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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]
[email protected] , [2]
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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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