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Abstract— People’s consciousness about environment and its

protection has grown largely in last few decades. Government has also framed and imposed laws on industries for protection of environment. Industries have also adopted and incorporated

concepts and practices in their supply chain management for meeting the prescribed standards. Now industries are emphasizing on green criteria for supplier selection. In this work a strategic frame work has been proposed for green supplier selection for decision makers. In the proposed work green criteria has been identified and Step-wise Weight Assessment Ratio Analysis (SWARA) for weight assignment to criteria. Range of Values (ROV) Multi Criteria Decision Making (MCDM) method has been further utilized for assessment of potential suppliers and final rank assignment for selection and contract allocation.

Effectiveness of the proposed methodology has been demonstrated with the help of a numerical example.

Index Terms— Step-wise Weight Assessment Ratio Analysis (SWARA), Range of Values (ROV), MCDM, supplier selection, Green supply chain management (GSCM)

I. INTRODUCTION

Selection of suitable supplier selection has attracted attention of authors as a key strategic decision which

influences the organization’s competitive position in market. Supplier selection process is a complex decision to make as (i) variety at criteria influence the selection (ii) both qualitative and quantitative criteri a’s are involved (iii)

criteria are compromising in nature. Further, severe competition among organizations, new government laws and regulations, ever increasing customer expectations from supply chain organizations have increased the

criticalness and complexity at supplier selection process. Customer awareness level for environment has increased and on other hand government has tightened the

environmental regulatory mandates. As a result industries are compelled to manage their supply chain operations and make them ecofriendly. Industries are looking for suppliers which are greener i.e. adhering to environmental

protection policies. Industries need to incorporate green criteria in supplier selection process to meet customer expectations and abide by the gov ernment laws.

In literature authors have identified various green criteria for supplier selection in industries. As time duration to incorporate and implement green criteria varies depending

on the vastness of change required both of supplier and industry end. Further all green criteria are not industry

specific. Hence decision maker need to be given authority to choose green criteria specific to industry and also on the basis of urgency and critically of criteria. Thus there is a need of comprehensive methodology selection, incorporate

provision for vagueness in decision maker’s responses and provide an arrangement weight assignment to criteria and have provision for supplier assessment and awarding final ranks to supplier.

In the proposed methodology all above mentioned objectives have been achieved. Fuzzy Kano model has been applied for criteria categorization, which authorizes

decision maker to classify criteria as per industry need. Further SAWARA method has been applied for criteria weight assignment. Finally supplier assessment has been done by ROV method.

The organization of paper is as follows. Next section covers the literature review. Section three covers fuzzy kano model. Section four covers steps of SWARA method and section five covers steps of ROV method. Proposed

methodology has been discussed in section six. Results discussion is under section seven. Conclusions are presented in section eight.

II. LITERATURE REVIEW

Increasing Awareness of environmental issues the amount customers have compelled industries to adopt green supply

chain management. In the same context Supplier selection criteria have evolved some traditional to conventional and from conventional to Green criteria from supplier selection.

In Literature authors have identified various green criteria for supplier selection as tabulated in Table 1. Nielsen et.al, (2015)[1], Galankashi et. al, (2015) [2]Dobos et.al, (2014)[3]has identified air emission, co2 emission and

greenhouse emission as criteria for green supplier selection. Kannan et.al, (2014) [4] has reported Energy consumption cost, Solid waste treatment cost, Chemical

waste treatment cost, Water pollution treatment cost, Use of environmental friendly materials, Pollution reduction capability & control, Environmental management system, Training supplier employees on environmental issues as

criteria’s for green supplier selection. Freeman and Chen (2015),[5] has proposed ISO 14001 certificate as criteria for

An integrated MCDM methodology for green supplier selection in GSCM

1]Naveen Jain, [2] GyanendrakumarShukla, [3]A.R.Singh [1][2] Department of Mechanical Engineering ShriShankracharya Institute of Professional

Management and Technology, Raipur, India [3]Department of Mechanical Engineering, National Institute of Technology, Raipur, India

[1] naveenjainbit@gmail.com , [2] gyanen786@gmail.com, [3] amitrajsingh1@gmail.com

International Journal of Pure and Applied MathematicsVolume 118 No. 20 2018, 461-467ISSN: 1314-3395 (on-line version)url: http://www.ijpam.euSpecial Issue ijpam.eu

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Table 1. GREEN CRITERIA FROM LITERATURE

S.No. Criteria Author

1 i. Air Emission level

ii . CO2 emission

iii . Greenhouse gases emission

Nielsen et.al , (2015)[1], Galankashi et. al, (2015)[2], Dobos et.al , (2014)[3]

2

i. Energy consumption cost ii . Solid waste treatment cost

iii . Chemical waste treatment cost

iv. Water pollution treatment cost v. Use of environmental friendly materials

vi. Pollution reduction capability & control

vii . Environmental management system viii . Training supplier employees on environmental issues

Kannan et.al, (2013)[4]

3 i. ISO 14001 certificate Freeman andChen (2015),[5]

4

i. Environmental planning & policies

ii . Environmental implementation & operations iii . Quality audits

Shaik and Kader (2010),[6]

