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Abstract: Supplier selection and evaluation has acquired a strategic decision status in supply chain management due to intense global competition, raised customer expectations.

Selection of right suppliers helps industries to achieve higher customer satisfaction thus acquiring greater market share. Supplier selection is considered a Multi Criteria Decision Making

(MCDM) problem which involves many conflicting quantitative and qualitative criteria. In this paper an integrated MCDM

methodology has been proposed for supplier selection in Iron and Steel industry. Analytical Hierarchy Process (AHP) has been applied for weight assignment to criteria and weighted

aggregated sum product assessment (WASPAS) method has been applied for ranking of supplier’s. The proposed methodology has been demonstrated with a help of a case study carried out in Iron and Steel plant located in central zone of India. Results of the present work elicit that the proposed methodology can help decision makers in selecting right suppliers and also reduce the decision making time significantly.

Keywords— Multi-Criteria Decision Making (MCDM), Supplier Selection, Weighted Aggregated Sum Product Assessment

(WASPAS), Analytic Hierarchy Process (AHP).

I. INTRODUCTION

Tough competition in the global market has forced the

industries to focus on their supply chain to gain competitive advantage. In supply chain management purchasing is one of the specific activities which provide industries wi th an opportunity for cost reduction and increasing profits. An

essential task of purchasing department is supplier selection as purchase material accounts for 80% of total production cost [1], [2]. Therefore selection of right

supplier presents a major opportunity to industries for providing good quality products in economical price [3]. Boer et al. (2001) proposed five stages for supplier selection process: (i ) determining the need for new

supplier; (ii ) Identification and elaboration of selection criteria; (ii i ) Initial screening of potential suppliers from a large set; (iv) Final supplier selection; and (v) Continuous

evaluation and assessment of selected suppliers [4]. Supplier selection criteria’s can be qualitative as well as qualitative. Sometimes criteria’s are conflicting and decision maker has to make a tradeoff between them.

Hence supplier selection is considered as a multi criteria decision making problem (MCDM) by researchers [5][6] Comprehensive literature review has been done by authors and from findingsit is elicited that,to evaluate and rank

suppliers, the decision making problem includes selection

of relevant criteria,determination of relative significance of each criterion, weighting finalized criteria and assessment of suppliers over these criteria and finally ranking of supplier based over their performance over these

criteria.[7]–[11]. Dickson (1966)carried pioneer work in field of supplier selection by reporting twenty three criteria’s for supplier selection and classified them

according to their importance[12]. Author classified criteria in to four main category as extreme importance, considerable importance, average importance and slight importance. In this work four criteria of extreme

importance category have been considered as in the present scenario also these criteria exist as of extreme importance for selection of potential suppliers[13].After

finalizing of selection criteria, decision makers are co fronted with two major challenges, i .e. ,weight assignment to selected criteria and assessment of suppliers over these criteria.

Thus objective of this appear is to provide decision maker with a methodology which fulfills both objectives with least mathematical complexity. In this work an integrated methodology has been proposed which involves less

computational steps and presents results to decision makers expeditiously. In this paper weight assignment has accomplished by

applying Analytic Hierarchy Process (AHP) and for assessment of suppliers Weighted Aggregated Sum Product Assessment (WASPAS) method has been applied for ranking of the suppliers.

Rest of the paper is organized as follows: Section two covers literature review. Section three covers steps involved in WASPAS method. Proposed methodology has been covered in section four. Results and discussion has

been covered in section five. Finally conclusions has been presented in section six.

II. LITERATURE REVIEW

Researchers have widely applied AHP for assigning weights to criteria of different fields. Peng (2012) applied AHP for selection of logistics service suppliers [14]. Avikal et

al.(2014) used fuzzy AHP for assigning criteria of disassembly line balancing problem [15]. Jain and Singh (2014) applied AHP for weight assignment for supplier

selection[5]. Veni et al. (2012) used AHP in vendor selection problem[16]. Badri et al. (2016) applied AHP for developing a frame work for evaluating andmonitoring of

Supplier Selection in Indian Iron and Steel

Industry: An Integrated MCDM Approach Naveen Jain

[1], A. R. Singh

[2]

[1] Department of Mechanical Engineering Shri Shankracharya Institute of Professional Management and Technology, Raipur, India

[2]Department of Mechanical Engineering, National Institute of Technology, Raipur, India [1]

naveenjainbit@gmail.com , [2]

amitrajsingh1@gmail.com

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school per romance [17]. Gould et al. (2016) incorporated AHP for evaluation of drug [18] . Singh et al. ( 2012) applied

