Vendor selection problem: a multi-criteria approach

This article was downloaded by: [Abdul Zubar] On: 11 December 2012, At: 21:29 Publisher: Taylor & Francis Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK

International Journal of Production Research Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/tprs20

Vendor selection problem: a multi-criteria approach based on strategic decisions a

a

P. Parthiban , H. Abdul Zubar & Pravin Katakar

a

a

Department of Production Engineering, National Institute of Technology, Tiruchirappalli, Tamil Nadu, India Version of record first published: 21 Aug 2012.

To cite this article: P. Parthiban , H. Abdul Zubar & Pravin Katakar (2013): Vendor selection problem: a multi-criteria approach based on strategic decisions, International Journal of Production Research, 51:5, 1535-1548 To link to this article: http://dx.doi.org/10.1080/00207543.2012.709644

PLEASE SCROLL DOWN FOR ARTICLE Full terms and conditions of use: http://www.tandfonline.com/page/terms-and-conditions This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae, and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand, or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material.

International Journal of Production Research Vol. 51, No. 5, 1 March 2013, 1535–1548

Vendor selection problem: a multi-criteria approach based on strategic decisions P. Parthiban*, H. Abdul Zubar and Pravin Katakar Department of Production Engineering, National Institute of Technology, Tiruchirappalli, Tamil Nadu, India

Downloaded by [Abdul Zubar] at 21:29 11 December 2012

(Received 31 January 2012; final version received 3 July 2012) Many factors in the current scenario have influenced manufacturing organisations to have a competitive edge by concentrating on entire supply chains. Sourcing decisions are one of the strategic decisions because they enable companies to reduce costs and improve profit figures. The main task in sourcing is vendor selection. Recent challenges such as shortened product life cycle, just-in-time environment, and the importance of strategic partnerships in upstream chains always influence companies to prioritise vendor selection. In addition, outsourced parts and components account for a significant contribution in the cost of finished goods. Thus evaluating and selecting the right vendor is the key to business. Vendors are selected merely on the basis of cost factors in the traditional approach. However, companies eventually have understood that their approach which emphasises costs as the sole criterion is inefficient and needs to be changed. To deal with the complex process of vendor evaluation, multiple criteria decision-making techniques have evolved. This study presents the integrated approach of multiple multi criteria decision making (MCDM) techniques such as fuzzy logic, strength-weakness-opportunity-threat (SWOT) analysis, and data envelopment analysis. The efficacy of the proposed approach is evident from the case study of an automotive component manufacturer involving 20 vendors, comprising of pre-qualification by fuzzy SWOT and final selection by DEA. Keywords: supply chain management; MCDM; vendor selection; SWOT; data envelopment analysis

1. Introduction Many factors in the current scenario have influenced manufacturing organisations to have a competitive leading edge by concentrating on the entire supply chain. Sourcing decisions are one of the strategic decisions because they enable companies to reduce costs and ultimately, improve profit figures. The main tasks in sourcing are vendor evaluation and selection. For industries dealing with the hi-tech products, outsourced parts and components account for more than 80% of the cost of finished goods. The role of vendors and supply chain management is becoming more and more significant in the new manufacturing environment of just-in-time (JIT), total quality management (TQM), and lean manufacturing (Aissaoui et al. 2007). Thus evaluating and selecting the right vendor is the key to business. The traditional approach to vendor selection is selecting vendors merely on the basis of cost factors. However, companies eventually have understood that their approach which emphasises costs as the sole criterion is inefficient and needs to be changed. Then the multiple criteria techniques come into play. Recently, these multi criteria decision making (MCDM) approaches have also become complex as environmental, social, and uncontrollable factors have been added along with the traditional criteria such as cost, service, quality, delivery, and so on. For researchers, vendor evaluation and selection is a subject of interest (Tahriri et al. 2008). Chopra et al. (2007) define a supply chain as the inclusion of all stages which are directly or indirectly involved in satisfying the customers’ demands. The main stages in supply chains are suppliers, manufacturers, distributors/wholesalers, retailers, and customers. From the business view of a supply chain, different functions within the enterprise are involved, such as purchasing, marketing, finance, operations, and customer support. Supply chains are not a set of isolated entities, but the collaborative approach of all these entities involved from the transformation of raw materials up to the final delivery to end users. In most supply chains, there is more than one entity at any stage of the chain. The main goal of any supply chain is to maximise the supply chain surplus or profit. This includes the total profit shared across all stages of the supply chain. This implies that the success of the supply chain is measured in terms of the whole chain instead of by individual stages. *Corresponding author. Email: [email protected] ISSN 0020–7543 print/ISSN 1366–588X online ß 2013 Taylor & Francis http://dx.doi.org/10.1080/00207543.2012.709644 http://www.tandfonline.com

1536

P. Parthiban et al. Make/Buy Vendor Selection Contract Negotiation Design Collaboration Procurement Sourcing Analysis


Figure 1. Sourcing decision framework.

