The Supplier Selection Model Using Dba Commerce Essay

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The complexity of managing an international operating environment has changed significantly over the last few years. The increasing presence of Multinational Corporations in emerging markets has not only increased the advantages of developing global economies of scale, but it has also identified a need for the further coordination and assimilation of company activities into the local market of operation. As more and more MNCs begin entering the Indian market in an effort to establish long-term competitive advantages and low cost production sites, there becomes an even greater need for a developed business infrastructure, globally integrated technology processes, greater overall efficiency, increased coordination of procedures and synchronized learning efforts. The complexity of managing an international operating environment has changed significantly over the last few years. In fact, selection problem is more crucial for the automobile industry to select best vendor, as it a multi-criteria decision problem. Among the available literature, the DBA has been recognised as an appropriate tool to tackle this problem. In this paper, a case study is reported to illustrate an innovative model for cars. The proposed model can provide not only a framework for the organisation to select best vendor, but also capability to deploy the company strategy to suppliers. Added to this, it also has the flexibility to select and modify any number of vendors, criteria and sub -criteria to responds to the changing needs of the industry due to dynamic business environment. However, no methodology has been evolved for cars automobiles till date for structural modelling and analyse using DBA wherein all criteria, sub-criteria and their relative importance are considered; the need arises and is proposed using Graph Theoretical Methodology.. Added to this, it also has flexibility to respond to the changing needs of the organisation due to dynamic business environment. Through an illustration of the proposed model, it is found that vendor selection problem can be solved in a structural and timely manner.

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INTRODUCTION

The automotive industry in India is one of the largest in the world and one of the fastest growing globally. India's passenger car and commercial vehicle manufacturing industry is these seventh largest in the world, with an annual production of more than 3.7 million units in 2010. According to recent reports, India is set to overtake Brazil to become the sixth largest passenger vehicle producer in the world, growing 16-18 per cent to sell around three million units in the course of 2011-12. In 2009, India emerged as Asia's fourth largest exporter of passenger cars, behind Japan, South Korea, and Thailand. In 2010, India reached as Asia's third largest exporter of passenger car, behind Japan and South Korea beating Thailand.

As of 2010, India is home to 40 million passenger vehicles. More than 3.7 million automotive vehicles were produced in India in 2010 (an increase of 33.9%), making the country the second fastest growing automobile market in the world. According to the Society of Indian Automobile Manufacturers, annual vehicle sales are projected to increase to 5 million by 2015 and more than 9 million by 2020. By 2050, the country is expected to top the world in car volumes with approximately 611 million vehicles on the nation's roads.

With the globalization of economic markets and the development of information technology, a well-designed and implemented supply chain management (SCM) system is now regarded as an important tool to increase competitive advantage, Choi et al. (2007); Li et al. (2007). In upstream echelons of supply chain, vendor selection or vendor evaluation continues to be a key element in the industrial purchasing process, and appears to be one of the major activities of the professionals.

In today's highly competitive environment, the proper evaluation and selection of vendors is very crucial for the progress and development of every manufacturing industry. With the emphasis on quality improvement concepts and wide use of enterprise systems, the managers try to go beyond the conventional boundaries of money and material and try to explore the vast new universe of possibilities. The concept of just-in-time (JIT) manufacturing has put additional pressure on the vendors emphasizing on delivery performance, quality and strategic integration between the two firms. Vendor selection involves various criteria including delivery performance, cost, quality, flexibility, service, etc. and often involves the selection of one while sacrificing the other. Consider a situation in which one vendor is providing goods in cheap rates but is not able to deliver on time. On the other hand, another vendor is providing the best quality goods but delivery performance and cost are not acceptable.

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Vendor selection is an integral component of relationship success and research has been done to find the critical success factors and vendor selection criteria. Vendor selection decisions become more complicated, if required, to consider various mutually dependent and conflicting selection criteria in the decision-making process. Dickson made a pioneering effort in this regard. Dickson (1966) in his study surveyed buyers in order to identify factors they considered in awarding contracts to suppliers. Out of the 23 factors considered, he concluded that quality, delivery, and performance history are the three most important criteria. The seven most mentioned criteria in the Dickson's survey were quality, delivery, performance history, warrant and claim policy, production facilities and capacity, net price, and technical capabilities.

