Scholarly article on topic 'An Integrated Approach for Supplier Selection'

An Integrated Approach for Supplier Selection Academic research paper on "Computer and information sciences"

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{"supply chain density" / complexity / "node criticality" / AHP / QFD / TOPSIS}

Abstract of research paper on Computer and information sciences, author of scientific article — Anupam Haldar, Debamalya Banerjee, Amitava Ray, Surojit Ghosh

Abstract This research is emphasized to develop a quantitative approach for assessing supply chain resilience under disruption environment, which incorporates an analytical framework in supply chain design. A three-stage framework is developed on supply chain quality, configuration and technology management. This is based on a survey of the perceptions of practicing managers from an OEM (original equipment manufacturer). The three-stage process involves empirical assessment of strategic supply chain quality, configuration and technology variables and then using quality function deployment to deploy them to improve the competitiveness of the supply chain. The focus of this multi-dimensional approach is on the selection of a suitable supplier considering multi-criteria by integrating Quality Function Deployment (QFD) tool with Technique for Order Preference by Similarity to Ideal Solution TOPSIS. A number of decision determinants for supply chain resilience have been chosen as technical requirements and customer criteria's. Furthermore, analytical hierarchy process (AHP) has also been embedded to deal with linguistic judgments expressing relationships and correlations required in QFD. TOPSIS is used to develop the ranking of a number of suppliers considering different multi-dimensional parameters in a uniform system. To illustrate the practical implications of the methodology, the approach are exemplified with the help of a case study in the automobile OEM scenario. This concept has helped in decision making in the occurrence of disruption on the performance of the supply chain system. Based on the strategic model developed, some very interesting managerial insights are offered with respect to the effect of cost efficient operations and resiliency in volatile or competitive global market scenario for choosing alternate suppliers, which in turn affects the overall supply chain performance. Finally decision maker's perception has taken an important role for the supplier selection i.e., whether he is much more worry about cost or rational to pay moderately for developing a resilient system in the supply chain.

Academic research paper on topic "An Integrated Approach for Supplier Selection"

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Procedia Engineering 38 (2012) 2087 - 2102

INTERNATIONAL CONFERENCE ON MODELLING, OPTIMIZATION AND

COMPUTING

An integrated approach for supplier selection

Anupam Haldara, Debamalya Banerjeeb, Amitava Rayc and Surojit Ghoshd*

aNetaji Subhash Engineering College, Kolkata, India bJadavpur University, Kolkata, India, cSikkim Manipal Institute of Technology, Sikkim, India and dThe Institution of Engineers (India), Kolkata, India

Abstract

This research is emphasized to develop a quantitative approach for assessing supply chain resilience under disruption environment, which incorporates an analytical framework in supply chain design. A three-stage framework is developed on supply chain quality, configuration and technology management. This is based on a survey of the perceptions of practicing managers from an OEM (original equipment manufacturer). The three-stage process involves empirical assessment of strategic supply chain quality, configuration and technology variables and then using quality function deployment to deploy them to improve the competitiveness of the supply chain. The focus of this multi-dimensional approach is on the selection of a suitable supplier considering multi-criteria by integrating Quality Function Deployment (QFD) tool with Technique for Order Preference by Similarity to Ideal Solution TOPSIS. A number of decision determinants for supply chain resilience have been chosen as technical requirements and customer criteria's. Furthermore, analytical hierarchy process (AHP) has also been embedded to deal with linguistic judgments expressing relationships and correlations required in QFD. TOPSIS is used to develop the ranking of a number of suppliers considering different multi-dimensional parameters in a uniform system. To illustrate the practical implications of the methodology, the approach are exemplified with the help of a case study in the automobile OEM scenario. This concept has helped in decision making in the occurrence of disruption on the performance of the supply chain system. Based on the strategic model developed, some very interesting managerial insights are offered with respect to the effect of cost efficient operations and resiliency in volatile or competitive global market scenario for choosing alternate suppliers, which in turn affects the overall supply chain performance. Finally decision maker's perception has taken an important role for the supplier selection i.e., whether he is much more worry about cost or rational to pay moderately for developing a resilient system in the supply chain.