For enterprises to remain competitive, they should think about sourcing functions as the main process of upstream chains. Six major sourcing decision processes as identified from research studies are shown in Figure 1. In the make/buy process, companies develop strategies for outsourcing or in-sourcing. This means deciding whether parts or finished/semi-finished goods should be procured from vendors or produced in-house. This decision depends upon the capabilities and capacities of companies and on the policies to be followed. In the next stage of vendor selection, a pool of desired vendors is chosen for procurement according to a predefined set of criteria. A supply contract is then negotiated with vendors. A good contract accounts for supply chain performance and is designed for supply chain profit improvement. About 80% of the costs of a product are decided during the design phase, so design collaboration is crucial. It allows for collaborative efforts of vendors and manufacturers. Once the design of the product is ready, the procurement process is carried out, which starts with vendors sending products in response to manufacturers’ orders. Finally, the role of the sourcing analysis stage is to identify areas of improvement for reducing total costs. The majority of studies on outsourcing decisions focus on the processes: vendor selection, procurement, and sourcing analysis (Aissaoui et al. 2007). Vendor selection decisions affect various functional areas like the procurement of raw materials and components, inventory management, production planning, and control. Most problems of vendor selection frameworks in the literature include multiple phases. Aissaoui et al. (2007) indicate the distinct steps of the vendor selection process: first, a preparatory step is carried out by formulating the problem and the different decision criteria; after that is the prequalification of potential vendors, and final choices are successively elaborated. Chou and Chang (2008) identified four distinct phases in the purchasing and supply literature, namely, defining the problem, formulation of criteria, qualification, and final selection. In this research work, a structured framework for solving the vendor evaluation and selection problem comprising four distinct stages has been presented. The first stage is to identify the need for vendor selection. In order to make the right choice, the purchasing process should deal with finding exactly what they require by selecting a vendor (such as new product development, contracting, expansion). These situations vary according to each company. The second stage is to identify and finalise the evaluation criteria. As mentioned above, vendor selection is complicated as it deals with multiple criteria which constitute tangible and intangible criteria. Additionally, these criteria may be of conflicting nature. The most widely used criteria in vendor selection include quality, delivery, service, costs, and so on. But the decision of criteria varies from company to company depending on the objective and type of industry. The third stage in this process is pre-qualification. With the limited resources of a company, decision makers are required to pre-screen potential vendors to reduce their numbers before proceeding with a more detailed analysis and evaluation. The criteria formulated in previous stages plays a key role in this process. In this study, the fuzzy strengths-weaknesses-opportunities-threats (SWOT) technique is proposed for this process. In the final stage, with the limited number of vendors, final scores are generated. In this study, a data envelopment analysis (DEA) model is proposed to generate scores for each vendor.

2. Literature review In the current scenario of global competition, the emphasis on vendor selection is evident from the number of research studies. Improper vendor selection adversely affects the enterprise performance. Vendor selection is a


International Journal of Production Research

1537

strategic decision and also a multiple criteria decision-making process involving several tangible and intangible criteria. Dickson (1966) carried out a survey to identify the criteria considered in vendor evaluation. Out of 23 criteria finalised, he suggested that quality, delivery, and performance history are the three most significant criteria. Another study conducted by Weber et al. (1991) derived key factors affecting the vendor selection decisions. They reviewed 74 major articles that have appeared since Dickson’s study. Weber et al. (1991) summarised that price was a highly valued factor, followed by quality, delivery, performance history and so on. Later on Weber et al. (1998) suggested that the increasing importance of strategic vendor selection makes factors like geographical location, environmental effects to be included in evaluation. Ghodsypour and O’Brien (1998) stated that cost, quality, and service are significant factors while Humphreys et al. (2003) integrated environmental criteria into the vendor selection process. Ho et al. (2010) found that the most popular evaluation criteria are quality, delivery, price, manufacturing capability, service, management, technology, flexibility, and so on. Punniyamoorthy et al. (2011) used 10 main criteria in vendor evaluation, including, quality, technical capability, financial position, service, safety, and so on. Agarwal et al. (2011) reviewed 68 research articles which included the latest academic literature from 2000 to 2011. They found that DEA, mathematical models, analytic hierarchy process (AHP), linear programming, analytic network process (ANP), specific, measurable, attainable, relevant and timely (SMART), and so on are mostly favoured approaches. The study carried out by Ho et al. (2010) deals with the prevalence of different approaches and the inadequacies of them based on the articles from the year 2000 to 2008. This research provides evidence that multi-criteria decision-making approaches are better than traditional cost-based approaches. In individual approaches, DEA is mostly used (17.95%). Among integrated approaches AHP–DEA is famous. They cited that individual approaches are slightly more popular than the integrated approaches because of their simplicity. Ha and Krishnan (2008) proposed a hybrid model which uses both quantitative and qualitative factors. Multiple techniques are integrated to generate supplier maps and their scores. They integrated AHP, DEA, and neural networks in their model. From above, it is clear that the majority of the previous research on vendor selection does not reflect strategic perspective. Thus, Amin et al. (2011) proposed a method, composed of two parts. In the first phase, fuzzy SWOT is applied to evaluate the vendor. In the second phase, a fuzzy linear programming model is proposed to determine the order quantity for each vendor. The quantified SWOT analytical method adopts the concept of MCDM which uses a multi-stage process to simplify the complicated problems. Thus, it is able to perform SWOT analysis on several vendor enterprises simultaneously (Chang and Huang 2006). Lee and Walsh (2011) studied a sport marketing outsourcing process in which SWOT analysis was used. While SWOT analysis is a commonly-used business analysis tool, these factors are difficult to quantify. Therefore, to overcome this problem, the fuzzy set theory is combined with SWOT analysis. It is found that each organisation has to be aware of internal strengths and weaknesses along with external opportunities and threats which could impact their success or failure. DEA is commonly used to find the efficiency of decision-making units (DMUs). In a statistical approach, all DMUs are compared to the average performance value of DMUs. In contrast, the DEA technique compares each DMU with only the best DMU. Every DMU may have multiple inputs which produce a set of outputs. Each DMU has varying levels of inputs and outputs. Farrell (1957) suggested frontier analysis which forms the basis of DEA. Charnes et al. (1978) started with the basic DEA model which was modified by Banker et al. (1984). The basic DEA model considers constant returns to scale and is known as a CCR (Charnes, Cooper & Rhodes) model. The modified model proposed by Banker et al. is known as a BCC model and it considers variable returns to scale. Weber and Desai (1996) and Weber et al. (1998) have discussed the application of DEA in vendor selection problems in several studies. DEA is most widely used because of its robustness (Mohajeri and Amin 2010, Songhori et al. 2010, Agarwal et al. 2011). The majority of previous research on vendor selection doesn’t reflect strategic perspective. Thus, this study concentrates on developing an innovative model which will reflect the strategic perspective of vendor selection. Various vendor selection techniques have been developed but each existing technique reflects only current performance outcomes and fails to consider vendor capabilities which are the main influences on future performance. It is evident from the literature that individual approaches are slightly more popular than the integrated approaches because of their simplicity. But each individual approach has certain limitations related to efficacy and computation burden. Traditional approaches such as mathematical programming or linear weighted models (such as AHP) possess some weaknesses. For example, it is difficult to deal with qualitative factors using mathematical programming. The AHP process is mainly structured on human judgements. Thus, it has become more viable to use an integrated approach in the decision-making process to overcome these weaknesses. This