The vendor selection process would be simple if only one criterion is used in the decision making process, however, in many situations, purchasers have to consider a number of criteria in decision making. Most of criteria are conflicting and complimentary, tangible and intangible, qualitative and quantitative, vague and precise to each another, and then we have to make decision on the basis of multi criteria. Such methodologies are nothing but are called MCDM methodologies. In such cases, it becomes necessary to determine how far each criterion influences the decision making process, whether all are to be equally weighted or whether the influence varies accordingly to the type of criteria (Yahay and Kingsman, 1999). Therefore, vendor selection belongs to the class of multi-criteria decision making (MCDM) problem in which the firms need to identify the top priorities of selecting the best vendor based on its working style and the industry type. As a multiple criteria decision - making (MCDM) problem is affected by several conflicting factors in supplying selection, a purchasing manager must analyze the trade off among the several criteria. MCDM techniques support the decision-makers (DMs) in evaluating a set of alternatives. Depending upon the purchasing situations, criteria have varying importance and there is a need to weigh criteria (Dulmin and Mininno, 2003). In fact, it is almost impossible to find a supplier that excel in all areas .In addition, some of criteria are qualitative while other are quantitative, which is certainly a weakness of existing reported approaches. Thus a methodology that can capture both qualitative and quantitative i.e. subjective and objective evaluation measures is needed. In the context of this paper, distance based approximation (DBA) methodology is adopted to develop a vendor selection model that can synchronize with corporate strategy and fulfil both the operational and the quality management system requirements. This paper is divided into six sections. Section 1 is a general introduction to the concept of vendor selection and its relevance to current developments in the analysis, selection and finally ranking of vendor in the particular automobile industry. In the section 2, an extensive background literature review has been presented. In the Section 3, highlights the methodology adopted in this work. A framework together with an illustration has been presented in section 4. The results are discussed in section 5, sensitivity analysis is discussed in section 6, and in the section 7 conclusions cum recommendation for future research have been presented.

2. LITERATURE REVIEW

2.1 Strategic Purchasing and Vendor Selection:

The 1980's were a period of shifting attitudes towards the role of the purchasing function in corporate strategy. The literature in this area focuses on the strategies that the purchasing function employs in its operation as well as the impact of these strategies on other functional areas. Porter (1975) in his seminal decisive work identified buyers and vendors as two out of five critical forces that shape the competitive nature of an industry.

Kiser (1976) presented a conceptual framework to support his belief that industrial purchasing strategy is influenced by two strategic choices. The first choice focuses on the decision regarding the vendor markets to enter when making a purchase decision. The second choice deals with the product or component level at which the firm elects to buy an item rather than to make it themselves. Buffa(1984) proposed four basic competitive elements of strategy: cost, quality, dependability, and flexibility in order to define the link between corporate and functional strategies. Functional managers translate these strategic concepts into tangible tasks.

Ellram and Carr (1994) summarized research work pertaining to the role of the purchasing function in supporting a firm's strategy. The work explores the broad issue of communicating and integrating purchasing strategy into corporate strategy. They also examined, how purchasing strategies and activities can support or detract from the strategies of the firm.

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Verma and Pullman (1998) ranked the importance of the vendor selection criteria of quality, on-time delivery, cost, lead-time and flexibility. Vonderembse and Tracey (1999) described that supplier and manufacturing performance were determined by supplier selection criteria and supplier involvement. It was concluded:

The supplier selection criteria could be evaluated by quality, availability, reliability and performance.

Supplier involvement could be evaluated by product R&D and improvement.

Supplier performance could be evaluated by stoppage, delivery damage and quality.

Manufacturing performance could be evaluated by cost, quality, inventory and delivery.

De Boer et al. (2001) covered all phases in the selection process from initial problem definition, formulation of criteria, pre-qualification of potential vendors, to the final choosing of the best vendors. They developed a prescriptive framework for classifying the available models in the literature. The diversity of the buying situation in terms of complexity and importance of the purchasing practice, including new task purchase, modified re-buy, and straight re-buy, are shown on one axis of the framework. The other axis covers the four different stages in the vendor evaluation process: (1) problem definition, (2) criteria formulation, (3) supplier qualification and (4) final selection. Most the decision making methods are applied to the qualification and final selection stage of the buying process, namely supplier selection. Thorough supplier selection is essential to partnership strategy. Early in the supplier development, the market is scanned for the alternative suppliers. These suppliers are screened based on their R&D and engineering capabilities, financial stability, production capacity, present R&D potential capability, and the quality of their logistics and quality systems.

Hong et al. (2005) defined important criteria of both supply risk and supply profit. In terms of supply risk, they defined the criteria which can be used to evaluate whether or not a vendor is capable of delivering the desired product, in the desired quantity, and at the desired time. They also framed the criteria that can be used to evaluate profit as price, quality and quantity. Chen (2005) divided the vendor selection into two stages: first stage considers environmental performance as the minimum requirement; and second stage includes general purchase practices such as quality, delivery, performance records etc. Xia and Wu (2007) developed the vendor selection hierarchy based on prices, quality and service under volume discount environment. Chan and Kumar (2007) implemented a fuzzy extended method using critical decision criteria to determine critical factors such as the risk factors, cost, quality, service performance for selecting efficient global vendor in present business scenario.