©2012 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Noorul Islam Centre for Higher Education

Keywords : supply chain density; complexity; node criticality; AHP; QFD; TOPSIS;

1877-7058 © 2012 Published by Elsevier Ltd. doi:10.1016/j.proeng.2012.06.251

*Tel: +8013352359;E-mailaddress: anujuster@gmail.com

Nomenclature

D Decision Matrix

xij Element of Decision Matrix

rij Element of element of the normalized decision matrix

Vi Element of weighted normalized decision matrix

Vi+ column maximum

Vi- column minimum

Si+ Separation Measure for positive ideal solution

Sr Separation Measure for negative ideal solution

CR closeness ratio

Fi Factors

Aj Alternatives

SSQ Sum of squares

SQRT Square root of Sum of Squares

a Attitude of the decision maker

1. Introduction

The business environment is facing challenge to manage constantly changing and uncertain future which requires resilience i.e., the capacity of an organisation to survive, adapt and sustain the business while facing the turbulent change. Resilience is "the ability of a system to return to its original [or desired] state after being disturbed". Implicit in this definition is the notion of network flexibility, and given that the desired state may be different from the original, 'adaptability' is also implied. A supply chain may also be defined as: "the network of organizations that are involved, through upstream and downstream linkages, in the different processes and activities that produce. The term "Supply chain resiliency" in the management arena has evolved in the recent years due to the volatile market situation. When working effectively and efficiently modern supply chains allow goods to be produced and delivered in the right quantities, to the right places, at the right time in a cost effective manner. In the manufacturing scenario, the term "supply chain" accumulates the images of sub-assemblies moving from suppliers to the manufacturers to distributors to retailers to customers, along a chain, in order to fulfil a customer request; Gong et al. [1]. In the present scenario the complexity of markets, uncertainty and turbulence, supply chain has become vulnerable to different kinds of risks. Globalization and recession has leaned the supply chain causing more risk. Although global recessions are rare, uncertainty and unpredictability are the facts of life in today's business environment. The future can not be truly predicted despite of the modern forecasting models followed by the companies. Today supply-chain becomes longer-reaching to the low cost countries for outsourcing or manufacturing. So it becomes increasingly clear that greater flexibility and the ability to react rapidly with the global change in the market conditions have the same importance as forecasting skills. Natural disasters, terrorism, transportation uncertainties results serious disruptions to supply chain activities. Supply chain managers' attempts to achieve the ideals of fully integrated efficient and effective supply chains, capable of creating and sustaining competitive advantage described by Christopher et. al [2]. In traditional supply chains, purchasing, production, distribution, planning and other logistics functions are handled independently by decision makers. To overcome global risks in

related markets, decision makers are interested to fix a mechanism where different objective functions (customer requirements, technical requirements, low production/inventory costs etc.) can be integrated together. Supply chain management (SCM) explicitly recognizes interdependencies and requires effective relationship management between layers and nodes. The challenge in global SCM is the development of decision-making frameworks that accommodate diverse concerns of multiple components across the supply chain. Considerable efforts have been spend in developing decision models for supply chain problems, Narasimhan et. al. [3]. The products and information flows travel within and between nodes in a variety of networks which link organisations, industries and economies.

Supply chain management integrates suppliers, manufacturers, distributors, and customers to meet final consumer needs and expectations efficiently and effectively, Cox [4]. Verma and Pullman [5] and Kannan and Tan, [6] explains quality, delivery, price of materials and services, responsiveness and service consistently emerge to be the important criteria for supplier selection. Appropriate global supply chain strategies can be developed depending upon market characteristics and by simultaneously seeking to achieve higher levels of customer responsiveness at a lower total cost to the supply chain as a whole, Christopher and Towill [7]. The reduction of the manufacturing depth leads to an increase of the proportion of the purchased parts and accordingly increases the dependency on suppliers, Maron and Bruckner [8]. Kagnicioglu [9] explains that supplier selection is a critical activity of purchasing management in a supply chain due to the key role of supplier's performance on cost, quality, delivery and service in achieving the objectives of a supply chain. Proper supplier selection surprisingly reduces the purchasing costs and improves corporate competitiveness, Ghodsypour and O'Brien [10]. An efficient supplier management begins with the identification of potential suppliers is of the central importance for successful supply chain management, Lasch and Janker [11]. A typical configuration for a supply chain consists of defining components of the system, setting operation policies for governing the interrelationships among these components and assigning values to characteristic parameters of each component, Truong and Azadivar [12].