1538

P. Parthiban et al.

research takes advantage of multiple techniques like fuzzy logic, SWOT, and DEA. The integrated approach of fuzzy SWOT–DEA helps to deal with vagueness of data and compares the vendors to the best performing vendors. Thus it can handle imprecise input data and gives output as vendors’ efficiency scores. The proposed model of fuzzy SWOT–DEA is a unique approach in which the strategic perspective of vendor selection is integrated with the relative ranking of vendors based on a set of criteria. DEA is the most favoured MCDM technique because of its robustness whereas SWOT reflects the present performance as well as the future capabilities of vendors. Traditional approaches of vendor selection consider only costs as the deciding factor. But as the global environment expects changes with respect to market demands, it has become necessary to include other factors (like quality, delivery, and service) in vendor selection.


2.1 Problem definition In this study, the primary objective is to develop an integrated model using MCDM techniques for solving the vendor selection problem. For the case study problem applied to the automotive component manufacturer, it is required to evaluate a set of 20 potential vendors based on 10 criteria. The integrated methodology was proposed in Section 1, Vendor evaluation and selection decision, is discussed in the next chapter.

3. Integrated model 3.1 Criteria formulation According to previous research, it is evident that some vendor selection criteria vary in different applications, and experts have the same opinion that there is no one best approach in all applications. After reviewing the literature and taking expert opinions into account, 10 criteria have been chosen for this study. 3.1.1 Quality Performance of vendors is about more than just a low cost of purchase. High quality provided by vendors is the foremost need of the current market scenario. Research contributions in the field of vendor selection have argued that supplier quality is one of the most critical factors in vendor selection (Dickson 1966, Weber et al. 1991, Ghodsypour O’Brien 1998). 3.1.2 Delivery Supply of quality products, at the appropriate time, and in a sufficient quantity is considered as a key influencing factor in supplier selection. Thus delivery accuracy and delivery timeliness are the issues needing to be satisfied. Many previous researchers have contributed to the study of vendor selection with delivery as one of the important criteria (Dickson 1966, Weber et al. 1991, Narasimhan et al. 2001). 3.1.3 Productivity Productivity and flexibility are inter-related in various approaches. Numerous researchers have considered flexibility criteria in their works (Sarkis and Talluri 2002, Chan and Chan 2004, Narasimhan et al. 2006, Demirtas and Ustun 2008, Mendoza and Ventura 2008). Production facilities and capacities are vital because they identify the productivity, process flexibility, machine capacity, and capabilities, measurement, and testing facilities (Punniyamoorthy et al. 2011). 3.1.4 Service Suppliers always improve service to withstand current scenarios of strategic business with manufacturers. Thus, for better opportunities, vendors should identify service as a significant criterion. Service refers to the after sales service, spare parts availability, technical support level, sales representatives’ competence, accurate rate of processing order form, the rate of delivery in time, degree of information modernised, and service manner (Punniyamoorthy et al. 2011). Many researchers have emphasised service as an important factor in their research (Dickson 1966, Weber et al. 1991, Ghodsypour and O’Brien 1998).

International Journal of Production Research

1539

3.1.5 Costs Every buyer emphasises the various costs involved in purchasing products or materials from any vendor. The main constituents of the costs factor are product price, ordering costs, transportation costs, delivery and inspection costs, and so on. The traditional approach of vendor selection considers only costs as a deciding factor. But because of the global environment and dynamic customer demands it is highly recommended not to rely only on cost as the deciding factor for vendor selection. The other major factors like quality, delivery time and service also should be considered while selecting the vendor. It is clear from the literature that the cost factor is given high priority in vendor selection (Dickson 1966, Ho et al. 2010). Weber et al. (1991) reviewed vendor evaluation methods and they found that price was the most important factor, followed by delivery and quality.