Ã-zgen et al. (2008) demonstrated a multi-objective probabilistic linear programming (MOPLP) technique integrating with the analytic hierarchy process and a multi-objective possibilistic linear programming (MOPLP) technique. It is developed to account for all tangible and intangible factors which are used to evaluate and select the vendors and define the optimum order quantities assigned to each vendor.

Kokangul and Susuz (2009) proposed an integral model of AHP and non linear integer program to determine the best vendor and optimal order quantity in quantity discount factor and minimizing the total cost of purchasing. Lee (2009) advised to have an analytical approach in selecting vendors under a fuzzy environment. Fuzzy analytic hierarchy process (FAHP) model incorporates the benefits, opportunities, costs and risks (BOCR) concept and is constructed to evaluate various aspects of vendors. Multiple factors that are positively or negatively affecting the success of the relationship are analyzed by taking into account experts' opinion on their importance, and by obtaining a performance ranking of the vendors.

Liao and Kao (2010) proposed integration of Taguchi loss function, analytical hierarchy process and multi-choice goal programming (MCGP) model for solving the vendor selection problem. The advantage of this proposed method is that it allows decision makers to set multiple aspiration levels for the decision criteria.

Chamodrakas et al. (2010) introduced a fresh approach for decision support enabling effective vendor selection processes in electronic marketplaces. It comprises of an evaluation method in two stages: initial screening of the vendor through the enforcement of hard constraints on the selection criteria and final vendor evaluation through the application of a modified variant of the fuzzy preference programming (FPP) method. Lam et al. (2010) proposed fuzzy principal component analysis (FPCA) method. This is a method for investigating a selection model for solving the material vendor selection problem from the perspective of property developers.

Kuo et al. (2010) has developed ANN-MADA model which is yet another study that intended to develop a green vendor selection model. This study integrates artificial neural network (ANN), and two multi-attribute decision analysis (MADA) methods - (i) data envelopment analysis (DEA) and (ii) analytic network process (ANP). Thus, it is called ANN- MADA hybrid method which considers both practicality in traditional vendor selection criteria and environmental regulations. It also overcomes traditional DEA drawbacks, limitations of data accuracy and decision making units (DMUs) constraints.

The above summary of existing approaches to vendor selection shows that it aims to fulfil a combination of qualitative and quantitative objectives. Therefore no straightforward methodology can be applied to solve vendor selection especially when different organisations have different qualitative requirements. Based on above reviews, it would not irrational to suggest that the vendor selection issues need further attention in order to harmonies the combination of qualitative and quantitative criteria to develop the best decision making models for the selection of best vendors.

There is a need to conceptualize and structure the numerous components of the evaluation problem into an analytic framework that may facilitate understanding and to devise a selection tool that can take both prospective qualitative and quantitative objectives into consideration as desired. DBA is a good candidate for these kinds of selection problem that both qualitative and quantitative objectives have to be considered.

2.2 Distance Based Approximation (DBA)

The evaluation of vendors is a complicated decision problem, for the following reasons:

The complexity comes from two main sources. The first is the relative difficulty to conceptualize and structure the numerous components of the evaluation into analytical framework that may facilitate understanding. The second is the nature of the components in the process; some are qualitative whereas others are objective.

As the competition in market place increases, there exists a large search space for decision makers.

There is a multitude of selection criteria which are after varying, conflicting, sometime complimenting and many times are non- expressible in commensurable units and some factors/ criteria might reflect psychological aspects such as qualitative consideration, vague, and intangibles.

Yahya and kingsman (1999) have discussed different methods of vendor rating such as categorical methods, simple linear weighted average method, cost ratio method and AHP. They have used AHP as useful practical and systematic method for vendor rating. This process was suggested by Satty in 1988. It is one among most popular techniques of multi-criteria decision making (MCDM) methods because of its ability to handle complex and ill-structured problems. The major drawback of AHP is that it cannot effectively take into account risk and uncertainty in estimating the relative importance of the attributes, because pair wise comparison of criteria becomes difficult for the experts as number of criteria /sub -criteria increases. But this difficulty has been overcome/overrule by DBA methodology which is one of the most popular techniques of multi-criteria decision making (MCDM). Five features of the DBA differentiate it from other decision making process.

Its ability to handle both tangible and intangible attributes;

Its ability to structure the problems, in a hierarchical manner, to gain insights into the decision -making process;

Its ability to monitor the consistency the consistency with which a decision maker makes a judgement.

Its ability to make the pair wise comparison of criteria/sub-criteria easy.

Its ability to consider any number of vendors and criteria/sub-criteria according to need and requirement.

Although the DBA is a good candidate for supplier selection, not too much implementation of DBA has been reported. In this paper, Implementation of the DBA vendor selection model is carried out in line with analysis, evaluation, selection, ranking of vendor and quality management system. Vendor involvement is an important dimension of quality management, there is need to incorporate vendor selection from a quality management perspective. Customer to customer approach reflects the contemporary business approach. It starts from understanding the customer needs to the delivery of the product for service. Implementation of DBA model with discussed in section 4.