Better management of supply inputs and timely delivery of products and services at the lowest possible cost are effective practices for achieving sustainable business success, Cox and Chicksand [4]. Huang and Keskar [13] recommended cost and quality have been the most dominant factors along with on-time delivery and flexibility. They describes an integration mechanism in terms of a set of comprehensive and configurable metrics arranged hierarchically that takes into account product type, supplier type and OEM/supplier integration level. In network design models focus is on the trade-off between the fixed costs of locating facilities and variable transportation costs between facilities and customers. A model was presented by Sourirajan et al. [14] for single product distribution network design problem with lead times and service level requirements, which enables them to capture the trade-off between lead times and inventory risk pooling benefits. The introduction of degree of alignment with the buyer is a novel feature in Hsu et al. [15] and Huang and Keskar [13]. "For companies, resiliency measures the ability to and speed at which they can, return, to their normal performance level (production, services, fill rate) following a disruption", Sheffi, [16]. In the available research literature about the concept of supply chain resilience (SCR) is emphasized more on supply chain management, risk, vulnerability, logistics, collaboration, Norrman et. al [17] Peck [18]; Sheffi [15]; Manuj and Mentzer [19]; Motiwalla and Thompson [20]. Allen et al., [21] emphasised that resilience does not only involve recovering from a disruption but it is a supply chain aspect that entails proactiveness , structured and integrated exploration of capabilities as measures to deal with uncertainties. Today supply chains have been changing from linear to complex networks that span the globe, regional or national. In the present global situation organisations are compelled to access worldwide markets and increase their speed of response to changes within the market. The only possible solution to maintain the operational efficiency is there must be, no disruptions to supply, demand and flow of information. The attitude of organisations for outsourcing of

non-core activities has contributed to the growth of global supply chains with several links that interconnect numerous networks of firms. As a result vulnerability to disruptions, bankruptcies, breakdowns, political and macroeconomic risks are occurred, Manuj and Mentzer [19].

The route map framework of supply chain resiliency is propounded by Christopher and Peck [22], which needs the significant analysis of the supply chain network connecting the focal organisation to its suppliers, network partners of their suppliers and to its customers downstream and their customers. Christopher and Peck [22] explained in their research that, supply chain resilience is a new phenomenon in the area of management, which has not been widely explored and is associated with supply chain networks. Supply chain networks are complex, dynamic and involve uncertainties that arise as a result of interplay between its structure, environment and function, Allen et al., [21]. As the supply chain spans over focal firm boundaries, it is critical to understand factors relating to the host countries on individual basis. Clear understanding of the supply chain improves the planning process and understanding of sources of disruptions. Supply chain is affected by disruptions which occur in various forms such as transport delays, terrorism activities, communication breakdown, industrial action, accidents, natural disasters, port stoppages, BlackHurst et al. [23]; World Economic Forum [24]; Sheffi [16]; Peck [18]; Schary and Skjott-Larsen, [25]; Manuj and Mentzer, [19]. Lin [26] thinks that supplier selection for reducing supplier base is an important goal in supply chain management (SCM). It is not possible to forecast the future, so that the organisations can equip themselves to adapt to the turbulence ahead, so resilience may be the key to sustainability Hart and Milstein [27], Hamel and Valikangas [28], Moore and Manring [29].

Falasca et al. [30] developed a quantitative approach for assessing supply chain resilience to disasters. Resilience is defined as the ability of a supply chain system to reduce the probabilities of disruptions, to reduce the consequences of those disruptions, and to reduce the time to recover normal performance. The decision framework incorporates three determinants of supply chain resilience (density, complexity, and node criticality) and their relationship to the occurrence of disruptions, to the impacts of those disruptions on the performance of a supply chain system and to the time needed for recovery is discussed. Lin [26] reconizes that supplier selection for reducing supplier base is an important goal in supply chain management (SCM). Without clear definitions and explanations of drivers of resilience and its importance for sustainability, resilience is merely a theoretical concept in small and medium enterprises (SMEs), Aylin Ates et. al [31]. The number of performance metrics that one could consider to aid in supplier selection is not only large but also depends on the context (strategic, operational, etc.), type of the product, nature of the markets, etc. Cost and responsiveness are the most important factors for selecting a supplier. In the context of our model, responsiveness is the ability of the supply chain to respond quickly to changing customer needs, preferences, options, etc. in terms of supply chain cycle time. Majority of the existing models are cost focused and do not address the responsiveness. Both of these issues are addressed in our model. Another major difference is that the model works on the strategic perspective with the aim of developing managerial insights for the supplier selection in a supply chain, Gangaraju Vanteddu, Ratna, [32].