5 ix. Green image x. Green competencies

xi. Green product Bali et.al, (2013)[7]

6 i. Resource consumption Fallahpour et.al, (2015)[8]

7 i. Remanufacturing/ reuse activity

ii . Hazardous waste management Cao et.al, (2015)[9]

8 i. Recycling Lee and Omar (2014)[10]

9 i. Internal control process Lee et.al, (2008)[11]

10 i. Environmental prerequisite Banaeia et. al , (2016)[12]

11 i. Environment responsibility Kaur et. al , (2016)[13]

green supplier selection. Shaik and Kader (2010),[6] has

reported Environmental planning & policies, Environmental

Implementation& operations, Quality audits as criteria for

green supplier selection. Bali et.al, (2013)[7] has proposed

Green image, Green competencies, Green product as

criteria for green supplier selection. Fallahpour et.al,

(2015)[8] has identified Resource consumption as criteria

for green supplier selection. Cao et.al, (2015)[9] has

reported Remanufacturing/ reuse activity, Hazardous waste

management as criteria for green supplier selection. Lee

and Omar (2014)[10] has identified recycling as criteria for

green supplier selection. Lee et.al, (2008)[11] has proposed

Internal control process as criteria for green supplier

selection. Banaeiaet. al, (2016)[12] has identified

Environmental prerequisite as criteria for green supplier

selection. Kaur et. al, (2016)[13] has reported Environment

responsibility as criteria for green supplier selection.

III. FUZZY KANO METHOD

Sustainability of industry in highly competitive globalized market depends much on customer satisfaction level.

Therefore analysis for customer satisfaction and requirement is of strategic importance aspects for decision maker of industry. Authors have applied philosophy of Kano model for identification and customer requirement. In

thispaper full Kano model has been applied to identify the customer expectation for green supplier selection. While selecting a supplier apparent industry act as a customer

because supplier provides different raw materials semi-finished products etc. to the industry. Hence,industry act as a customer therefore for application of fuzzy

Kanomodel in the proposed methodology is justified. Advantage of fuzzy kano model is (i) respondent is free to work more than one response for each question (ii)

vagueness is respondent response can be captured and real picture of customer expectation can be portrait. In literature also author have applied fuzzy kano model in various field to identify customer expectation and increase

the customer satisfaction level.

IV. SWARA METHOD

Reviewing the literature it can be noted that authors have

identified supplier selection as a MCDM problem involving qualitative and quantitative criteria. After identification of criteria weight assignment presents for Challenge to

decision makers. Author have applied different MCDM methods. In this methodology SWARA method has been applied for weight assignment because of the reason (i) SWARA involves less complex and fast mathematical

calculation in comparison to other MCDM method. (i i) results of SWARA methods are easy to interpret. Steps of SWARA methods are[14], [15][16][17][18]:-

Step1: Arrangement of criteria in descending order of their expected significances. Step2: Calculate Comparative importance of average value, Sj. Starting from second criterion, for each criterion, the

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respondent expresses its relative importance with respect to previous criterion.

Step3: Establish coefficient Kj as K j = 1 if j = 1 (1) K j =

Sj + 1 if j > 1 (2)

Step4: Establish recalculated weight Qj as Qj = 1 if j = 1 (3)

Qj = Kj−1

Kj if j > 1 (4)

Step5: Assign relative weights of the evaluation criteria Wj

Wj = Q j

Q knk=1

5)

V. ROV method

Many MCDM methods have been proposed by authors to help decision makers in assessment of suppliers over selected criteria . This methodology presents a less explored MCDM method i.e. Range of values (ROV) for

ranking of suppliers. This method was proposed by Yakowitz et.al. The procedure of the application of this method is simple and steps involved are[19]:-

Step:1 Establish criteria for evaluating available alternatives.

Step:2 Establish a decision matrix

Dmatrix= Xij =

mnmmn

n

n

n

xxxA

xxxA

xxxA

CCC

21

222212

112111

21

(6)

Where Ai denotes the alternatives i, i=1…..m.

Cj denotes the jth criterion, j=1…….n related to ith alternative. Xij is the numerical value indicating the performance rating of each criterion Ai with respect to each criterion Cj.

Step: 3 Establish a Normalized decision matrix. In this step

performance measure of alternatives are normalized (Xij)

and normalized decision matrix is established.

11 12 1

21 22 2

1 2

n

n

IJ

m m mn

X X X

X X X

X

X X X

(7)

Normalization of performance measure (criteria) depends whether it’s a beneficial criteria or non-beneficial criteria.

For beneficial criteria, maximum values are preferred and for non-beneficial criteria minimum values are preferred.

(i) For beneficial criteria maximum values are preferred and normalization is done by applying linear

transformation [20].