3. RESEARCH MODEL/Methodology

The methodology adopted for the research consisted of five main stages

Problem background review that covers the recent business trends and environment of automobile manufacturing industry in India, the company background, the product and its competitive strategy. It also includes a review of current company practices of vendor selection.

The preliminary research includes review of various vendor selection criteria from the available literature, preparation of questionnaire, Interview of companies personnel involved in vendor selection etc. The research direction is established and foundation of the model is built based on research finding, from the analysis of data so collected.

The complete and exhaustive literature review for the development of hierarchical vendor selection model covered the five main topics, namely strategic purchasing, supply management, vendor selection, vendor selection criteria, vendor selection methods etc. and has been discussed section - 2 on the Literature Review.

The development of hierarchical vendor selection process that includes the establishment of vendor selection criteria, identification and development of respondent sample base, construction of matrix and distance based approximation models, design and preparation of questionnaire, data collection, data analysis and finally synthesis of the model.

Developing a framework to implement the vendor selection model through quality management system principles is the conceptual work concluded from vendor selection model. The frame work gives the methodology regarding the facilitation of the implementation of the DBA model using quality management system principles.

The research model of the research problem is presented in Fig. - 3.1.

Development of Supplier selection criteria

Vendor Selection Methods

Strategic Purchasing

Supply Management

Vendor Selection Criteria

Quality Management System

Problem Background Review

Preliminary Research

Buyer Interview

Questionnaire Analysis

Literature Review

1

2

3

Frame Work

Model Development

Criteria Evaluation Design

Respondent Interview

Building Matrix/DBA Model

Quality Management

4

5

Fig. 3.1 An overview of research model

Development of Supplier selection criteria

4.1 Formation of vendor selection criteria

In this work the purchasing competitive priorities and its measures given by Krause et al. (2001) have been adopted as the criteria and sub criteria. However the criteria delivery purposed by Krause et al. (2001) has been replaced by prototype development time. In this paper vendor selection criteria and sub criteria are described in the subsequent paragraph

Cost

The Cost factor is measured, on the basis of importance of the cost /price dimensions in the buying firm's vendor selection i.e. total cost, the vendor willingness and ability to share cost data, discount rate per unit and discount rate on annually purchased units, freight cost, outdated product cost, cost of forming relationship, etc.

Quality

The quality factor is measured, in terms of vendor's ability to provide inputs that are reliable, durable and confirms to buying firms specifications. It also includes repair and return rate, product reliability, vendor's quality systems and quality certifications.

Flexibility

The flexibility factor is measured on the basis of importance of flexibility dimensions in buying firm's selection process: the ability and willingness of the vendor to change order volumes and to mix the ordered items.

Delivery

The delivery factor is measured on the basis of importance of the delivery dimensions in buying firm's vendors selection process: ability and willingness to expedite an order, how quickly a vendor can deliver, the amount of time that it takes a vendor to deliver prototype ,the ability of the vendor to meet due date.

Service

The service factor is measured on the basis of importance of the service dimensions in buying firm's vendor selection. These include the vendor attitude to handling complaints, and the ability and willingness to provide problem solving aids and to share vendor's logistic information. It also involves teaching and training to the employees of the company and the availability of products in stock with a vendor.

Infrastructure, Environment Management &Pollution Control

This factor is measured on the basis of importance of the capacity of the machines, development of product, production in the company, availability of raw material in the company and environmental related certificates of vendor and company to achieve green computing, and new horizon in energy efficiency and e- waste minimization.

Information, communication and Technological capabilities

This factor is measured on the basis of importance of availability of information, communication and technological devices and systems with vendors and manufacturers, vendor technical innovativeness, research and development capabilities, etc.

Organization profile, culture and strategy

This factor is measured on the basis of importance of financial status of the vendor, historical background for three decades, reputation of the vendor in the national / international market, organization culture and strategy, relationship among employer and employees, information exchange level between vendor and company, relation among labors, organization employees and management.

Summary of criteria

All these criteria and corresponding sub criteria are presented in an aggregated in manner in fig.3.2 at the second and third level of hierarchy respectively.

Building the DBA model

DBA modeling process involves four phases namely development of decision hierarchy, measurement and data collection, determining the normalized weight, and synthesis- finding solution to the problem. Using this four phase, a DBA model is formulated for vendor selection and presented in the following

4.2.1 Development of decision hierarchy

This phase involves development of a decision hierarchy and calculating the weights of each level of vendor selection model. The development of a vendor selection model includes an establishment of vendor selection criteria and sub criteria, development of respondent sample base, construction of model with appropriate methodology, design and analysis of questionnaire and finally the synthesis of the model. The decision hierarchy contains five levels and is presented in Fig. - 3.2.