AHP for developing robust strategies for mitigating operational and disruptive risks in supply chain management [19]. It is elicited from literature review that

AHP has been preferred by authors for weight assignment. WASPAS method is a particular combination of weighted sum method and weighted product method. This method has been widely accepted by authors as it involves simpler

mathematical calculations and easier interpretations of results[20]. WASPAS method has been applied by researchers in various fields. Omoregie & Achebo (2015)

applied WASPAS method for determining the performance evaluation of the various welding processes [21]. Zakare, (2012) applied WASPAS method in computer- aided systems to support multiple criteria decisions [22]. Aghdaie,

Zolfani, & Zavadskas, (2014) utilized Stepwise weight assessment ratio analysis (SWARA) was applied to prioritize and calculate the relative importance of the criteria and WASPAS methodology to evaluate the branches [23].

Madid, Vitkovi, & Trifunovid, (2014) demonstrated applicability and capability of method in selecting software [24].

III. WASPAS METHOD

WASPAS is a recently developed MCDM method proposed by Zavadskas et al. (2012)[25]. The major steps of WASPAS[24]

Step: 1 Determination of Decision Matrix

Xij mxn =

X11 X12 − − X1n

X21 X22 − − X2n

− − − − −− − − − −

Xm1 Xm2 − − Xmn

(1)

Where Xij is assessment value of the i

th alternative with respect to

the jth

criterion. m is number of alternatives

n is number of criteria’s. Step: 2 Normalization of Decision Matrix if the criteria is beneficial in nature

Xij =Xij

max i Xij (2)

where maxi Xij is the most preferable value. if the criteria is non beneficial in nature

Xij =mix i Xij

Xij (3)

where min Xij is the most preferable value.

Step: 3 The total Relative importance of ith

alternative based on WSM [20]

Qi

1 = Xijnj=1 . wj (4)

Where wj is the importance weight of jth

criteria.

Step:4 The total relative importance of the i

th alternative

based on WPM [26]

Qi

2 = Xij

w jnj=1 (5)

Where wj is the importance weight of jth

criteria

Step:5 The generalized equation for determining the total

relative importance of alternatives in WASPAS is [25]

Qi=λ.Qi

1 + (1 −λ).Qi

2 (6)

= λ . Xijnj=1 . wj + (1- λ). Xij

w jnj=1 (7)

Where λ=0,0.1,0.2,………..1.

Based over the value of Q best alternative is chosen. The

best alternative is the one with the highest value of Q. with λ =0, WASPAS method transforms in to WPM and with λ=1 it transforms in WSM [20].

IV. PROPOSED METHODOLOGY

Proposed methodology

Step: 1 Establishment of criteria’s for supplier selection.

Step: 2 Assignment of Importance weights to criteria’s

using AHP method.

Step: 3 Establishment of decision and normalized matrix.

Step: 4 Calculation of Relative weights based on WSM and

WPM.

Step: 5 Calculation for Total relative importance of

alternatives using WASPAS method.

Step: 6 Final ranks are awarded to suppliers.

Flow chart of proposed methodology has been shown in

Fig. 1.

Fig. 1 Flow Chart of Proposed Methodology

V. RESULTS AND DISCUSSION

India is considered as a fast growing economy in world market. Iron and steel industry is a major contributor to Indian economy and is third largest steel producing country

in world. Owing to globalized market, Indian iron and steel industry are facing tough challenge from its counterparts. To withstand the competition, supply chain management optimization has been seen as possible solution by experts.

In this context selection of potential suppliers by steel plants is of strategic importance as suppliers helps industries to improve quality while lowering cost and lead time which ultimately results in higher customer

satisfaction. In this study, middle and top management personnel of iron and steel industry located in central zone of India has

been identified as members of decision making team. All

Establishment of Criteria’s For Supplier Selection

Weight assignment to criteria using AHP

Establish Decision and Normalized Matrix

Establish Total Relative Importance of Alternatives Using WaspasMethod

Award Final Ranks to Suppliers.

Establish Relative weights based on WSM and

WPM

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members of the team have average experience of more than 7 years. Team members has been chosen from

marketing, production, quality control and sales department of the steel plant. In this study four criteria’s of extreme importance has been

chosen out of twenty three criteria. The criteria are: (i) Quality (C1) (ii) Delivery (C2) (ii i) Performance history (C3) and (iv) Cost (C4). Decision makers were assigned the responsibility of comparing each criteria with remaining of

criteria and pairwise comparison was done. Further AHP method has been applied to assign importance weight to these criteria’s. Initially decision matrix is established in

Table I.