1. a) Supply chain density

Supply chain density is explained as the quantity and geographical spacing of nodes within a supply chain. Several sub-assemblies are compiled at a connecting point or nodes for further operation from a manufacturing point of view. As a large number of nodes within a supply chain are clustered closely together, the portion of the supply chain is said to have a high density level. To increase the resilience of a dense supply chain the nodes may be redistributed for reducing the risk associated with those nodes, b) Supply chain complexity

Supply chain complexity; Craighead et al. [33] is related both to the number of nodes in a supply chain and to the interconnections between those nodes. Addition of extra nodes to a supply chain converts the supply chain more complex but it creates a practical advantage incurring anxiety for timely receiving of a critical item. The additional resource of supply can act as a potential "buffer" in the system. The added resource actually increase the overall supply chain resilience level, F. De Felice et. al. [34].

c) Number of critical nodes in a Supply chain

Node criticality is the relative importance of a given node or set of nodes within a supply chain; Craighead et al., [33]. The number of critical nodes in a supply chain is one of the most vital factor of supply chain resilience. For example, in a manufacturing unit, a disruption that affects the most critical node in the entire supply chain has a different impact on supply chain performance than a disruption that affects a supplier of generic item (e.g., a standard nut & bolt). A supply chain that contains a large number of critical nodes would have a greater potential for disruption than if these critical processes is distributed among several different nodes.

2. TOPSIS for MCDM

Multiple criteria decision-making (MCDM) is used to select a project from several alternatives according to various criteria. The technique for order preference by similarity to ideal solution (TOPSIS) was first developed by Hwang and Yoon [35] based on the concept that the chosen alternative should have the shortest distance from the positive ideal solution (PIS) and the farthest from the negative-ideal solution (NIS) for solving a multiple criteria decision-making problem. Thus, the best alternative should not only have the shortest distance from the positive ideal solution, but also should have the largest distance from the negative ideal solution. In short, the ideal solution is composed of all best values attainable of criteria, whereas the negative ideal solution is made up of all worst values attainable of criteria. The calculation processes of this method are as follows.

1. Normalization of the evaluation matrix. The process is to transform different scales and units among various criteria into common measurable units to allow comparisons across the criteria. Assume Xy to be element of the decision matrix D of alternative i under customer criterion j then an element r^ of the normalized decision matrix R can calculated by many normalization methods.

2. Construction of the weighted normalized evaluation matrix: we cannot assume that each evaluation criterion is of equal importance because the evaluation criteria are not uni-dimensional, they have various meanings. There are many methods that can be employed to determine weights [Hwang et. al. 35], such as the eigenvector method, weighted least square method, entropy method, AHP, as well as linear programming techniques for multi-dimension of analysis preference (LINMAP). The selection of method depends on the nature of the problems. The weighted normalized decision matrix is used here by multiplying the normalized decision matrix r^ with its associated weight wj to obtain the result.

V - [wjr.j] - [vs] (1)

3. Determination positive and negative ideal solutions: the positive ideal solution (A+) indicates the most preferable alternative (column max.), and the negative ideal solution (A ) indicates the least preferable alternative (column min.).

A+= [maxi=1>...mVii, maxi=1>...mVi2,......., maxi=lj...mVii,]=[Vi+, V2+,.......Vm+] (2)

A" = [minH...BVii, mini=1>...mvi2,........., mini=1>...mVii,M W, V2",.......Vm"] (3)

4. Calculation of the separation measure: the separation from the positive and negative ideal for each alternative can be measured by the n-criteria Euclidean distance [Hwang et. al. [35],

S; +=[ (Vij - Vjt)2]"2, where i=l,2........m (4)

S;" =[ (vy - V")2]1'2, where i=l,2.......,m (5)

5. Calculation of the relative closeness to the ideal solution: the relative closeness of the ith alternative with respect to ideal solution A + is defined as

CRr —¡—5—r where, 0< CR, <1, i=l,2,....,m (6)

Sf +St

6. Ranking of the priority: a set of alternatives then can be preference ranked according to the descending order of CR;.

3. Analytical Hierarchy Process (AHP)

The Analytic Hierarchy Process (AHP), was introduced by Saaty [36], is a multi-criteria method, which tries to satisfy the several conflicting criteria involving subjective or intangible criteria. AHP evaluates decision alternatives by pair wise comparisons. The comparisons are recorded in a square reciprocal matrix. The Priority vector among the criteria's is determined using AHP on the basis of Saaty's nine point scale. The consistency of pair wise comparison matrix can be measured by calculating a consistency ratio. The consistency of pair wise comparison matrix can be measured by calculating a consistency ratio. The Consistency Ratio fC.R.) of each of the matrices is checked using the following relation:

C.R. = C.I. / R.I. (7)

Where, R.I. is the Random Consistency index, which depends on the number of alternative. The Consistency Index (C.I.) is measured by computing C.I. of the 'N' alternatives by the following relationship:

CX^W-NytN-l) (8)

where, 'N' is the decision alternatives or dimension of the matrix and is the eigen-value. A consistency ratio of less than 0.1 should be obtained otherwise it should be re-evaluated.