The first level of the hierarchy is 'Goal'. In the present research work, the goal of our problem is to select a vendor for an automobile industry in India that can provide best material, aligned with the industry strategy and willing to long term collaborative business relationship.

Second and third levels contain the criteria and sub criteria that are relevant to vendor selection for automobile industry in India, which were identified in the previous section.

The fourth level relates to the rating scale. The rating scale will be assigned to each criteria and sub-criteria related to each vendor. The use of rating scale can be found in previous studies, Liberatore (1987, 1989, and 1992). The major advantage of this method is to overcome the explosion in the number of required comparisons when the numbers of alternates are large.

In order to avoid potential complications that may arise when assigning the rating scale by using the 5-point rating system and to make fine discrimination in judgment, 10-point fuzzy and conventional scales are used. All efforts are made to keep the assessment process as simple as possible.

The fifth i.e. lowest level of hierarchy consists of potentially viable alternate vendors to be evaluated for ranking and selection of best vendor. Five potential vendors are picked from an automobile industry for evaluation.

4.2.2 Measurement and data collection

Primary data can be defined as new data used to solve the problem, or question at hand. The primary data is facts and information collected by researchers for specific purpose (Rabianski, 2003). The methods for primary data involve interviews, observation and questionnaire. Interview techniques can be used in different forms such as personal direct contact, phone, e-mail or other communication media. Secondary data can be defined as the data obtained from any journals, articles, books, papers and Internet sources, which could provide with focused information regarding the vendor selection process. About 500 hundred journals were consulted to get the secondary data prepared for the questionnaire. And this qualitative data was converted into quantitative data by using fuzzy ten scale rating.

Determination of normalized weights

The importance of the criteria/sub-criteria assigned by the 26 experts involving 5 automobile industries involved in manufacturing of cars. The participation was voluntary but limited to the executive level involved directly or indirectly with the process of vendor selection. These 26 experts were from purchase, production engineering, operation management, quality. The participation from purchase and production department was 60 percent, who were mainly involved in vendor selection process. The normalized average priority weight of each criterion/sub-criterion using fuzzy set theory, are given in Tables 1 and 2, respectively.

TABLE - 1 AGGREGATED AVERAGE WEIGHTS OF SELECTION CRITERIA

Criterion No.

Criterion

Average

weight

Car

1

Cost

0.19545

2

Quality

0.241986

3

Flexibility

0.105481

4

Delivery

0.137539

5

Service

0.108583

6

Infrastructure, Environment Management &Pollution Control

0.058945

7

Information, Communication and Technological capabilities

0.09514

8

Organization Profile, Culture and Strategy

0.056877

TABLE - 2 RANKING OF STRATEGIC MEASURES (CRITERIA)

Criterion No.

Criterion

Average

weight

Car

1

Quality

0.241986

2

Cost

0.19545

3

Delivery

0.137539

4

Service

0.108583

5

Flexibility

0.105481

6

Information, Communication and Technological capabilities

0.09514

7

Infrastructure, Environment Management &Pollution Control

0.058945

8

Organization Profile, Culture and Strategy

0.056877

TABLE - 3 AGGREGATED AVERAGE WEIGHTS OF SELECTION SUB-CRITERIA

Sub-criterion No.

Sub-Criterion

Average

weight

Cars

1.1

Unit Cost

0.196324

1.2

Freight Cost

0.161267

1.3

Operation & Maintenance Cost

0.174995

1.4

Cost of Relationship

0.140569

1.5

Quantity Discount Rate

0.11556

1.6

Cost Reduction Plan

0.046569

1.7

Product Return Costs

0.116422

1.8

Obsolescence Cost

0.048294

2.1

Rejection Rate

0.188862

2.2

Confirmation to Specifications

0.188862

2.3

Repair and Return Rate

0.146086

2.4

Human-hour loss

0.083132

2.5

Product Reliability

0.131558

2.6

Product Durability

0.104923

2.7

Quality Management Capabilities

0.066182

2.8

Quality Certification

0.090395

Sub-criterion No.