1

1 1

min ( )

max min

m

iij ij

ij m m

i iij ij

x xx

x x

(8)

(ii) For non-beneficial criteria minimum values are preferred and normalization is done by applying linear transformation

1

1 1

max ( )

max min

m

i ij ij

ij m m

i iij ij

x xx

x x

(9)

Step: 4 performed for each alternative. It is achieved by maximization or minimization of a utility function. For

linear additive model, for each alternative best util ity (ui+) and worst utility (ui -) are calculated with help of following equations

Maximize: 1

.n

i ij jj

u x w

(10)

Minimize: 1

.n

i ij jj

u x w

(11)

Where jw (j=1,.....,n) are weights of criteria which satisfy

1

1n

jj

w

and 0

If ui+<ui- then alternative ‘i ’ outperforms alternative ‘i ’ regardless of the actual quantitative weights. If

alternatives are not comparable using this rule then scoring can be attained from the midpoint. To calculate scoring following rule is applied [20]–[22].

2

i i

i

u uu

(12)

Step: 5 In final step of method, on the basis of ui complete ordinal ranking of alternatives are obtained. Alternative having highest ui value is considered as best and awarded

first rank and alternative having lowest ui value is considered as worst choice and is ranked last.

VI. PROPOSED METHODOLOGY

Iron and steel industry of India is a prime importance is it contributes much in nation GDP. Further India being a

developing country depends heavily over iron and steel industries for growth. With economic development Indian government has been long stressing over environmental

issues. Various laws have been imposed over Steel Industries by government for cleaner and green environmental. To avoid the laws and sustain the competition a Steel Plant of Central India has incorporated

green criteria for supplier selection. This case study takes

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the data and response from the decision makers of the same plant. The decision maker team consisted of 6

decision makers having an average experience of 6 and 5 years. The decision makers were representative of department l ike marketing, purchasing, production,

finance. The proposed methodology consists of:- Step 1: identification of green criteria for supplier selection from literature. Step 2: formation of decision maker’s team.

Step 3: criteria classification. Step 4: weight assignment to criteria. Step 5: supplier assessment by RO V method.

Step 6: final ranking of suppliers. VII. RESULT DISCUSSION

Fuzzy Kano model has been applied to green criteria as per Table 1. Fuzzy Kano model questionnaire was prepared and

decision maker’s responses were analyzed. Cr iteria under Must be category were identified as i) Greenhouse gases emission, ii) solid waste management, i ii) ISO: 14001certification, iv) Recycling, v) Environmental

management systems.All finalized criteria has been ranked according to importance by each decision maker and on average value of ranks criteria has been listed rank wise.

Criteria with highest ranking is listed first and least rank criteria has been listed last. Further SWARA method has been applied for weight assignment. After l isting the criteria in decreasing order of their ranks decision makers,

compares the successive criteria and award comparative importance coefficient. Further recalculated rates has been calculated and final weights has been calculated and tabulated in Table 2.There are six suppliers available as

alternatives and they are assessed by DM’s over five criteria’s according to their performances. Linguistic response of DM’s has been converted in to quantitative

response using the scale Very low (1), Medium (5), Very high (9). Green house emission has been considered as

non-beneficial criteria. Normalized matric has been established (Table 3). U

+ and U

-has been calculated as per

equation (12) and results have been tabulated in Table 4.

Finally ranks has been awarded to suppliers. Final Ranks of suppliers are S6> S1 > S2 > S4 > S5 > S3.

VIII. CONCLUSIONS

Increasing awareness among customers have compelled decision maker to include green criteria for supplier selection. Present methodology identifies criteria from

literature and authorizes decision maker to classify them according to their importance. This methodology helps decision maker to select green suppliers effectively with less time. Results of this work is of interest to industrial

personnel, researchers and academicians . Proposed methodology also demonstrates effectiveness of ROVmethod.

TABLE 2. SWARA METHOD CALCULATIONS

S. No Criteria Comparative Importance Of Average Value

Sj

Coefficient Kj= Sj +1

Recalculated Weight

Wj= 𝐗𝐉−𝟏

𝐊𝐉

Weight

Qj = 𝐖𝐉

𝐖𝐉 1 Greenhouse gases emission 1 1.00000 0.27100

2 solid waste management 0.175 1.175 0.85106 0.23064

3 ISO: 14001certification 0.2 1.2 0.70922 0.19220

4 Recycling 0.13 1.13 0.62763 0.17009

5 Environmental management systems.

0.25 1.25 0.50210 0.13607

TABLE 3. NORMALIZED MATRIX FOR ROV METHOD

Criteria C1 C2 C3 C4 C5

Supplier 1 0.7500

0.3333 1.0000 1.0000 0.0000

Supplier 2 0.2500

0.3333 0.6667 0.5000 1.0000

Supplier 3 0.5000

0.0000 0.0000 0.0000 0.6667

Supplier 4 0.0000

0.6667 0.3333 0.5000 0.3333

Supplier 5 0.5000

0.0000 0.0000 1.0000 0.0000

Supplier 6 1.0000

1.0000 1.0000 0.0000 0.0000

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Table 4. Computational details of ROV method

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Suppliers iu

iu

iu

Ranks

Supplier 1 0.4392 0.2033 0.3212 2

Supplier 2 0.4261 0.0678 0.2469 3

Supplier 3 0.0907 0.1355 0.1131 6

Supplier 4 0.3482 0.0000 0.1741 4

Supplier 5 0.1701 0.1355 0.1528 5

Supplier 6 0.4228 0.2710 0.3469 1

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