4. Quality Function Deployment (QFD)

Quality Function Deployment (QFD) is a quality tool that helps to translate the Voice of the Customer (VoC) into new products that truly satisfy their needs. The concept of QFD was created in Japan in the late 1960s. It is a method of continuous product improvement, emphasizing the impact of organizational learning on innovation, Govers, [37] .The QFD method includes building one or more matrices known as 'quality tables.' The first matrix is named the "House of Quality" (HoQ). It exhibits the customer's needs or voice of customer (VoC) on the left hand side, and the technical response to meet those needs along the top. The task of the QFD team is to list the technical requirements (TRs). These requirements are most likely to affect the CRs. TR evaluators, in the QFD team, evaluate how the competitors' products compare with that of company's product. This evaluation leads to fixing of technical targets. From the QFD matrix, the discrepancies, if any, between the customers' perception and the QFD team's correlation of CR and TR can be easily understood. The vertical part of the QFD matrix shows how the company may respond to customer requirements.

5. Proposed methodology

This paper uses a TOPSIS methodology to determine the weighting of customer criteria (subjective judgments) where personal preference is modified by the decision weights. We considered normalized closeness ratio derived from the TOPSIS methodology as the weights of the customer criteria in the QFD

matrix. From QFD matrix we determined degree of importance of selection criteria (technical criteria). For each of the technical criteria AHP is used to determine the comparative weights of the alternative suppliers. The normalized value of this technical criteria and the importance weight of each of the alternative for the technical criteria are considered for determining the overall score of the suppliers. These are the subjective judgments of the alternatives. Cost values are used to determine the objective factor measurement of each of the alternatives using the mathematical model presented by Ray A. et. al [38]

Step 1: Identification of the essential decision determinants for the normal selection.

Step 2: Determining the closeness ratio on the basis of the essential decision determinants using TOPSIS.

Step 3: Developing a QFD matrix considering personal preference of TOPSIS as the importance weights

(on the basis of the essential decision determinants) of the customers (here suppliers) criteria.

Step 4: Individual importance of each of the supplier for each of technical criteria is determined from the

AHP matrix develop on the basis the collected data.

Step 5: Now overall score for each of the supplier alternatives is determined on the basis of the technical criteria imposed by manufacturer.

Step 6: This overall score of step 5 is considered as the subjective factor.

Step 7: Now objective factor of each of the supplier is determined considering the cost of the equipment. Step 8: Ranking of all the supplier alternatives and selection of the best one using the analogy "the higher the score, the better the selection" (Chuang [39]

6. A Case study

For developing a strategy for supply chain resiliency for a automobile giant the decision maker's select it's supplier among a number of potential suppliers for its in the volatile market situation. Calculation starts from the personal preference (linguistic) of the decision makers. AHP is used to determine the personal weights among the suppliers. The essential decision determinants for the normal selection of the supplier are considered for the initial ranking of the supplier. Profit margin (Fl) (in Rs/unit), Production rate (F2) (unit/hour), Processing Time (F3) (in minutes), Buffer capacity (F4) (no. of unit) and percentage of actual capacity of the resource utilised (F5) are chosen as the essential decision determinants for each of the five supplier alternatives as supplier selection criteria. TOPSIS is used for determining the ranking on the basis of the essential decision determinants.

A QFD matrix is developed where normalized closeness ratio of TOPSIS is considered as the revised importance weights (on the basis of the essential decision determinants) of the customers' (here supplier's) criteria. Initially AHP matrix is developed among the suppliers for each of the technical criteria. The decision maker's, importance weights of the suppliers are considered. Six of the decision determinants of supply chain resilience have been chosen as technical requirements of the supplier. Apart from that five other determinants have been considered for rating the suppliers with the help of QFD. Five customer criteria's for supplier selection are supplier/customer Profit margin (Fl), Production rate (F2), Processing Time (F3), Buffer capacity (F4) and percentage utilisation of resource capacity (F5). This concept has helped in decision making in the occurrence of disruption on the performance of the supply chain system in the present work. AHP has also been embedded in this work to make the pairwise comparison with the conventional approach. TOPSIS and Decision maker's attitude has taken an important role for the supplier selection. The objective of the paper is to explain how, using a combined TOPSIS-AHP-QFD model, the authors are able to determine the ranking of the suppliers.

