consistency, which provides

information on the severity of the numerical and transitive

consistency violations [18]. If consistency ratio (CR)

suggested by Saaty [16] is above 0.1, the person making the

judgment should seek additional information, re-examine the

data used in constructing the scale, and then make a new

judgment. However, it is not absolute standard and can be

changed according to circumstances. There have been a

number of studies on the quantification of subjective judgment

or the elicitation of subjective probabilities using AHP [16] –

[24].

Step 2. Weight the relative importance on SCOR by AHP :

This step, researcher set up the SCOR Performance to

Hierarchy diagram (see Fig. 3.) and then, the expert provided

important of the all pairs for each level except SCOR

performance level2. They judged the importance scale based

on Table I. These results are also expressed with the matrix.

The consistency is also checked.

Step 3. Rate the relative likelihood of a pair SCOR

Performance level 2 on each level 1 : This step, The expert

judged the rating scale based on Table II. The executed this

rating for all pairs of SCOR Performance level2 on each

level1. The results are expressed with the matrix from AHP.

Whenever an expert finishes a rating, consistency is checked

through AHP. If the consistency is unsatisfactory, the expert

must execute the rating again.

TABLE I SCALE OF RELATIVE IMPORTANCE USED IN THE PAIR WISE

COMPARISONS OF AHP

Comparative Judgment

Scale of relative

importance

and are equally important

is moderately more important

is strongly more important than

is very strongly more important than

is extremely more important than

Intermediate values between two adjacent

judgment

1

3

5

7

9

2,4,6,8

The most general objective

of the decision problem

Decision attribute1 Decision attribute2 Decision attribute n

Decision attribute1 Decision attribute2 Decision attribute n

Goal

Attributes

Alternative

Fig.2 An example form of decision elements in AHP

Step 4.Calculate failure Likelihood Indexes (FLIs): The

FLIs of each SCOR performance level 2 are calculated via

AHP using the result from the rating and weighting step.

1. Selection of Experts.

2. Weight the relative importance on SCOR by AHP

3. Rate the relative likelihood on SCOR Level2

4. Calculate failure Likelihood Indexes.

5. Calculate failure Index on SCOR Performance by Fault

Tree Analysis.

6. Convert failure Index to Reliability Index.


108

TABLE II

SCALE OF RELATIVE LIKELIHOOD USED IN THE PAIRWISE COMPARISONS OF

SCOR PERFORMANCE LEVEL 2

Comparative Judgment

Scale of relative

likelihood

and are equally likely

is moderately more likely than

is strongly more likely than

is very strongly likely than

is extremely more likely than

Intermediate values between two adjacent

judgments

1

3

5

7

9

2,4,6,8

SCOR Performance

External Internal

Reliability Responsiveness Agility Cost Asset Management Eff

R1.1

R2.1 R2.2

Level 1

Level 2

Attribute 2

Attribute 1

Fig. 3 SCOR Performance Hierarchy

Step 5.Calculate failure Index on SCOR performance by

Fault Tree Analysis : This step applied the FTA for calculated

the failure Index, The model of SCOR Performance will be

structured by the method of Fault Tree Analysis (see Fig. 4)

The failure Index evaluated using the FLIs by FTA in Eq. (1)

and (2)

External Internal

Relia

bility

Resp

on

ses

Agility

Co

st

Asse

ts

Perfe

ct O

rder

Fu

llfillmen

t

Ord

er

Fu

llfillmen

t

Cy

cle tim

e

Upsid

e

Fle

xib

ility

Upsid

e

Adap

tability

Dow

nsid

e

Adap

tability

To

tal Co

st to

Serv

e

Cash

to C

ash

cy

cle

time

Retu

rn o

n fix

asse

ts

Retu

rn o

n

work

ing C

apita

l

SCOR

Performance

level2

level2

level2

P(x) P(x) P(x)

Fig. 4 The structure of SCOR performance by FTA

The output fault events of lower and intermediate logic

gates such as OR and AND. The occurrence probability of the

OR gate output fault event is given by Dhillon [25]

( ) ∏ * ( )+ (1)

Where:

( ) is the OR gate output fault event, , occurrence

probability;

n is the total number of input fault event;

( ) is the probability of occurrence of OR gate input

fault event ; for i = 1,2,3,….,n

Step 6. Convert SCOR Performance failure Index to SCOR

Performance Reliability

Index : This step is calculate the Reliability index for node

enterprise by (2)

(2)

III. RESULT

The paper takes the crude palm oil industry as background

for research reliability of the supply chain enterprises

A. Result from selection of Experts.

From the interview result, the researcher analysis the

criteria for selecting the experts and applies the important,

Using the AHP to find the expert of crude palm oil mill in

Chum Porn province and then check the consistency. We get

one crude palm oil mill manager.

B. Results from weight the relative importance on

SCOR performance

This time the researcher interviewed the expert individually,

to give the importance by using AHP in Expert Choice

Program, by comparing each pair on each level of SCOR

performance hierarchy. It would stop when the Consistency

Ratio (CR) was equal to 0.1 or less, in each group pair, as in

Fig. 5 and 6

Fig. 5 Metric table importance Level1 on agility attribute of the crude

palm oil mill

Fig. 6 The weight of Level1 on Agility attribute of the crude palm oil

mill


109

C. Results from rate likelihood of a pair SCOR

Performance level2 on each level1

This time the researcher interviewed the expert individually, to

give the likelihood by using AHP in Expert Choice Program,

by comparing each pair SCOR performance level2 on each

level1. It would stop when the Consistency Ratio (CR) was

equal to 0.1 or less, in each group pair, as in Fig 7 and 8

Fig.7 Metric table Failure likelihood Level2 on Level 1 of crude palm

oil mill

Fig. 8 The Failure likelihood on Level1 of crude palm oil mill

D. Calculated Failure likelihood indexes (FLIs)

The researcher, Used AHP in Expert Choice Program to

calculate the Failure likelihood indexes (FLIs), From the AHP

result, FLIs are 42 values (see Table. III), that are initial

values for calculate the failure index on SCOR performance

by using FTA.