3.1.6 Technological capabilities Within any manufacturing company, there is vital interdependence between technology, innovation, and other activities. In strategic planning, technology becomes a high priority. The degree of technological capabilities varies from company to company. This is because of the lack of skills and limited capabilities to analyse the information or inadequate information for their needs. Design capability, technology, and innovation, collaboration with research institutes, quick response capacity of product research and development, and so on come under technological capabilities (Punniyamoorthy et al. 2011). Numerous researchers have considered technology or technological capabilities as one of the important criteria in vendor selection. This is found in the literature with different industry applications such as information systems, quality, process control, finance and human resource (Chan 2003, Sarkar and Mohapatra 2006). 3.1.7 Application of conceptual manufacturing There is a conceptual difference between the application of conceptual manufacturing and the traditional manufacturing process. For instance, the traditional process is based on inventory where as conceptual manufacturing like JIT and lean defines it as a waste. These concepts like lean manufacturing, agile manufacturing, JIT, and TQM have changed people’s points of view. Conceptual manufacturing systematically generates potentially profitable alternatives based on the experimental and mathematical analysis to produce desired products from available resources. 3.1.8 Environment management Today many companies have recognised that proper environment management is an essential driving force for a dynamic manufacturing environment. Some of the driving forces are strong laws; better work environment for employees; customer emphasis; company image; and stakeholders’ pressure for social and environmental responsibilities. Thus it has become essential to evaluate vendor companies based on an environment management approach. The evaluation of environment management systems is based on the preventive and controlling measures taken for the unwanted effect on the environment through different processes within the company. 3.1.9 Human resource management Human resource management (HRM) is the legal link between the organisation and the employees. The main responsibility of HRM is to hire people with the knowledge to bring new technology into the manufacturing process. In the automotive market, there is always a need for highly skilled, flexible, and committed people. Thus, to be successful, they need flexible management, the ability to retain talent, and a strong link between management and labour unions. HRM helps to build commitment and loyalty among the workforce by use of recent technology, and to provide job security and a better working culture. 3.1.10 Manufacturing challenges The current scenario of global manufacturing constitutes five major dynamics responsible for reshaping global manufacturing. These are global value chains; technology exploitation; investment in intangibles such as research & development (R&D); design; branding; investment in people and skills; and movement towards a low-carbon economy. These challenges also provide opportunities to improve upon the current capabilities of companies. Thus it is the need of the future to evaluate vendors based on the manufacturing challenges they are facing and opportunities they are exploiting.

1540

P. Parthiban et al.

Figure 2. Triangular fuzzy number. Figure 3. Representation of linguistic scale.

Table 1. Details of linguistic scale.


Grade VL L ML M MH H VH

Table 2. SWOT criteria groups.

Description

Numeric scale

TFN (a1, a2, a3)

Very low Low Medium low Medium Medium high High Very high

1 2 3 4 5 6 7

(0, 0, 1) (0, 1, 3) (1.3.5) (3, 5, 7) (5, 7, 9) (7.9.10) (9, 10, 10)

Sr. No.

SWOT category

1 2 3 4 5 6 7 8 9 10

Strengths/ weaknesses assessment Opportunities/ threats assessment

Criteria Quality Delivery Productivity Costs Service Technology capability Environment management Human resource management Manufacturing challenges Application of conceptual manufacturing

3.2 Pre-qualification using fuzzy SWOT technique Today’s global competitive environment requires a low number of vendors because it is very difficult to manage a high number. Therefore, the purpose of this stage is to rule out the inefficient candidates and reduce the set of all vendors to a small range of acceptable ones. Amin et al. (2011) suggested that SWOT analysis can be used for evaluating vendors, but also it can be utilised for the pre-qualification of suitable vendors. From a SWOT matrix, managers of a company can choose a pool of vendors. In this study, we propose a fuzzy SWOT technique to deal with the imprecise data of evaluating decision makers. Fuzzy assessment expressed in linguistic terms is often intuitive and effective for DMs during the assessment process (Chou and Chang 2008). In this study, fuzzy opinions are represented as triangular fuzzy numbers (TFNs) because they are intuitive and easy to use. A TFN A~ can be defined as (a1, a2, a3) which is shown in Figure 2. Its membership function A~ ðxÞ is defined as below. 8 x a1 > , > > < a2 a1 A ðxÞ ¼ a3 x , > > > : a3 a2 0,

a1 x a2 a2 x a3 otherwise

The linguistic scale referred to by Amin et al. (2011) which is introduced in this study is shown in Figure 3 and its triangular fuzzy numbers with corresponding grades are shown in Table 1. The fuzzy SWOT analytical method consists of the following steps: Step 1: Decide what is to be compared. Here, 20 vendors. Step 2: Finalise the key criteria of a strength/weakness (S/W) assessment and an opportunity/threat (O/T) assessment. Table 2 shows the two groups formed for analysis. Step 3: Collect data of the objects to be compared. The input data for the 20 vendors is shown in Table 3. Step 4: Conduct questionnaire investigation to investigate the weights of key criteria using the linguistic scale. Table 4 summarises the inputs for criteria and their defuzzified and normalised weights. Based on the linguistic

1541

International Journal of Production Research Table 3. Vendors’ input data.


Vendors s1 s2 s3 s4 s5 s6 s7 s8 s9 s10 s11 s12 s13 s14 s15 s16 s17 s18 s19 s20

Quality

Delivery

Productivity

Costs

Service

App. of con. manu.

Tech. cap.

Envt. mgmt.

HRM

Manu. challenges

H H MH MH H H H MH H H MH MH MH MH MH H H H H H

H MH H MH H H H MH MH H H H H H MH H MH H MH MH

MH H MH MH MH MH MH MH MH H MH H H MH MH MH MH MH H H

MH H H H MH MH MH MH H MH H MH MH H MH MH H MH H MH

MH H H H H MH MH H MH H H MH H MH MH H H MH H H

L L ML M M L M L ML ML L M L ML ML ML M L ML M

MH MH H M M MH M M MH M M H MH H M M H MH M M

MH M H MH M MH H M H H MH MH M H H MH M H M H

MH H H MH H H H H MH H MH H MH H MH H MH H H MH

H MH MH H MH MH MH H MH H MH MH H MH MH H MH H MH H

Table 4. Weights of criteria.

