Proceedings of 13th Asian Business Research Conference
26 - 27 December, 2015, BIAM Foundation, Dhaka, Bangladesh,
ISBN: 978-1-922069-93-1
Applying the Modified Failure Mode and Effects Analysis
(MFMEA) Approach for Supplier Selection in Risk Environment
Sayed Rezwanul Islam1*, Masudur Rahman2 and Sanwar-Ul-Quadir3
Selecting an appropriate supplier is essential for the success of a company.
Supplier selection is a multi-criteria problem which involves both qualitative
and quantitative factors. It is a complex, multi-person and group decision
making process that can be improved by systematic and logical approaches
to assess priorities based on the inputs of several people from different
functional areas within the company. In this paper we have shown how fuzzy
analytic hierarchy process (F-AHP) can be effectively used with failure mode
and effects analysis (FMEA) approach for selecting the best supplier in risk’s
environment. F-AHP is used to determine the relative weight of every
criterion and FMEA is used to calculate the risk of every criterion and subcriterion related to the supplier. Therefore, this research proposes a modified
FMEA approach to select the best supplier from supply chain risk’s
environment.
Field of Research: Management
1. Introduction
Supplier selection can be defined as the process of finding the right suppliers. Manufacturers
spend more than 60% of its total sales on purchased items [Krajewsld & Ritzman,
1996].Therefore, selecting the right supplier significantly reduces purchasing costs,
improves competitiveness in the market and enhances end user satisfaction [Onut et.al.,
2009]. Since supplier selection process mainly involves the evaluation of different criteria
and various supplier attributes, so it can be considered as a multiple criteria decision making
(MCDM) problem [Liao et.al.2011]. Dickson, the pioneer of supplier selection problem,
identified 23 different criteria for selecting suppliers, including quality, delivery, performance
history, warranties, price, technical capability, and financial position. With a thorough
literature survey, [Weber, et. al.1991] reviewed 74 different articles by classifying into three
categories; linear weighting methods, mathematical programming models, and statistical
approaches. [Boran et.al.2009], identified four stages for supplier selection including;
definition of the problem, formulation of criteria, qualification, and final selection respectively.
The F-AHP approach is capable of handling the inherent subjectivity and ambiguity
associated with the mapping of one's perception to an exact number. Hence, [Buckley, 1985]
developed a fuzzy AHP model and following Buckley's work, various developments of Fuzzy
AHP methods and applications have been carried out [Chen et.al.,2006]. To the best of the
*Corresponding Author: Sayed Rezwanul Islam, Department of Industrial & Production Engineering, Rajshahi
University of Engineering & Technology, Rajshahi-6204, Bangladesh. Email: syedrezwanulislam@gmail.com
2 Md. Masudur Rahman, Department of Industrial & Production Engineering, Rajshahi University of
Engineering & Technology, Rajshahi-6204, Bangladesh. Email: mrahmanruet@gmail.com
3 Md. Sanwar-Ul-Quadir, Department of Industrial & Production Engineering, Shahjalal University of Science
& Technology, Sylhet-3441, Bangladesh. Email: sanwarquadir@gmail.com
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Proceedings of 13th Asian Business Research Conference
26 - 27 December, 2015, BIAM Foundation, Dhaka, Bangladesh,
ISBN: 978-1-922069-93-1
Author’s knowledge, no AHP, F-AHP and other multi-criteria decision making process is not
so effective for supplier selection in risk environment. In risk environment, risk associated
with different criteria should also be calculated. In this research work, we proposed a
modified Failure Mode and Effects Analysis in which weights of different criteria are
calculated by using F-AHP and risks associated with each criteria are calculated by FMEA.
In this study, we have considered six criteria: cost, quality, deliverability, organizational
behaviour, environmental assessment and twenty six sub-criteria. The main objectives of
this study are to identify the main criterion and sub criterion that affects supplier selection
decision and to evaluate the relative weights and risks associated with decision criteria.
This study arranged as follows- Part 2 presents the literature review to identify the research
scope on supplier selection of risk environment. Part 3 explains the methodology and the
solution procedure. In this part, the F-AHP and FMEA methods are explained in detail which
is utilized to solve the supplier selection of risk environment. Part 4 presents summarized
results and the part 5 presents the conclusions of the study.
2. Literature Review
Generally, when a supplier selection decision needs to be made, the enterprise
establishes a set of evaluation criteria that can be used to compare potential sources.