Table 1: Decision Matrix

FACTORS

ALTERN F5 (percentage

ATIVES F1 (Rs/unit) F2 (unit/hour) F3 (minutes) F4 (no. of unit) of actual capacity)

Al 52 4324 127 2650 84

A2 46 3980 158 2480 76

A3 55 6173 144 2430 87

A4 43 5327 125 2366 92

A5 39 5733 136 2510 71

SSQ 11215 133887523 95950 30975856 33906

SQRT 105.9008971 11570.97762 309.75797 5565.595745 184.1358194

Table 2: Normalised Decision Matrix

Fl F2 F3 F4 F5

Al 0.491025113 0.373693576 0.409997522 0.4761395040.456185007

A2 0.43436837 0.343964022 0.510075657 0.445594706 0.412738815

A3 0.519353485 0.533489927 0.46487908 0.4366109420.472477328

A4 0.406039998 0.460375966 0.403540868 0.4251117240.499631198

A5 0.368268835 0.495463753 0.439052464 0.4509849650.385584946

Table 3: Pair-Wise Comparison Matrix (Personal Preference)

Fl F2 F3 F4 F5 Weights (Eigen Vector)

Fl 1 4 2 1/2 3 0.253436

F2 1/4 1 1/3 1/7 1/2 0.0553932

F3 1/2 3 1 1/3 2 0.154035

F4 2 7 3 1 5 0.447457

F5 1/3 2 1/2 1/5 1 0.0896775

The priority vector (PV) values of this decision matrix are [0.253436, 0.0553932, 0.154035, 0.447457, 0.0896775]T. To check the level of consistency, equations (1) & (2) are used, and the results obtained are where, Maximum Eigen-Value, X ^ = 5.03417; CI=0.00854236; RI =1.12; CR=0.007627107; as CR<10%, the level of inconsistency present in the information present in the information stored in 'D' matrix is within acceptable range or satisfactory.

Table 4: Weighted Normalised Decision Matrix (V) for Regular/Normal-Supplier Selection

_Fl_FT_F3_F4_F5

Al 0.124445405 0.020698887 0.063156018 0.213053383 0.0409107

A2 0.11008632 0.019052167 0.078572054 0.199385807 0.0370144

Vij = A3 0.131624947 0.029550007 0.071609973 0.195365932 0.0423718

A4 0.102906777 0.025500225 0.062161435 0.190220492 0.0448069

A5 0.093334054 0.027443737 0.067631642 0.201797732 0.0345793

MAX (A+) 0.131624947 0.029550007 0.078572054 0.213053383 0.0448069

MIN (A-) 0.093334054 0.025500225 0.071609973 0.195365932 0.0345793

Table 5: Calculation of Separation Measure

Si+=SQRT(SSQ) Si-=SQRT(SSQ) Closeness Ratio (CR) Normalised CR

Sl+ 0.01956331 SI" 0.037621418 CR1 0.787150685 0.3728355

S2+ 0.028664315 S2" 0.019818573 CR2 0.323427007 0.1531918

S3+ 0.019163676 S3" 0.039285067 CR3 0.807781012 0.3826071

S4+ 0.040395338 S4" 0.017663314 CR4 0.160508541 0.0760252

S5+ 0.042680398 S5" 0.007808484 CR5 0.032387539 0.0153404

The ranking of the suppliers on the basis of customer criteria (CR) only is A3>A1>A2>A4>A5.

Table 6: A QFD matrix is developed

Technical criteria's for supplier selection (for developing resilient Supply chain)

Factors

(¡4 W

Fl F2 F3 F4 F5

Supply

Density

Supply Chain

Complexity G3

Number Of

Critical

g p- Degree wj ^importance

S lfor

g q selection

fa ^.criteria £

G4 G4 of3.8923275

G3 G3 3.9528525

G4 G4 Gl G4 G2

4.2829161

£ Normalised 148202024 0.150506539 0.163073851 2 degree of U importance hJfor

52 oo selection

criteria %)

Supply Chain Re- ' Engineering

Supplier's Important Resource Weightings Flexibility of

Customer

Responsiveness Requirements

G2 Gl 0.253436

G2 Gl Gl 0.0553932

Gl Gl G4 0.154035

G4 G2 0.447457

G4 G2 G5 0.0896775

2.0430999 5.6290193 6.4634443

0.077791897 0.214327302 0.246098388

Legends: Gl: Strong = 9; G2:Less strong=7; G3: Moderate = 5; G4:Weak = 3; G5: more weak=l; & Blank: No relationship exists = 0_

Table 7: Pairwise Comparison Matrix for Supply Chain Density Among Suppliers

Weights 1 (Eigen A5_Vector)

0.432013

A2 1/3 1 1/2 4 3 0.16966

A3 1/2 2 1 6 4 0.277516

A4 1/7 1/4 1/6 1 1/2 0.0460795

A5 1/5 1/3 1/4 2 1 0.074732

The priority vector (PV) values of this decision matrix are [0.432013, 0.16966, 0.277516, 0.0460795, 0.074732]T. To check the level of consistency, equations (1) & (2) are used, and the results obtained are where, Maximum Eigen-Value, X = 5.07448; CI=0.0186194; RI =1.12; CR=0.016624464; as CR<10%, the level of inconsistency present in the information present in the information stored in 'Ml' matrix is within acceptable range or satisfactory.