Strength/weakness criteria

Opportunity/threat criteria

Criteria

Importance grade

Defuzzified weight

Normalised weight (0-1)

Quality Delivery Productivity Costs Service Environment management Human resource management Application of conceptual manufacturing Technology capability Manufacturing challenges

VH H MH MH H M MH ML MH MH

9.666666667 8.666666667 7 7 8.666666667 5 7 3 7 7

0.235772358 0.211382114 0.170731707 0.170731707 0.211382114 0.172413793 0.24137931 0.103448276 0.24137931 0.24137931

scale, the defuzzified scores are calculated by a centroid formula: Defuzzified weight ¼

a1 þ a2 þ a3 3

For example, consider quality criteria having a VH grade. Hence the defuzzified weight for quality: (9 þ 9 þ 10)/ 3 ¼ 9.6667. Similarly, for all criteria and vendor data, defuzzified weights are calculated and are shown in Table 4 and Table 5, respectively. Step 5: Normalise the performance for each strength/weakness assessment and opportunity/threat assessment. The normalised weights for the strength/weakness and opportunity/threat assessments are calculated using the formula (maximum is better): Normalized weight ¼

DFWi Max DFWi

For example, in Table 5, the normalised weight for vendor s1 with costs defuzzified weight ¼ 7, is (7/8.6667) ¼ 0.80769. Similarly, the normalised weights for opportunity/threat assessment can be calculated.

1542

P. Parthiban et al.

Table 5. Normalisation of strength/weakness assessment. Quality

Delivery

0.235772358


Vendors s1 s2 s3 s4 s5 s6 s7 s8 s9 s10 s11 s12 s13 s14 s15 s16 s17 s18 s19 s20

0.211382114

Productivity 0.170731707

Costs

Service

0.170731707

0.211382114

Defuz. Wt.

Nor. scores

Defuz. Wt.

Nor. scores

Defuz. Wt.

Nor. scores

Defuz. Wt.

Nor. scores

Defuz. Wt.

Nor. scores

8.66667 8.66667 7 7 8.66667 8.66667 8.66667 7 8.66667 8.66667 7 7 7 7 7 8.66667 8.66667 8.66667 8.66667 8.66667

1 1 0.80769 0.80769 1 1 1 0.80769 1 1 0.80769 0.80769 0.80769 0.80769 0.80769 1 1 1 1 1

8.66667 7 8.66667 7 8.66667 8.66667 8.66667 7 7 8.66667 8.6667 8.6667 8.6667 8.6667 7 8.6667 7 8.6667 7 7

1 0.80769 1 0.80769 1 1 1 0.80769 0.80769 1 1 1 1 1 0.80769 1 0.80769 1 0.80769 0.80769

7 8.6667 7 7 7 7 7 7 7 8.6667 7 8.6667 8.6667 7 7 7 7 7 8.6667 8.6667

0.80769 1 0.80769 0.80769 0.80769 0.80769 0.80769 0.80769 0.80769 1 0.80769 1 1 0.80769 0.80769 0.80769 0.80769 0.80769 1 1

7 8.6667 8.6667 8.6667 7 7 7 7 8.6667 7 8.6667 7 7 8.6667 7 7 8.6667 7 8.6667 7

0.80769 1 1 1 0.80769 0.80769 0.80769 0.80769 1 0.80769 1 0.80769 0.80769 1 0.80769 0.80769 1 0.80769 1 0.80769

7 8.6667 8.6667 8.6667 8.6667 7 7 8.6667 7 8.6667 8.6667 7 8.6667 7 7 8.6667 8.6667 7 8.6667 8.6667

0.80769 1 1 1 1 0.80769 0.80769 1 0.80769 1 1 0.80769 1 0.80769 0.80769 1 1 0.80769 1 1

Step 6: Calculate the S/W and O/T total weighted score of the comparing criteria separately (normalisation of performance weights) and determine the benchmarking value. It is suggested in this research that the determination of the benchmarking value is carried out by taking the mean as the benchmarking value. Now, using these normalised weights, the total weighted value for strength/weakness and opportunity/threat assessments are tabulated in Table 6. The total weighted value is calculated by multiplying the weights of criteria with normalised scores. For example, Total Weighted Value ðTWVÞ S=W assessment for vendor s1 ¼ ½0:23578 1 þ 0:21138 1 þ 0:17073 0:80769 þ 0:17073 0:80769 þ 0:21138 0:80769 ¼ 0:893681022

Step 7: Calculate and compare the coordinate values of the strength/weakness spectrum and the opportunity/ threat spectrum and then plot them on the four-quadrant coordinates. Firstly, the strength/weakness and opportunity/threat scores of the compared enterprises should be added together and then the benchmarking value subtracted. The final value will be the coordinate value of the compared enterprise in the SWOT analysis matrix. The coordinate value will be within 1 to þ1. SWCj : coordinate for X axis SWCj ¼ SWj SWB, j ¼ 1, 2, . . . , n; OTCj ¼ OTj OTB, j ¼ 1, 2, . . . , n; OTCj : coordinate for Y axis 1 SWCj þ1 1 OTCj þ1 Where, SWCj

X-coordinate value for the strength/weakness assessment of the jth vendor.

1543

International Journal of Production Research Table 6. Final coordinates for SWOT matrix.