Sub-Criterion

Average

weight

Car

3.1

Volume Flexibility

0.210102

3.2

Process Flexibility

0.163637

3.3

Service Flexibility

0.135354

3.4

Emergency Order Processing

0.209087

3.5

Product Flexibility

0.164647

3.6

Customization

0.117172

4.1

Order Lead Time

0.167296

4.2

On Time Delivery

0.211056

4.3

Emergent Response Time

0.170953

4.4

Delivery Reliability

0.164554

4.5

Distribution Network Capabilities

0.117016

4.6

Return Product Velocity

0.169125

5.1

After Sales Service

0.253369

5.2

Response to Customer

0.206069

5.3

Training and Teaching

0.114113

5.4

Maintenance

0.224813

5.5

Stock Availability

0.201637

6.1

Production facility and capacity

0.208

6.2

Product development/ appearance

0.208

6.3

Production capability

0.160889

6.4

Availability of resources

0.091556

6.5

Environmental certificates

0.119111

6.6

Pollution control

0.048

6.7

Green product

0.047111

6.8

Green competency

0.117333

7.1

Information devices and system

0.109364

7.2

Communication systems

0.135244

7.3

Technical Innovation

0.156949

7.4

Technological system

0.146931

7.5

R&D capabilities

0.111033

7.6

Technical support

0.192712

7.7

Geographical Location

0.147766

8.1

Financial status

0.179366

8.2

Performance history

0.220447

8.3

Reputation

0.184294

8.4

Risk factor

0.126148

8.5

Culture and strategy

0.053219

8.6

Relationship

0.053219

8.7

Level of corporation and information exchange

0.133046

8.8

Labor relations

0.050262

TABLE 4 RANKING OF STRATEGIC MEASURES (SUB-CRITERIA)

Rank No.

Sub-Criterion

Global Weight

Rank

No.

Sub-Criterion

Global Weight

Rejection Rate

0.044468

Quality Management Capabilities

0.016094

Confirmation to Specifications

0.044374

Service Flexibility

0.014695

Unit Cost

0.038581

Technical Innovation

0.014617

Operation & Maintenance Cost

0.034695

Geographical Location

0.01382

Repair and Return Rate

0.034405

Technological system

0.013785

Product Reliability

0.031038

Training and Teaching

0.013545

Freight Cost

0.030135

Customization

0.012877

On Time Delivery

0.029076

Communication systems

0.012723

After Sales Service

0.028273

Performance history

0.012465

Cost of Relationship

0.02693

Production facility and capacity

0.012338

Product Durability

0.025537

Product development/ appearance

0.012337

Maintenance

0.025273

Financial status

0.010595

Order Lead Time

0.023573

Information devices and system

0.010165

Volume Flexibility

0.023486

Reputation

0.010013

Emergency Order Processing

0.023264

R&D capabilities

0.009933

Emergent Response Time

0.022855

Production capability

0.009507

Return Product Velocity

0.022855

Obsolescence Cost

0.009333

Delivery Reliability

0.022801

Cost Reduction Plan

0.00919

Quantity Discount Rate

0.022228

Risk factor

0.007291

Response to Customer

0.022003

Environmental certificates

0.007079

Stock Availability

0.022003

Level of corporation and information exchange

0.007007

Product Return Costs

0.021871

Green competency

0.006671

Quality Certification

0.02176

Availability of resources

0.005922

Human-hour loss

0.021431

Labor relations

0.003078

Process Flexibility

0.018333

Culture and strategy

0.00303

Product Flexibility

0.017998

Relationship

0.002959

Technical support

0.017834

Pollution control

0.002927

Distribution Network Capabilities

0.016156

Green product

0.002768

The global weights of each sub-criterion are calculated and arranged in descending order of priority. It can be seen that the measures of 'quality' occupy the top most rankings in the list, the top ranks being the 'rejection rate' and 'conformation to specifications'. The measures of 'cost' occupy next ranks 3 and 4, which are the 'unit cost' and operation and maintenance cost.' Ranks 5 and 6 are occupied by 'quality' measures whereas rank 7 by 'cost' measure which is followed by 'delivery' measure 'On Time delivery' at rank 8. Rank 9 is occupied by 'service' measure i.e. 'after sales service'. Among top 10 ranks, 4 ranks are occupied by 'quality' and 'cost' measures each whereas one rank is held by 'delivery' and 'service' measures. An overview of the ranking of sub-criteria measures is given in Table - 6.9.