Table 8: Pairwise Comparison Matrix for Supply Chain Complexity Among Suppliers

Weights 2

A1 A2 A3 A4 A5 (Eigen Vector)

A1 1 1/4 1/2 2 3 0.138728

A2 4 1 2 5 7 0.455692

A3 2 1/2 1 4 5 0.268176

A4 1/2 1/5 1/4 1 3 0.0899731

A5 1/3 1/7 1/5 1/3 1 0.047431

Maximum Eigen Value, Xmi= 5.11055; C.I.=0.0276386; CR=0.024677321 <10% i.e., the level of inconsistency present in the information present in the information stored in 'M2' matrix is satisfactory.

Table 9: Pairwise Comparison Matrix for Number Of Critical Nodes Among Suppliers

A1 A2 A3 A4 A5 Weights 3 (Eigen Vector)

A1 1 4 2 1/2 3 0.253436

A2 1/4 1 1/3 1/7 1/2 0.0553932

A3 1/2 3 1 1/3 2 0.154035

A4 2 7 3 1 5 0.447457

A5 1/3 2 1/2 1/5 1 0.0896775

Maximum Eigen Value, X max =5.03417; C.I "0.00854236; CR-007627143 <10% , i.e., the level of inconsistency present in the information present in the information stored in 'M3' matrix is satisfactory.

Table 10: Pairwise Comparison Matrix for Responsiveness Among Suppliers

Weights 4

A1 A2 A3 A4 A5 (Eigen Vector)

SI 1 8 2 0.5 3 0.254533

S2 1/8 1 1/5 1/9 1/2 0.0371309

S3 1/2 5 1 1/6 2 0.134154

S4 2 9 6 1 5 0.49531

S5_1/3_2_1/2_1/5_1_0.0788716

Maximum Eigen Value =5.12373; C.I.=0.0309327; CR=.027618482; <10% , i.e., the level of inconsistency present in the information present in the information stored in 'M4' matrix is satisfactory.

Table 11: Pairwise Comparison Matrix for Supply Chain Re-Engineering Among Suppliers

Al A2 A3 A4 A5 Weights 5 (Eigen Vector)

Al 1 1/7 1/5 2 1/3 0.0622679

A2 7 1 2 9 5 0.495336

M5 = A3 5 1/2 1 6 2 0.265512

A4 1/2 1/9 1/6 1 1/3 0.0428757

A5 3 1/5 1/2 3 1 0.134008

Maximum Eigen Value =5.08373, C.I.=0.0209329, CR=0.074758928; <10% , i.e., the level of

inconsistency present in the information present in the information stored in 'M5' matrix is satisfactory.

Table 12: Pairwise Comparison Matrix for Supplier's Resource Flexibility

Al A2 A3 A4 A5 Weights 6 (Eigen Vector)

Al 1 7 5 3 1/2 0.296849

A2 1/7 1 1/3 1/5 1/9 0.0349278

M6 = A3 1/5 3 1 1/2 1/7 0.0716562

A4 1/3 5 2 1 1/4 0.12938

A5 2 9 7 4 1 0.467187

Maximum Eigen Value =5.12503, C.I.=0.0312578; CR=0.02790875; <10%, i.e., the level of

inconsistency present in the information present in the information stored in 'M6' matrix is satisfactory.

Table 13: Overall scores of the five suppliers

Importance weights for suppliers

Technical criteria's Wt. of TR

for supplier from Al A2 A3 A4 . _ Consistency Rärin

selection QFD matrix Iva LIU

Supply chain

density 0.148202024 0.432013 0.16966 0.277516 0.0460795 0.074732 0.016624464

Supply chain

complexity 0.150506539 0.138728 0.455692 0.268176 0.0899731 0.047431 0.024677321

Number of critical

nodes 0.163073851 0.253436 0.0553932 0.154035 0.447457 0.0896775 0.007627143

Responsiveness 0.077791897 0.254533 0.0371309 0.134154 0.49531 0.0788716 0.027618482

Supply chain re-

engineering 0.214327302 0.0622679 0.495336 0.265512 0.0428757 0.134008 0.074758928

Supplier's Machine 0.246098388 0.296849 0.296849 0.0716562 0.12938 0.467187 0.02790875

flexibility

Overall score 0.2324338 0.2848683 0.1915867 0.1728998 0.1826692

(SFM)_33_36_95_98_77_

Ranking of the supplier will be modified to A2>A1>A3>A5>A4, when both of the criteria (CR) & (TR) are integrated. Now we are considering the Objective factor measure (OFM), on the basis of cost factor.