Vendors [1] s1 s2 s3 s4 s5 s6 s7 s8 s9 s10 s11 s12 s13 s14 s15 s16 s17 s18 s19 s20

TWV: S/W assessment [2] 0.893681022 0.959348937 0.921824798 0.881174548 0.934332085 0.893681022 0.893681022 0.848340997 0.885864323 0.967165636 0.921825611 0.881174548 0.921825611 0.881174548 0.807689934 0.934332898 0.926515386 0.893681835 0.959348937 0.926515386

SWj SWB OTCj OTj OTB

Benchmarking value: S/W assessment [3] 0.906658954

TWV: O/T assessment [4] 0.798140511 0.758352866 0.912200842 0.818300566 0.778512921 0.798140511 0.8514576 0.74907001 0.819361146 0.856498138 0.69601796 0.920423118 0.758352866 0.912200842 0.763658442 0.823341104 0.834215624 0.877717393 0.73713361 0.8514576

Benchmarking value: O/T assessment [5] 0.815727684

Coordinates for S/W assessment [6] ¼ [2]–[3]

Coordinates for O/T assessment [7] ¼ [4]–[5]

0.012977932 0.052689983 0.015165844 0.025484406 0.027673131 0.012977932 0.012977932 0.058317957 0.020794631 0.060506682 0.015166657 0.025484406 0.015166657 0.025484406 0.09896902 0.027673944 0.019856432 0.012977119 0.052689983 0.019856432

0.017587172 0.057374818 0.096473158 0.002572883 0.037214763 0.017587172 0.035729917 0.066657673 0.003633462 0.040770455 0.119709724 0.104695434 0.057374818 0.096473158 0.052069241 0.00761342 0.018487941 0.06198971 0.078594073 0.035729917

Score for the strength/weakness assessment of the jth vendor. Benchmarking value of the strength/weakness assessment. Y-coordinate value for the opportunity/threat assessment of the jth vendor. Score for the opportunity/threat assessment of the jth vendor. Benchmarking value of the opportunity/threat assessment.

In order to show the comparison on the four-quadrant coordinates, the ordinate is prescribed to stand for the opportunities and threats (OT) while the abscissa is prescribed to stand for the strengths and weaknesses (SW). Now, each vendor will have coordinates (X, Y), so its position in the competition can be clearly realised.

3.3 Final selection using DEA This is the final stage of vendor evaluation and selection. The output of the pre-qualification stage is the list of shortlisted candidates after removing the inefficient vendors. Using these vendors, final efficiency scores are evaluated using the data envelopment analysis technique. The efficiency of a DMU is defined as the ratio of the weighted sum of its outputs (that is to say performance) to the weighted sum of its inputs (that is to say resources utilised). For each DMU, DEA finds the most favourable set of weights, that is to say the set of weights that maximises the DMU efficiency rating without making its own or any other DMUs rating 41. We assume the constant returns to scale (CRS) in the model. DMUs are able to linearly scale the inputs and outputs without increasing or decreasing efficiency. The DEA model proposed for this study is composed of two outputs, namely the strength/weakness total weighted value (SWj) and the opportunity/ threat total weighted value (OTj) which are obtained from a fuzzy SWOT analysis. To complete the DEA model shown in Table 7, a dummy input is considered whose value is equal to 1 for each DMU (Mohajeri and Amin 2010).

1544

P. Parthiban et al.

Table 7. DEA model.

DMUs

Output1 Strength/ weakness total weighted value (SWj)

Output2 Opportunity/ threat total weighted value (OTj)

Input Dummy input

DMU1 DMU2 DMU3 : : : DMUn

W11 W21 W31 : : : Wn1

W12 W22 W32 : : : Wn2

1 1 1 : : : 1


Figure 4. Output of fuzzy SWOT analysis.

The mathematical model for the above scenario is (DEA model I): Zk ¼ MAX

m X

Wkj Uj

j¼1

subject to m P

Wij Uj 1,

i ¼ 1, 2, . . . , n

j¼1

j ¼ 1, 2

Uj ", where Zk

! Efficiency score of kth potential supplier ðk ¼ 1, 2, . . . , nÞ " ! a non-Archimedean

To find the value of ", we have to solve the following model (DEA model II): "max ¼ MAX " subject to m P Wij Uj 1, i ¼ 1, 2, . . . , n j¼1

Uj " 0,

j ¼ 1, 2

Using the optimal value "max in DEA model I, the final efficiency scores for all vendors can be found.

4. Case study results 4.1 Pre-qualification: fuzzy SWOT technique The output of fuzzy SWOT analysis is the plot of vendors on the SWOT matrix as shown in Figure 4. The position of each vendor on the SWOT matrix reflects its competitive position and helps decision makers chose a pool of vendors for final selection. The vendors s3, s10, s16, s17, and s20 are in the first quadrant. They have opportunities for development and also possess competing strengths. Thus they are in the best position for facing competition. Strategic partnership with these vendors is suggested for long-term benefits. Vendors s1, s6, s8, and s15 are present in third quadrant. Due to challenges from s3, s10, s16, s17, and s20, they face threats. Vendors s4, s7, s9, s12, s14, and s18 are in the second quadrant. They should concentrate on market penetration and liquidation strategies to sustain S/W capabilities. For vendors s2, s5, s11, s13, and s19 laying in the fourth quadrant, diversification strategies will help to improve their

1545

International Journal of Production Research Table 8. Data of DEA model for case study.

DMUs


s2 s3 s5 s7 s10 s12 s14 s16 s17 s18 s20

Output1 Strength/weakness total weighted value (SWj)

Output2 Opportunity/threat total weighted value (OTj)

Input Dummy input

0.95934894 0.9218248 0.93433208 0.89368102 0.96716564 0.88117455 0.88117455 0.9343329 0.92651539 0.89368183 0.92651539

0.75835287 0.91220084 0.77851292 0.8514576 0.85649814 0.92042312 0.91220084 0.8233411 0.83421562 0.87771739 0.8514576

1 1 1 1 1 1 1 1 1 1 1

scores. Joint ventures with other strong competitors will help them to improve. After discussion and brainstorming sessions, the final list of vendors selected for further processing is: s3, s10, s16, s17, and s20 (first quadrant); s12, s14, s18, and s7 (second quadrant); and s5 and s2 (fourth quadrant).