In supplier selection decisions, two issues are very significant, first one is what criteria should
be used, and the next one is what methods can be used to compare suppliers. Many studies
have been performed for supplier selection by using different criteria starting from the
[Dickson, 1996] 23 criteria. [Cheraghi et. al., 2004] Updated Dickson’s criteria with 13 more
criteria. As the pace of market globalization quickens, the number of criteria to be considered
will increase [Arikan, 2013]. Quality, price, and delivery performances are suggested as the
most important selection criteria for supplier selection [Liao & Kao, 2011]. Recently, it is
observed that, various multi criteria decision making methods are implemented, which can
be grouped into three broad categories [Wang, et. al., 2011]. These are:
1) Value Measurement Models: AHP and multi attribute utility theory (MAUT) are the best
known method in this group.
2) Goal, Aspiration, and Reference Models: Goal programming and TOPSIS are the most
important methods that belong to the group.
3) Outranking Methods: ELECTRE and PROMETHEE are two main families of methods in
this group.
The impreciseness of human judgments can be handled through the fuzzy sets theory
developed by Zadeh [Zadeh, 1965]. Fuzzy AHP method [Cheng, 1997], [Cheng, et. al.,
1999], [Ruoning & Xiaoyan, 1992] systematically solves the selection problem that uses the
concepts of fuzzy set theory and hierarchical structure analysis. Applications of F-AHP in
various fields including; weapon selection [Dağdeviren & Yüksel, 2009], energy alternatives
selection [Kahraman & Kaya, 2010], job selection [Kilic & Cevikcan, 2011] and performance
evaluation systems [Kilic, 2011], [Kilic & Cevikcan, 2012]. In 2010, a Fuzzy AHP method is
used for supplier selection in electronic market places [Chamodrakas, et.al.2010]. In 2011,
Fuzzy AHP approach is used for supplier selection in a washing machine company [Kilincci
& Onal, 2011]. In 2012, a combination of fuzzy AHP and fuzzy objective linear programming
is used to select the best supplier to develop a low carbon supply chain [Shaw, et.al.2012].
In 2013, an interactive solution approach is proposed for multiple objective supplier selection
problems with Fuzzy AHP [Arikan, 2013]. In risk environment, FMEA is a very popular
method of measuring preventing risks [Liu, et. al., 2013]. The traditional FMEA evaluated
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Proceedings of 13th Asian Business Research Conference
26 - 27 December, 2015, BIAM Foundation, Dhaka, Bangladesh,
ISBN: 978-1-922069-93-1
risks by only calculated the RPNs. It does not calculate the relative weight of the criteria.
[Hughes et al., 1999] stated that traditional quantitative methods for modeling mechanical
systems are in-appropriate for automated mechanical production. [Arunachalam &
Jegadheesan, 2006] proposed a new FMEA with reliability and cost based approach to
overcome the drawbacks of traditional FMEA. FMEA does not allow one to differentiate
between different risks implications [Sankar & Prabhu, 2001]. [Chen, 2007] modify the
traditional FMEA by using Interpretive Structural Model (ISM) to evaluate the structure of
hierarchy and interdependence of corrective action and then calculate the weight of a
corrective action through the analytic network process (ANP). [Dong, 2007] modify the
traditional FMEA by using fuzzy based utility theory and fuzzy membership functions to
assess severity, occurrence and detection. [Wang et al., 2009] modify the traditional FMEA
by using Fuzzy Risk Priority Numbers (FRPNs) to prioritize failure modes and fuzzy
geometric means to weigh the fuzzy ratings for Occurrence (O), Severity (S) and Detection
(D). [Narayanagounder et al. 2009] addressed the limitations in traditional FMEA and
proposed a new approach to overcome these shortcomings.
In any decision making problem, all the criteria associated with it do not have the same
impact. So it is necessary to calculate the weight of every criteria. Our proposed MFMEA
not only calculate the RPNs but also measure the weights of criteria under uncertainty.