E. Calculate Failure Index on SCOR Performance

by Fault Tree Analysis.

From the Table III, it could calculated the failure Index on

SCOR Performance by FTA is 0.653 ,Therefore reliability

index is 0.347 , Indexes failure in each Level on SCOR

performance are show in table IV to VI

TABLE III

FAILURE LIKELIHOOD INDEXES ON SCOR PERFORMANCE LEVEL2

NO. Scor performance level2 FLIs

1 % Order Delivered In Full 0.03

2 Delivery Performance to Customer Commit Date 0.065

3 Documentation Accuracy 0.015

4 Perfect Condition 0.009

5 Source Cycle Time 0.065

6 Make Cycle Time 0.012

7 Deliver Cycle Time 0.034

8 Delivery Retail Cycle Time 0.009

9 Upside Source Flexibility 0.012

10 Upside Make Flexibility 0.004

11 Upside Deliver Flexibility 0.02

12 Upside Source Return Flexibility 0.005

13 Upside Delivery Return Flexibility 0.004

14 Upside Supply Chain Adaptability 0.068

15 Upside Make Adaptability 0.017

16 Upside Deliver Adaptability 0.044

17 Upside Source Return Adaptability 0.026

18 Upside Deliver Return Adaptability 0.08

19 Downside Source Adaptability 0.063

20 Downside Make Adaptability 0.018

21 Downside Deliver Adaptablity 0.015

22 VAR Source 0.027

23 VAR Plan 0.006

24 VAR Make 0.011

25 VAR Deliver 0.032

26 VAR Return 0.021

27 Planning Cost 0.004

28 Sourcing Cost 0.005

29 Material Landed Cost 0.016

30 Production Cost 0.008

31 Order Management Cost 0.002

32 Fulfillment Cost 0.002

33 Return Cost 0.008

34 Cost of Goods Sold 0.018

35 Day Sales Outstanding 0.04

36 Inventory Days of Supply 0.04

37 Days Payable Outstanding 0.04

38 Supply Chain Fixed Assets 0.035

39 Supply Chain Revenue 0.035

40 Accounts Payable 0.015

41 Acconts Receivable 0.006

42 Inventory 0.01

TABLE IV

FAILURE INDEXES ON SCOR PERFORMANCE LEVEL1

NO. SCOR Performance Level1 Failure Indexes

1 Perfect Order Fulfillment 0.1147

2 Order Fulfillment 0.1157

3 Upside Flexibility 0.0395

4 Upside Adaptability 0.2152

5 Downside Adaptability 0.0937

6 Value at Risk 0.0881

7 Total Cost to Serve 0.0614

8 Cash-to-Cash Cycle Time 0.1153

9 Return on Fixed Assets 0.0688

10 Return on Working Capital 0.0307


110

TABLE V

FAILURE INDEXES ON SCOR PERFORMANCE ATTRIBUTE II

NO. SCOR Performance Attribute II Failure Index

1 Reliability 0.1147

2 Responsiveness 0.1157

3 Agility 0.3125

4 Cost 0.0614

5 Assets Management Eff. 0.2014

TABLE VI

FAILURE INDEX OF SCOR PERFORMANCE ATTRIBUTE I

NO. SCOR Performance Attribute I Failure Index

1 External Facing 0.4617

2 Internal Facing 0.3547

IV. DISCUSSION

Table IV to VI indicated the failure indexes of SCOR

performance Level1 and performance attribute. The maximum

value of failure index of SCOR performance metric level1 in

Table IV is 0.2152 (Upside adaptability). This value affects

the next level on SCOR Performance hierarchy. The

maximum value of failure index of the performance attribute

II in Table V. is 0.3125 (Agility attribute). The maximum

value of failure index on Performance attribute I in Table VI.

is 0.4617 (External Facing). Therefore, improvement of the

SCOR Performance Level1 will affect the failure index to the

next level and affect the failure index to the top level.

V. CONCLUSION

In this study it was found that the Failure likelihood

Indexes (FLIs) from the interview of the expert who was

involved with the crude palm oil industry which was presented

in the form of giving importance weight in each level of

supply chain performance except level 2, which level2 giving

the failure likelihood, could lead to calculating the failure

index and then using the FTA to calculate the failure index on

SCOR performance constructed. Failure indexes should be

considered in each level of the SCOR performance to the top

level. Therefore this index can help to indicate any

performance improvement. The reliability index of enterprise

was converted from the failure index. At the same time the

reliability of member enterprises of the supply chain impact on

the overall supply chain reliability (SCR). Further study, based

on the system reliability engineering cloud extend the supply

chain reliability.

ACKNOWLEDGMENT

Thank you to those experts who offered their comments,

tie and opinions. I appreciated their help. My thanks also to

those who participated in the comparison interviews in

Analytic Hierarchy Process

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