4.2 Final selection Output of pre-qualification is given to the final selection using the DEA model. As discussed above, the two outputs and a dummy input are provided in Table 8. The following equations are used to calculate Output1 and Output2 in Table 8. X SWj ¼ SW Criteria Weight Normalized Score For Vendor X OTj ¼ OT Criteria Weight Normalized Score For Vendor The mathematical formation to calculate efficiency of each DMU (vendor) for the above DEA model is defined below. For calculating efficiency of s2, the following linear programming problem is formulated. Z2 ¼ MAX ð0:95934894 U1 þ 0:75835287 U2 Þ subject to 0:95934894 U1 þ 0:75835287 U2 1; 0:92182480 U1 þ 0:91220084 U2 1; 0:93433208 U1 þ 0:77851292 U2 1; 0:89368102 U1 þ 0:85145760 U2 1; 0:96716564 U1 þ 0:85649814 U2 1; 0:88117455 U1 þ 0:92042312 U2 1; 0:88117455 U1 þ 0:91220084 U2 1; 0:93433290 U1 þ 0:82334110 U2 1; 0:92651539 U1 þ 0:83421562 U2 1; 0:89368183 U1 þ 0:87771739 U2 1; 0:92651539 U1 þ 0:85145760 U2 1; U1 , U2 "; Similarly, by changing the objective functions for each vendor, efficiency can be calculated. Professional tools such as LINGO can be used for solving the LP problem formulated above. Final efficiency scores for vendors are shown in Table 9.

1546

P. Parthiban et al.

Table 9. Vendors’ efficiency and ranking. Vendor Efficiency Rank

s2

s3

s5

s7

s10

s12

s14

s16

s17

s18

s20

0.992 5

1 2

0.966 9

0.953 11

1 1

1 3

0.993 4

0.966 7

0.965 10

0.966 8

0.973 6

Table 10. Sensitivity analysis.


Sr. no. 1 2 3 4 5 6 7 8 9 10 11

Vendors

Efficiency

Peer vendors

s2 s3 s5 s7 s10 s12 s14 s16 s17 s18 s20

0.992 1 0.966 0.953 1 1 0.993 0.966 0.965 0.966 0.973

s10 s3 s10 s3, s10 s10 s12 s3, s12 s10 s10, s3 s3, s10 s10, s3

Peer weights 0.992 1 0.966 0.626, 1 1 0.162, 0.966 0.822, 0.900, 0.650,

0.327 0.830 0.143 0.066 0.323

Figure 5. SWOT matrix for scenario.

4.3 Sensitivity analysis It is expected that the robustness of the DEA results be tested using some form of sensitivity analysis. According to the DEA technique, it is possible for a vendor to become efficient if it achieves exceptionally better results in terms of one output but performs below average in terms of another output. An easy way to test these efficient units is by identifying the peers for inefficient units. If the unit is genuinely efficient, it is expected that there are some inefficient units in its vicinity, so that it is considered a peer for these inefficient units. Thus, the analysis of peers is important sensitivity information for the results of DEA analysis (Ramanathan 2003). Such peer analysis has been carried out for the present study. As indicated in Table 10, s3 formed a peer for five inefficient vendors (s7, s14, s17, s18, and s20). Similarly s10 formed a peer for seven inefficient vendors (s2, s5, s7, s16, s17, s18, and s20), and s12 formed a peer for one inefficient vendor (s14). Thus it is clear from the above analysis that the model developed is robust and stable.

4.4 Scenario analysis The proposed methodology is helpful for decision makers in the development of strategies and also reflects vendors’ competitive positions. As seen from the SWOT matrix, the vendor s1 is laying in the third quadrant. So it is eliminated from further process. Suppose, the management of vendor s1 revised its policy and paid more attention to the criteria productivity and application of conceptual manufacturing as critical factors. We want to examine the effect of this decision and analyse the changes for it. The decision makers now decide new grades to these criteria for vendor s1. The productivity grade improved from MH to H while the application of conceptual manufacturing grade improved from L to M. The output of prequalification is shown in Figure 5 while final ranking for the modified scenario is shown in Table 11. Therefore, the vendor s1 possesses the potential to improve in the near future and creates a threat for other vendors. Thus it is clear from the above scenario analysis that the proposed methodology is helpful to each and every vendor to improve in a dynamic competitive market.

1547

International Journal of Production Research Table 11. Vendors’ ranking for scenario. Vendor Efficiency Rank

s1

s3

s5

s7

s10

s12

s14

s16

s17

s18

s20

0.984 5

1 2

0.966 9

0.953 11

1 1

1 3

0.993 4

0.966 7

0.965 10

0.966 8

0.973 6


5. Conclusions There is a need to evaluate the vendors based on the strategic perspective of business. The implication of manufacturing concepts such as lean manufacturing and the just-in-time approach always forces decision makers to shift the focus from a traditional approach to a quality- and efficiency-based approach. The single criterion approaches of costs concentric techniques do not reflect the performance of the vendors effectively. Thus, the other criteria such as quality, delivery, and service dominate the costs criteria during the evaluation process. Using the proposed methodology involving fuzzy SWOT and DEA, the vendors can be ranked and evaluated to make an optimal vendor selection. Particularly, inclusion of the SWOT analysis provides the opportunity to develop a policy of strategic partnership with top performing vendors. In addition, it gives a clear idea about the competitive position of all vendors. The efficacy of the proposed methodology is evident from the case study results. It is applied to an automotive components manufacturing company having 20 potential vendors for the pre-qualification stage. Vendors selected for final selection were s3, s10, s16, s17, s20, s12, s14, s18, s7, s5, and s2. With these 11 vendors, final efficiency scores are generated by using a DEA model. From the results of the final selection, it is clear that vendors s3, s10, and s12 are efficient vendors and are peers (role models) for other inefficient vendors. The sensitivity analysis of the results confirms the robustness and stability of the proposed model.