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Proceedings of 13th Asian Business Research Conference
26 - 27 December, 2015, BIAM Foundation, Dhaka, Bangladesh,
ISBN: 978-1-922069-93-1
3. Methodology
Step 1: Determining criteria for supplier selections
Step 2: Conducting the MFMEA method:
2.1. Defining a scoring system for the supplier selection
2.2. Identifying the potential failure modes
2.3. Evaluating the impact of a failure mode and justifying its severity. Following
scales are used for severity:
Seve Rat
Descriptions
rity
ing
Rem
1 Cost: supplier offers cheap raw material
ote
Quality: supplier offer very few defects material
Deliverability: supplier very rarely miss the on time delivery
Productivity: supplier has a strong ability to manufacture raw materials for
the company
organizational behaviour: supplier can handle the case company’s
complaint within very short time
Environmental assessment: very little amount of emission of CO2 , NOx ,
SO2 and dust
Low
2 Cost: supplier offers medium-priced raw material
Quality: supplier offer small amount defects material
Deliverability: supplier rarely miss the on time delivery
Productivity: supplier has a normal ability to manufacture raw materials
for the company
organizational behaviour: supplier can handle the case company’s
complaint within moderate time
Environmental assessment: little amount of emission of CO 2 , NOx , SO2
and dust
Mod
3 Cost: supplier offers high-priced raw material
erate
Quality: supplier offer many defects material
Deliverability: supplier miss the on time delivery most of the time
Productivity: supplier has low ability to manufacture raw materials for the
company
organizational behaviour: supplier can handle the case company’s
complaint within long time
Environmental assessment: : moderate amount of emission of CO2 , NOx
, SO2 and dust
High
4 Cost: supplier offers very expensive raw material
Quality: supplier offer abundant defects material
Deliverability: supplier always miss the on time delivery
Productivity: supplier has very low ability to manufacture raw materials for
the company
organizational behaviour: supplier can handle the case company’s
complaint within very long time
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Proceedings of 13th Asian Business Research Conference
26 - 27 December, 2015, BIAM Foundation, Dhaka, Bangladesh,
ISBN: 978-1-922069-93-1
Environmental assessment: : large amount of emission of CO2 , NOx , SO2
and dust
2.4. Determining factors that cause to failures and classifying the probability of
occurrence of the failure. Following scales are used for occurrence:
Oc Ra
Descriptions
cur tin
anc g
e
Re
1 Cost: supplier rarely offers expensive raw materials
mot
Quality: supplier rarely offers defects material
e
Deliverability: supplier very rarely miss the on time delivery
Productivity: supplier rarely cannot manufacture raw materials for the
company
organizational behaviour: supplier rarely fails to handle the case company’s
complaint
Environmental assessment: supplier rarely emits CO2 , NOx , SO2 and dust
in a large scale
Lo
2 Cost: supplier often offers expensive raw materials
w
Quality: supplier sometimes offers defects material
Deliverability: supplier rarely miss the on time delivery
Productivity: supplier cannot sometimes manufacture raw materials for the
company
organizational behaviour: supplier sometimes fails to handle the case
company’s complaint
Environmental assessment: supplier sometimes emits CO2 , NOx , SO2 and
dust in a large scale
Mo 3 Cost: supplier usually offers expensive raw materials
der
Quality: supplier often offers many defects material
ate
Deliverability: supplier miss the on time delivery most of the time
Productivity: supplier often cannot manufacture raw materials for the
company
organizational behaviour: supplier fails to handle the case company’s
complaint most of the time
Environmental assessment: supplier emits CO2 , NOx , SO2 and dust most
of the time in a large scale
Hig 4 Cost: supplier always offers expensive raw materials
h
Quality: supplier offers abundant defects materials
Deliverability: supplier always miss the on time delivery
Productivity: supplier usually cannot manufacture raw materials for the
company
organizational behaviour: supplier always fails to handle the case
company’s complaint
Environmental assessment: supplier always emits CO2 , NOx , SO2 and dust
in a large scale
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Proceedings of 13th Asian Business Research Conference
26 - 27 December, 2015, BIAM Foundation, Dhaka, Bangladesh,
ISBN: 978-1-922069-93-1
2.5. Delineating the detection of each failure and scoring each sub-criteria.
Following scales are used for detection:
Detection
Rating
Probability of detection(%) for all
criteria
Remote
1
76-100
Low
2
51-75
Moderate
3
26-50
High
4
0-25
2.6. Weighting each criterion based on its importance: We have used Fuzzy
analytic hierarchy process to determine the relative weights of each criterion.