Vendor - 1

Vendor - 2

Vendor - 3

Vendor - 4

Vendor - 5

Rating Scale: Fuzzy ten point

Rejection Rate

Confirmation to Specifications

Repair and Return Rate

Human-hour loss

Product Reliability

Product Durability

Quality Management Capabilities

8. Quality Certification

Volume Flexibility

Process Flexibility

Service Flexibility

Emergency Order Processing

Product Flexibility

Customization

Order Lead Time

On Time Delivery

Emergent Response Time

Delivery Reliability

Distribution Network Capabilities

Return Product Velocity

After Sales Service

Response to Customer

Training and Teaching

Maintenance

Stock Availability

Production facility and capacity

Product development/appearance

Production capability

Availability of resources

Environmental certificates

Pollution control

Green product

Green competency

Information devices and system

Communication systems

Technical Innovation

Technological system

R&D capabilities

Technical support

Geographical Location

Financial status

Performance history

Reputation

Risk factor

Culture and strategy

Relationship

Level of corporation and information exchange

Labor relations

Unit Cost

Freight Cost

Operation & Maintenance Cost

Cost of Relationship

Quantity Discount Rate

Cost Reduction Plan

Product Return Costs

Obsolescence Cost

COST

QUALITY

FLEXIBILITY

DELIVERY

SERVICE

INFRASTRUCTURE, ENVIRONMENT MANAGEMENT AND POLLUTION CONTROL

INFORMATION, COMMUNICATION AND TECHNOLOGICAL CAPABILITIES

ORGANIZATION PROFILE, CULTURE AND STRATEGY

GOAL: SELECTION OF BEST VENDOR

Fig. 3.2 Decision hierarchy for vendor selection system

Synthesis: finding a solution to the problem

After computing the normalized priority weight for each criteria and sub criteria, next phase is to synthesize the solution for vendor selection problem by using DBA methodology.

An illustration of supplier selection using DBA model

A simplified example is given to illustrate the vendor selection process. It has been assumed that vendor has to be chosen for critical items. The five competitive vendors are to be accessed by evaluation team consisting of ten expert members. Below is the step by step illustration.

Select the global weight set for critical items vendor selection; the global weights employed for the demonstration are shown in the table and table

Evaluate the competitive vendors; here a ten point conventional rating system/scale is used as shown in the fig. The rating obtained from expert one is shown in the table.

The average aggregated ratings of each vendor for each criterion is multiplied by the average weight of that criterion to get the absolute values of the criterion.

Finally, the Euclidean composite distance, CD, between each vendor to the optimal state is derived from Eq. 6.22. Table 6.12 shows the composite distance values and the rankings of each vendor based on selection criteria. The vendor with lowest value of the composite distance is ranked as #1, and with second lowest value as rank #2, and so on. The vendor with maximum value of the composite distance is ranked last.

Finally, the Euclidean composite distance, CD, between each vendor to the optimal state is derived from Eq. 6.22. Table 6.12 shows the composite distance values and the rankings of each vendor based on selection criteria. The vendor with lowest value of the composite distance is ranked as #1, and with second lowest value as rank #2, and so on. The vendor with maximum value of the composite distance is ranked last.

TABLE - 6.12 RANKING OF VENDORS BASED ON CRITERIA BY DBA METHOD

Vendor

Sum

Composite Distance

Rank #

Vendor - 1 (V1)

16.14030

4.0175

1

Vendor - 2 (V2)

27.136806

5.2093

5

Vendor - 3 (V3)

18.89640

4.347

2

Vendor - 4 (V4)

20.59525

4.5382

3

Vendor - 5 (V5)

23.53705

4.8515

4

Ranking Criterion

Cost

Quality

Flexibility

Delivery

Service

Infrastructure, Environment Management &Pollution Control

Information, communication and Technological capabilities

Organization profile, culture and strategy

Vendor

CD

Rank

CD

Rank

CD

Rank

CD

Rank

CD

Rank

CD

Rank

CD

Rank

CD

Rank

(V1)

4.7628

4

0.2870

1

2.4283

2

1.4231

1

5.9831

5

0.3561

1

4.8785

3

0.0000

1

(V2)

6.9355

5

1.6889

3

4.0514

3

5.2438

5

3.8010

4

5.9257

5

4.2087

2

3.0383

2

(V3)

2.5762

3

0.7757

2

4.9439

4

3.2931

3

3.1010

3

4.3535

3

4.9271

4

3.6499

3

(V4)

1.7450

2

3.8182

4

5.4523

5

2.7859

2

2.9322

2

4.5466

4

0.0000

1

7.1684

5

(V5)

0.0000

1

6.7281

5

0.0000

1

5.0216

4

0.0000

1

2.9185

2

6.0222

5

5.5260

4

4.4 Development of frame work to implement the vendor selection model using DBA methodology

The development of the DBA method begins with defining the optimal state of the overall objective, and specifies the ideally good values of comparison criteria involved in the process. The optimal state of the objective is represented by the optimum model, the OPTIMAL. The vector OP, , is the set of "optimum" simultaneous criteria values. In an n-dimensional space, the vector OP is called the optimal point. For practical purposes, the optimal good value for criteria is defined as the best values which exist within the range of values of attributes. The OPTIMAL, then, is simply the alternate vendor that has best values of all vendor selection criteria. It is very unlikely that a certain alternate vendor has the best values for all criteria. Instead, a variety of alternate vendors may be used to simulate the optimal state. For this reason, the OPTIMAL is not to be considered as feasible alternatives, but it is used only as reference to which other alternatives are quantitatively compared. The numerical difference resulting from comparison represents the effectiveness of alternate vendors to achieve the optimal state of the objective function. Hence, here, the decision problem is to find a feasible solution which is as close as possible to the optimal point. The objective function for finding such a solution can be formulated as