TABLE I. COMPARISION MATRIX OF CRITERIA

C1 C2 C3 C4

C1 1 3 5 7

C2 1/3 1 2 3

C3 1/5 1/2 1 3

C4 1/7 1/3 1/3 1

TABLE II. NORMALIZED COMPARISON MATRIX OF CRITERIA

C1 C2 C3 C4

C1 0.6 0.63 0.6 0.5

C2 0.2 0.20 0.24 0.21

C3 0.12 0.10 0.12 0.21

C4 0.08 0.07 0.04 0.08

Normalization of pair wise comparison matrix has been done and calculated and values have been complied in Table II . Based over the values of normalized matrix

priority vectors have been calculated and depicted in Table III.

TABLE III. WEIGHTS OF CRITERIA

Criteria’s Priority vector

Quality (C1) 0.58

Delivery (C2) 0.21

Performance history (C3) 0.14

Cost (C4) 0.07

To ensure consistency of preference for determining criteria weights, consistency check was performed. The

value of λmax has been calculated as 4.08, value of Consistency index (CI) as 2.92% and Consistency Ratio (CR) value is 0.00325 which is less than 0.01 so CR is within

limits. It is observed that Quality criterion is assigned maximum weight and a cost criterion has minimum weight assignment. Further based over these criteria’s six probable suppliers are to be ranked. Six suppliers are assessed over

four criteria’s and linguistic responses are recorded. Choice of linguistic responses are Very low (1), Low (3), Medium (5), High, (7), Very high (9). Results of responses for six

suppliers in form of decision matrix are depicted in Table IV. Application of WASPAS method initiates with normalization of decision matrix as per step 2. Out of four considered

criteria’s Quality and Performance history are beneficial criteria’s where as Delivery and Cost are non beneficial

criteria’s. For normalization of Quality and Performance history criteria’s, equation (1) is applied and for Delivery and Cost criteria’s equation (2) is utilized. Normalized

Matrix has been shown in Table V .Subsequently, total relative importance (Qi

(1)) of each alternative is calculated

as per equation (3) of step 3. Further total relative importance (Qi

(2)) for WPM is calculated using equation (4).

Finally joint criterion of optimality (Q i) of WASPAS method is calculated using equation (5). Values of total relative importance for all suppliers, for λ =0.5 has been shown in

Table VI . On the basis of calculated values of Qi suppliers are ranked. From the values of Table VI it is observed that Supplier S3 has highest value of Qi and supplier S1 has the least value, making them as the best and the last choices

respectively. Final ranking of suppliers is S2>S4>S3>S1>S5>S6.

TABLE IV. DECISION MATRIX FOR WASPAS METHOD

C1 C2 C3 C4

S1 3 5 1 5

S2 9 7 7 1

S3 5 9 5 5

S4 7 1 3 9

S5 3 5 1 7

S6 1 3 7 3

TABLE V NORMALIZED DECISION MATRIX FOR WASPAS METHOD

C1 C2 C3 C4

S1 0.33333 0.20000 0.14286 0.20000

S2 1.00000 0.14286 1.00000 1.00000

S3 0.55556 0.11111 0.71429 0.20000

S4 0.77778 1.00000 0.42857 0.11111

S5 0.33333 0.20000 0.14286 0.14286

S6 0.11111 0.33333 1.00000 0.33333

TABLE VICOMPUTATIONAL DETAILS OF THE WASPAS METHOD FOR Aʎ

VALUE OF 0.5

Qi

(1) Qi

(2) Qi RANK

S1 0.269333 0.256593 0.262963 4

S2 0.82 0.664553 0.742276 1

S3 0.459556 0.38209 0.420823 3

S4 0.728889 0.658237 0.693563 2

S5 0.265333 0.25062 0.257977 5

S6 0.297778 0.205563 0.25167 6

VI. CONCLUSIONS

Selection of potential supplier is a difficult MCDM decision involving set of quantitative and qualitative

criteria’s. In available literature different MCDM methods have been applied by researchers and academicians to structure the supplier selection problem and reach a unbiased decision. In present work an integrated

methodology has been proposed which helps decsion

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makers to select potential suppliers in less time. Proposed methodolgy offers advantage of encapsulating any number

of quantittaibe and quali tative criteria as desired by decsion makers. Further this method offers simple computational procedure for determination of ranking of

alternatives under consideration.Results of the present work elicit that the proposed methodology can help decision makers in selecting right suppliers and also reduce the decision making time significantly.

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