2.6.1. Fuzzy Analytic Hierarchy Process
Fuzzy Analytic Hierarchy Process (F-AHP) embeds the fuzzy theory to basic Analytic
Hierarchy Process (AHP). It takes the pair-wise comparisons of different alternatives
with respective to various criteria that are performed through the linguistic variables,
which are represented by triangular numbers and provides a decision support tool
for multi criteria decision problems. F-AHP uses the following steps:
i. Decision Maker compares the criteria or alternatives via linguistic
terms shown in Table
Table: Linguistic terms and the corresponding triangular fuzzy numbers
Saaty Scale
Definition
Fuzzy triangular
Scale
1
3
5
7
9
2
4
6
8
Equal important
Weakly important
Fairly important
Strongly important
Absolutely
important
The intermittent
values between two
adjacent scales
(1,1,1)
(2,3,4)
(4,5,6)
(6,7,8)
(9,9,9)
(1,2,3)
(3,4,5)
(5,6,7)
(7,8,9)
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Proceedings of 13th Asian Business Research Conference
26 - 27 December, 2015, BIAM Foundation, Dhaka, Bangladesh,
ISBN: 978-1-922069-93-1
ii. If there is more than one decision maker, preferences of each decision
maker (dkij ) are averaged and (dij) is calculated as follows:
k
∑k
k=1 dij
dij=
K
iii. According to averaged preferences, pair wise contribution matrices is
updated as follows:
d11 ⋯ d1n
⋱
⋮ ]
A=[ ⋮
dn1 ⋯ dnn
iv. The geometric mean of fuzzy comparison values of each criterion is
calculated as follows:
1/n
ri= (∏nj=1 dij )
i= 1, 2, 3,…….n
Where, ri represents triangular values.
v. The fuzzy weights of each criterion can be found with Eq. (a), by
incorporating next three steps.
vi. Find the vector summation of each ri
vii. Find the (-1) power of summation vector. Replace the fuzzy triangular
number, to make it in an increasing order.
viii. To find the fuzzy weight of criterion i (wi), multiply each ri with this
reverse vector.
wi = ri ×(r1 +r2 + ………+rn )-1
= (lw , mw , uw )…………………………. (1)
i
i
i
Since wi are still fuzzy triangular numbers, it is de-fuzzified by using the following equation:
lw +mw + uwi
Mi = i 3 i
………………………... (2)
Mi is a non- fuzzy number. It is normalized by using following equation:
M
Ni =∑n iM ……………………………….. (3)
i=1
2.7.
i
Calculating the RPN by FMEA
2.7.1. Failure Mode and Effects Analysis
It is a proactive tool, technique and quality method that enables the identification and
prevention of process or product errors before they occur. In FMEA, the Risk Priority Number
(RPN) is defined as follows:
Risk Priority Number (RPN), R= S×O×D ……………………………………..... (4)
Where, “S” is called the Severity, which may be defined as an assessment of how serious
the effect of the potential failure mode is on the customer. “O” is called the probability of
Occurrence, which may be defined as an assessment of the likelihood that a particular cause
will happen and result in the failure mode during the intended life of the product. “D” is called
the Detection, which may be defined as the assessment of the likelihood that the current
controls will detect the cause of the failure mode. This can be defined also as the likelihood
that the defect will reach the customer.
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Proceedings of 13th Asian Business Research Conference
26 - 27 December, 2015, BIAM Foundation, Dhaka, Bangladesh,
ISBN: 978-1-922069-93-1
2.8. Sorting each criterion by its RPNs
Step 3: Categorizing qualified suppliers
4. Summarized Results
Table 3. The comparisons among three suppliers
Criterion
Weight RPNs of Each criterion
Supplier A
Supplier B
Supplier C
Cost
1.159635
1.003666
1.503678
Quality
3.377229
1.954132
3.37432
Deliverability
0.784574
0.701019
0.49403
Productivity
0.30117
0.177869
0.40156
Organizational
0.430184
0.392957
0.42191
behaviour
Environmental
0.513261
0.465397
0.47601
assessment
Average RPNs of six
1.094342
0.782507
1.11192
criteria
From the above table it is clear that Supplier B give a fair result than the other two suppliers.
Only in one case i.e. in deliverability Supplier C give fair result than Supplier A and Supplier
B. Hence, supplier B outperforms in all sections except deliverability. The weighted RPNs
of supplier B is 0.782507 which is far less than supplier A and supplier C. Hence, the best
supplier is Supplier B. The detail calculations of each supplier is shown in Appendix.