Table 14: Objective factor measure (OFM)

SUPPLIERS

Al A2 A3 A4 A5

COST (Fl) in 102 Rs 64 58 72 61 69

1/OFCi 0.015625 0.017241379 0.013888889 0.016393443 0.014492754

V = OFCi * SUM 4.969053725 4.503204938 5.59018544 4.736129331 5.357261047

OFM = 1/V 0.20124556 0.222064066 0.178884942 0.211142883 0.186662549

The supplier selection index is calculated using the mathematical model presented by Ray A., Sarkar B. & Sanyal S.[37],

Table 15: SFM and OFM values of the criteria

SFM OFM

0.232433833 0.20124556

0.284868336 0.222064066

0.191586795 0.178884942

0.172899898 0.211142883

0.182669277 0.186662549

8. Results & discussion

With the dramatic growth of the international business sector and globalisation, the concept of business outsourcing even to the outside their native country has increased greatly. Initially this paper uses TOPSIS methodology to rank the supplier which is based on the customer criteria (subjective judgments) only, where personal preference is modified by the decision weights. Secondly, the customer weight of the first stage is integrated in stage 2 and the overall score (Table 13) determine the ranking of the supplier. We have used the cost criteria to determine the objective factor for supplier selection. We used the sensitivity analysis for the final selection of the supplier. The normalized value of this technical criteria and the importance weight of each of the alternative for the technical criteria are considered for determining the overall score of the suppliers. These are considered as the subjective judgments of the alternatives. Cost values are used to determine the objective factor measurement of each of the alternatives. Now using the mathematical model proposed by Ray A. et. al. [37] to combine the cost-factor components with the importance weightings found form QFD. The governing equation of the stated model is

SSIi=[(aXSFMi) + (l-a)OFMi], (9)

Where, OFMi =--(10)

[OFM^OFC-1]

Where, OFM is the objective factor measure, OFC is the objective factor cost. SFM is the subjective factor measure, SSI is the suppliers selection index, a is the objective factor decision weight and n is the number of alternative suppliers (n=5 in the present case). SFM values, i.e., are the global priorities for each candidate supplier. The choice of a is one of the vital issue which is the combined decision of the design engineer, production engineer and the vendor development engineer of a manufacturing firm. This value depends on the decision maker's perception. The supplier selection index (a) in the supplier selection problem is analysed using the relation in (9).

Fig 1. Sensitivity Analysis

0.3 0.4 0.5 0.6 0.7 Decision maker's perception

9. Conclusion

Supply chain management has recently reached to a considerable attention in business management literature. Supplier selection is the fundamental decision for the overall well being of an industry. Supply chain is a complex multi criteria decision problem that includes qualitative, quantitative human judgment. There are many advantages of this approach. First, multiple qualitative and quantitative factors can be considered to evaluate the response of alternative suppliers. This will ensure that the selected supplier is optimal in terms of (quality, availability, lowest cost, safety, reliable customer service, Supply chain

density, complexity, number of critical nodes within the supply chain, Re-engineering on the subassembly and Supplier's resource flexibility and above all the shock absorption capacity of the supply chain). Second, the evaluating factors are related to the variable cost of the company. This ensures optimal selection of the supplier can be achieved considering the organizational objectives. Third, the application of AHP ensures consistent supplier performance measurement using the quality index. This ensures that the judgments made are guaranteed to be consistent, which is the basic ingredient for making good decisions. Fourth, the proposed approach involves a team of people representing various functional departments (finance, procurement, production, and quality control) for supplier selection. Fifth, selection index combines both the qualitative and quantitative data for decision making. As the 'attitude' of the decision maker changes, the proposed model could be applied to check the effect of changes in the importance levels of various factors on final outcome. Therefore, the proposed approach is quite straightforward for considering the ingredients of different factors for supplier selection rather than the conventional approaches for the same and supplier performance measurement. The prime limitation or drawback of proposed approach due to AHP and QFD is for reaching in consensus, it may be time-consuming. Decision makers have to compare each of the criterias in a pair wise fashion based on their own experience and knowledge. Similar approach could be adopted in other areas for effective management of supply chain and selection, distribution centre and warehouse location evaluation and selection, supply chain performance measurement, and so on. However, this methodology for thorough research as both frameworks and criteria for decision-making may vary widely depending on the context.

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