(3.14)

x Ï‚ X

Where {}, and represent an alternate vendor in the n-dimensional space, and the distance from the optimal point, respectively. Thus the problem, and its solutions depend on the choice of optimal point, OPTIMAL, and the distance metric,, used in the model. In two dimensional spaces, this solution function can be illustrated as in Fig. - 3.5, where H is the feasible region and the OP is the optimal point.

fig2dpi600

Fig. 3.5 Solution function in 2-dimensional space

The DBA method determines the point in the H region which is "the closest" to the optimal point, and is graphically explained in Fig. - 3.6 for two dimensional cases. Note that the lines, and are parallel to the X1, and X2 axis respectively. Therefore, , and . Based on Pythagoras theorem, in two dimensional space, is

(3.15)

In general terms, the "distance" can be formulated as

(3.16)

where i = 1, 2, 3, ..., n = alternate vendor(s), and j=1, 2, 3,... ,m = selection criterion.

fig3

Fig. 3.6 Distances of real vector in 2-dimensional space

To implement the above approach, let us assume that we have 'n' alternate vendors and 'm' selection criteria corresponding to each alternate vendor e.g. , , , and the OPTIMAL where = the best value of the criterion 'm'. It is observed that the best numerical value of some criterion is smaller than that of the worst level of the other criterion. To avoid confusion and difficulties in performing the analysis, those values have been adjusted using following two cases:

Case - I: When smaller value of the criterion represents fitting well to the actual data i.e. is the best value:

Criterion Adjusted Value = Criterion Maximum Value in the database - Criterion Value.

Case - II: When bigger value of the Criterion represents fitting well to the actual data i.e. is the best value:

Criterion Adjusted Value = Criterion Value - Criterion Minimum value in the database.

Thus, the whole set of alternatives can be represented using the adjusted values of the criteria by the matrix:

. (3.17)

Thus, in this matrix, a vector in an m-dimensional space represents every alternate vendor. To ease the process, and to eliminate the influence of different units of measurement, the matrix is standardized using

(3.18)

Here, , and (3.19)

(3.20)

Where i = 1, 2, 3, ... , n, and j = 1, 2, 3, … , m.

, and represent the average value, and the standard deviation of each selection criterion for all alternate vendors. The standardized matrix is represented as:

(3.21)

where

The next step is to obtain the difference of each alternate vendor to the reference point, the OPTIMAL, by subtracting each element of the optimal set by a corresponding element in the alternate set. This result in another interim matrix namely distance matrix and is given as:

(3.22)

Finally, the Euclidean composite distance, CD, between each alternate vendor to the optimal state, OPTIMAL, is derived using

(3.23)

Within any given set of alternate vendors, this distance of each alternate to every other is obviously a composite distance. In other words, it can be referred to as the mathematical expression of several distances on each selection criterion for which the vendors are evaluated and ranked.

The complete DBA method has been summarized and is represented in the flow chart as shown in Fig. - 3.7. This method has been implemented in Matlab 7.

CONCLUSIONS AND RECOMMENDATIONS:

In this paper study has attempted to advance the art of supply chain management by developing heuristic methodologies using " "Distance Based Approximation Method" which simplifies the task of vendor selection and reduces the tediousness as well as the degree of error by directly involving the decision maker in the selection process.

After calculating the weights of each criterion and sub-criterion, they are arranged in descending order of priority as shown in Table 4. 'Quality', 'Cost' and 'Delivery' occupy the first, second and thirds ranks respectively, followed by 'Service' and 'Flexibility', and the last is 'Organization profile, culture and strategy'.

The results show that the 'Quality', 'Cost' and 'Delivery' are the most important strategic pointers to be considered in the vendor selection problem, representing more than 50% (56.9%) of the total weighting. The local weight figure of strategic priority shows that quality (Local weight of 0.239107) is around four times important than 'Infrastructure, Environment Management & Pollution Control' (Local weight of 0.059549) and 'Organization profile, culture and strategy' (Local weight of 0.56438).

The global weights of each sub-criterion are calculated and arranged in descending order of priority. It can be seen that the measures of 'quality' occupy the top most rankings in the list, the top ranks being the 'rejection rate' and 'conformation to specifications'. Since the automobiles are high end consumer goods which should provide efficient, reliable and safest performance due to involvement of human lives, high quality is the supreme requirement. Cost is the next most important strategic priority with its weight just less than that of quality by 0.046, which is not surprising because cost is an important dimension from business perspective. During these tough times, cost cutting is the main operation activity in automobile industry due to large number of multinational industries and cutting edge competition in the market. Best efforts are made to source vendor who can provide a conforming quality item with an attractive price or discount.

Ranking of vendors have been done on the basis of eight criteria as well as individual criteria and their ranking has been done as shown in table 6.12.