5. Conclusions
Supplier selection performed using conventional approach, which does not have the ability
to calculate relative weight and risk associated with each criterion at the same time. Multicriteria decision making (MCDM) approaches only measure the relative weights of every
alternatives and on the basis of the relative weights it take decision. Conventional failure
mode and effects analysis only calculate the risks, it does not measure the relative weights
of criteria. In this study, we propose a modified failure mode and effects analysis (MFMEA)
to select the best supplier in supply chain risk environment. This propose method overcome
the limitation of conventional failure mode and effects analysis (FMEA). To verify the
feasibility of this propose method, we have done case study on Steel Manufacturing
Company. In this research because of confidentiality the suppliers’ exact names are hidden.
None of the voters claim anything negative about the selection process and they were also
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Proceedings of 13th Asian Business Research Conference
26 - 27 December, 2015, BIAM Foundation, Dhaka, Bangladesh,
ISBN: 978-1-922069-93-1
very satisfied about the selection process. Despite the contributions, the study has still some
limitations. The study is based on the information given by the suppliers. And the relative
weight of every criterion may be changed when the case company will different. We consider
six criteria and twenty six sub criteria for this work, but it may not be sufficient for other
company. Other than the F-AHP, the other multi-criteria decision making approach such as
Fuzzy Analytic Network Process (ANP), Fuzzy TOPSIS etc. can also be used with FMEA.
The authors also suggest to use Fault tree analysis, Delphi method or stress testing other
than FMEA and to incorporate with MCDM approaches.
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Proceedings of 13th Asian Business Research Conference
26 - 27 December, 2015, BIAM Foundation, Dhaka, Bangladesh,
ISBN: 978-1-922069-93-1
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Proceedings of 13th Asian Business Research Conference
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Proceedings of 13th Asian Business Research Conference
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Appendix
Table A1: Synthesized table of weighted RPNs of supplier A
supplier A
Criterion
Cost
Quality
Deliverability
Productivity
organizational
behaviour
environmental
assessment
Sub criterion
risk assessment
S O D RPN
product's cost
Inbound
transportation cost
charge of support
service
exchange rate
input quality control
Reliability
Durability
defect rate
product line
compliant rate
production cycle
on time livery
delivery lead time
idle rate
productivity
flexibility
amount of
production
responsiveness
2
3
2
2
1
1
4
6
subcriterion's
weight(Wi)
0.09038
0.029472
weight
RPN (Ri)
3
2
1
6
0.029472
0.176831
2
2
3
2
2
3
4
3
2
4
4
2
1
1
1
1
1
1
8
6
6
8
8
6
0.055557
0.198583
0.106223
0.106223
0.048825
0.051335
0.444455
1.191499
0.637338
0.849784
0.390596
0.308012
4
2
3
2
2
2
3
2
4
3
1
1
1
1
1
8
6
6
8
6
0.018158
0.053747
0.008882
0.032942
0.033012
0.145262
0.322485
0.053291
0.263536
0.198071
3
3
1
9
0.011455
0.103099
4
2
1
8
0.010276
0.082205
claim policy
social responsibility
research &
development
Reputation
Innovation
energy
consumption
green packaging
waste
management
recyclable/reusable
product
Emission
3
4
3
2
3
4
1
1
1
6
12
12
0.010276
0.005791
0.010276
0.061654
0.069487
0.123307
4
3
4
3
2
2
1
1
1
12
6
8
0.002656
0.010276
0.016026
0.031877
0.061654
0.128208
2
3
3
4
1
1
6
12
0.016026
0.016026
0.096156
0.192312
2
3
1
6
0.005413
0.032481
2
2
1
4
0.016026
0.993332
0.064104
6.566052
1.094342
0.361518