References Agarwal, P., et al., 2011. A review of multi-criteria decision making techniques for supplier evaluation and selection. International Journal of Industrial Engineering Computations, 2, 801–810. Aissaoui, N., Haouari, M., and Hassini, E., 2007. Supplier selection and order lot sizing modeling: a review. Computers & Operations Research, 34 (12), 3516–3540. Amin, S.H., Razmi, J., and Zhang, G., 2011. Supplier selection and order allocation based on fuzzy SWOT analysis and fuzzy linear programming. Expert Systems with Applications, 38 (1), 334–342. Banker, R.D., Charnes, A., and Cooper, W.W., 1984. Some models for estimating technical and scale inefficiencies in data envelopment analysis. A Journal of the Institute for Operations Research and Management Sciences, 30 (9), 1078–1092. Chan, F.T.S., 2003. Interactive selection model for supplier selection process: an analytical hierarchy process approach. International Journal of Production Research, 41 (15), 3549–3579. Chan, F.T.S. and Chan, H.K., 2004. Development of the supplier selection model – A case study in the advanced technology industry. Proceedings of the Institution of Mechanical Engineers Part B – Journal of Engineering Manufacture, 218 (12), 1807–1824. Chang, H.H. and Huang, W.C., 2006. Application of a quantification SWOT analytical method. Mathematical and Computer Modelling, 43 (1–2), 158–169. Charnes, A., Cooper, W.W., and Rhodes, E., 1978. Measuring the efficiency of decision making units. European Journal of Operation Research, 2, 429–444. Chopra, S., Meindl, P., and Kalra, D.V., 2007. Supply chain management: strategy, planning, and operation. 3rd ed. Saddle River, New Jersey: Prentice Hall. Chou, S.Y. and Chang, Y.H., 2008. A decision support system for supplier selection based on a strategy-aligned fuzzy SMART approach. Expert Systems with Applications, 34 (4), 2241–2253. Demirtas, E.A. and Ustun, O., 2008. An integrated multiobjective decision making process for supplier selection and order allocation. Omega, 36 (1), 76–90. Dickson, G.W., 1966. An analysis of vendor selection systems and decisions. Journal of Purchasing, 2, 5–17. Farrell, M.J., 1957. The measurement of productive efficiency. Journal of Royal Statistical Society (A), 120 (3), 253–281. Ghodsypour, S.H. and O’Brien, C., 1998. A decision support system for supplier selection using an integrated analytic hierarchy process and linear programming. International Journal of Production Economics, 56–57 (20), 199–212. Ha, S.H. and Krishnan, R., 2008. A hybrid approach to supplier selection for the maintenance of a competitive supply chain. Expert Systems with Applications, 34 (2), 1303–1311.


1548

P. Parthiban et al.

Ho, W., Xu, X., and Dey, P.K., 2010. Multi-criteria decision making approaches for supplier evaluation and selection: a literature review. European Journal of Operational Research, 202, 16–24. Humphreys, P.K., Wong, Y.K., and Chan, F.T.S., 2003. Integrating environmental criteria into the supplier selection process. Journal of Materials Processing Technology, 138 (1–3), 349–356. Lee, S. and Walsh, P., 2011. SWOT and AHP hybrid model for sport marketing outsourcing using a case of intercollegiate sport. Sport Management Review, 14 (4), 361–369. Mendoza, A. and Ventura, J.A., 2008. An effective method to supplier selection and order quantity allocation. International Journal of Business and Systems Research, 2 (1), 1–15. Mohajeri, N. and Amin, G.R., 2010. Railway station site selection using analytical hierarchy process and data envelopment analysis. Computers & Industrial Engineering, 59 (1), 107–114. Narasimhan, R., Talluri, S., and Mahapatra, S.K., 2006. Multiproduct, multi-criteria model for supplier selection with product life-cycle considerations. Decision Sciences, 37 (4), 577–603. Narasimhan, R., Talluri, S., and Mendez, D., 2001. Supplier evaluation and rationalization via data envelopment analysis: an empirical examination. Journal of Supply Chain Management, 37 (3), 28–37. Punniyamoorthy, M., Mathiyalagan, P., and Parthiban, P., 2011. A strategic model using structural equation modeling and fuzzy logic in supplier selection. Expert Systems with Applications, 38 (1), 458–474. Ramanathan, R., 2003. An introduction to data envelopment analysis: a tool for performance measurement. Delhi: Sage Publications. Sarkar, A. and Mohapatra, P.K.J., 2006. Evaluation of supplier capability and performance: a method for supply base reduction. Journal of Purchasing and Supply Management, 12 (3), 148–163. Sarkis, J. and Talluri, S., 2002. A model for strategic supplier selection. Journal of Supply Chain Management, 38 (1), 18–28. Songhori, M.J., et al., 2010. A supplier selection and order allocation model with multiple transportation alternatives. International Journal of Advanced Manufacturing Technology, 52 (1–4), 365–376. Tahriri, F., et al., 2008. A review of supplier selection methods in manufacturing industries. Suranaree Journal of Science & Technology, 15 (3), 201–208. Weber, C.A., Current, J.R., and Benton, W.C., 1991. Vendor selection criteria and methods. European Journal of Operational Research, 50 (1), 2–18. Weber, C.A., Current, J.R., and Desai, A., 1998. Non-cooperative negotiation strategies for vendor Selection. European Journal of Operational Research, 108 (1), 208–223. Weber, C.A. and Desai, A., 1996. Determination of paths to vendor market efficiency using parallel coordinates representation: a negotiation tool for buyers. European Journal of Operational Research, 90 (1), 142–155.