0.176831
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Proceedings of 13th Asian Business Research Conference
26 - 27 December, 2015, BIAM Foundation, Dhaka, Bangladesh,
ISBN: 978-1-922069-93-1
Note: “S” means Severity; “O” means Occurrence; and “D” means Detection
Risk Priority Number = S×O×D; Weight RPN (Ri) = Criterions weight (W i) × RPN
Table A2: Synthesized table of weighted RPNs of supplier B
Supplier B
Criterion
Cost
Quality
deliverability
productivity
organizational
behaviour
environmental
assessment
Sub criterion
risk assessment
S
O
D RPN
product's cost
inbound
transportation cost
charge of support
service
exchange rate
input quality control
Reliability
durability
defect rate
product line
compliant rate
production cycle
on time livery
delivery lead time
idle rate
productivity flexibility
amount of
production
responsiveness
3
2
2
3
1
1
6
6
subcriterion's
weight(Wi)
0.09038
0.029472
weight
RPN(Ri)
2
2
1
4
0.029472
0.117887
3
1
2
3
3
2
1
2
3
1
2
3
1
1
1
1
1
1
3
2
6
3
6
6
0.055557
0.198583
0.106223
0.106223
0.048825
0.051335
0.166671
0.397166
0.637338
0.318669
0.292947
0.308012
3
1
2
3
2
1
2
3
2
4
2
4
1
1
1
1
1
1
6
3
4
12
4
4
0.018158
0.053747
0.008882
0.032942
0.033012
0.011455
0.108947
0.161242
0.035527
0.395303
0.132047
0.045822
3
2
1
6
0.010276
0.061654
claim policy
social responsibility
research &
development
reputation
innovation
energy consumption
2
3
3
4
1
3
1
1
1
8
3
9
0.010276
0.005791
0.010276
0.082205
0.017372
0.092481
3
3
4
2
4
1
1
1
1
6
12
4
0.002656
0.010276
0.016026
0.015938
0.123307
0.064104
green packaging
waste management
recyclable/reusable
product
Emission
2
4
3
4
2
3
1
1
1
8
8
9
0.016026
0.016026
0.005413
0.128208
0.128208
0.048721
2
3
1
6
0.016026
0.993332
0.096156
4.695041
0.782507
0.542277
0.176831
13
Proceedings of 13th Asian Business Research Conference
26 - 27 December, 2015, BIAM Foundation, Dhaka, Bangladesh,
ISBN: 978-1-922069-93-1
Note: “S” means Severity; “O” means Occurrence; and “D” means Detection
Risk Priority Number = S×O×D; Weight RPN (Ri) = Criterions weight (W i) × RPN
Table A2: Synthesized table of weighted RPNs of supplier C
supplier C
criterion
cost
quality
deliverability
productivity
organizational
behaviour
environmental
assessment
Sub criterion
risk assessment
S O D RPN
product's cost
inbound
transportation cost
charge of support
service
exchange rate
input quality control
Reliability
Durability
defect rate
product line
compliant rate
production cycle
on time livery
delivery lead time
idle rate
productivity flexibility
amount of
production
responsiveness
3
2
2
3
1
1
6
6
subweight
criterion's RPN(Ri)
weight(Wi)
0.09038
0.542277
0.029472 0.176831
2
2
1
4
0.029472
0.117887
3
2
3
4
2
3
4
2
3
3
2
1
1
1
1
1
1
1
12
4
9
12
4
3
0.055557
0.198583
0.106223
0.106223
0.048825
0.051335
0.666683
0.794333
0.956007
1.274676
0.195298
0.154006
3
2
3
3
4
3
2
1
3
2
2
4
1
1
1
1
1
1
6
2
9
6
8
12
0.018158
0.053747
0.008882
0.032942
0.033012
0.011455
0.108947
0.107495
0.079936
0.197652
0.264095
0.137465
3
2
1
6
0.010276
0.061654
claim policy
social responsibility
research &
development
Reputation
Innovation
energy consumption
3
4
2
3
2
4
1
1
1
9
8
8
0.010276
0.005791
0.010276
0.092481
0.046325
0.082205
2
3
3
3
4
3
1
1
1
6
12
9
0.002656
0.010276
0.016026
0.015938
0.123307
0.144234
green packaging
waste management
recyclable/reusable
product
Emission
3
3
4
2
2
2
1
1
1
6
6
8
0.016026
0.016026
0.005413
0.096156
0.096156
0.043308
2
3
1
6
0.016026
0.096156
14
Proceedings of 13th Asian Business Research Conference
26 - 27 December, 2015, BIAM Foundation, Dhaka, Bangladesh,
ISBN: 978-1-922069-93-1
0.993332
6.671506
1.111918
Note: “S” means Severity; “O” means Occurrence; and “D” means Detection
Risk Priority Number = S×O×D; Weight RPN (Ri) = Criterions weight (W i) × RPN
15