Performance Measurement of Supply Chain Management: A

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Performance measurement of supply chain

management: A decision framework for

evaluating and selecting supplier performance in a

supply chain

Sarode A.D. Lokmanya Tilak College of Engineering Koperkhairane,

Navi Mumbai 400709 (India)

Khodke P.M. Government College of Engineering, Amravati 444604

(India)

Abstract

Supply Chain Management (SCM) has gained significance as one of the 21

century manufacturing paradigms for improving organizational competitiveness. Supply

chain ensures improved efficiency and effectiveness of not only product transfer, but also

information sharing between the complex hierarchies of all the tiers. The literature on

SCM that deals with strategies and technologies for effectively managing a supply chain

is quite vast. In recent years, organizational performance measurement (PM) and metrics

have received much attention from researchers and practitioners. Performance

measurement and metrics have an important role to play in setting objectives, evaluating

performance, and determining future courses of actions. Apart from the common criteria

such as cost and quality, ten other performance measurements are defined, visibility,

trust, innovativeness, delivery reliability, flexibility and responsiveness, resource

utilization, cost, assets, technological capability, service and time to market, so total

twelve criteria and fifty eight subcriteria are used to evaluate the performance in supply

chain. So, for evaluating and selecting supplier a multi-attribute decision-making

technique, an analytic hierarchy process (AHP), is used to make decision based on the

priority of performance measures. This paper describes a decision framework for

evaluating and selecting supplier performance in a supply chain. A case study from the

automotive industry is used to demonstrate the AHP technique.

Keywords

Supply chain, Analytical hierarchy process (AHP), supplier selection, SCM

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Introduction

The development of economy of any country is supported by growth of its

manufacturing industries. Currently, the manufacturing industries are passing through a

phase of very tough competition. The economic environment is becoming harsh. In order

to survive, every industry has to strive to improve productivity in all spheres of activity.

What is required is to devise new ways of improving manufacturing performance by

optimally utilizing the resources. In this context, effective supply chain management is

vital to the competitiveness of manufacturing enterprises, as it directly impacts their

ability to meet changing market demands in a timely and cost effective manner. Figure1

shows the typical supply chain consisting of different levels e.g. supplier, manufacturer,

distributor and consumer, who work together in an effort to acquire raw materials,

convert these raw materials into specified final products and deliver these final products

to retailers Beamon (1998). So, it is a network of companies which influence each other.

The complexity and the large network affect one another’s performance. In this context,

Chan (2003) highlighted some important issues like, how would the supply chain

perform? How can the managers choose the most optimum supply chain best suited for

its particular industry? Karthik (2006) observed that the objective of the supply chain

was to maximize the difference between worth of the final product to the customer and

the effort the supply chain expended in fulfilling the customer needs.

Manufacturer Distributor Retailer Customer

Supplier

Fig.1 A typical Supply Chain

The aim of supply chain management is to gain an advantage in terms of customer

service and cost over competitors. Therefore it is desirable to assess the company’s

performance by benchmarking. Given the inherent complexity of the typical supply

chain, selecting appropriate performance measures for supply chain analysis is

particularly critical, since the system of interest is generally large and complex.

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The purchasing function has gained great importance in the supply chain

management due to factors such as globalization, increased value added in supply, and

accelerated technological change. Purchasing involves buying the raw materials,

supplies, and components for the organization. The activities associated with it include

selecting and qualifying supplier, rating supplier performance, negotiating contracts,

comparing price, quality and service, sourcing goods and service, timing purchases,

selling terms of sale, evaluating the value received, predicting price, service, and

sometimes demand changes, specifying the form in which goods are to be received, etc.

The key and perhaps the most important process of the purchasing function is the

efficient selection of supplier/ vendors, because it brings significant savings for the

organization (Ballow 1999). The objective of the supplier selection process is to reduce

risk and maximize the total value for the buyer, and it involves considering a series of

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strategic variables. Some authors have identified several criteria for supplier/vendor

selection, such as the net price, quality, delivery, historical supplier performance,

capacity, flexibility, service, communication systems and geographic location (Dickson

1966, Dempsey 1978, Weber et al 1991, Noorul Haq and Kannan 2006, Sarode et al

2008). These criteria are key issues in the supplier assessment process since it measures

the performance of the suppliers.

This paper present a total twelve criteria and fifty eight subcriteria for evaluating

the supplier/vendor selection for the automobile manufacturing industries located at the

western part of India using the analytical hierarchy process (AHP) even this paper

describes a decision framework for pairwise comparison which helps to identify easily

the importance of different performance measures. A case study from the automotive

industry is used to demonstrate the AHP technique.

Literature Review

At present, there is a boom in supply chain management research. A large

amount of publications appeared on this issue, particularly in the supplier/vendor

selection problem. This problem has been extensively studied in the literature, the

following paragraphs summarizes some of the contribution that are important to this

paper.

In early 1986, Timmerman (1986) propose linear weighting models in which

suppliers are rated on several criteria and in which these ratings are combined into a

single score. These models include the categorical, the weighted point and the analytical

hierarchical process (Nydick and Hill 1992). The major limitation of this approach is that

it is difficult to effectively take qualitative evaluation criteria into consideration. Total

cost approaches attempt to quantify all costs related to the selection of a vendor in

monetary units. This approach includes cost ratio by Timmerman (1986) and total cost of

ownership (Ellram 1995). Petroni and Braglia (2000), discuss the principle component

analysis (PCA) method which is multiobjective approach to vendor selection that

attempts to provide a useful decision support system for purchasing manager faced with

multiple vendors and trade-offs such as price, delivery, reliability, and product quality.

The major limitations of this approach are it requires the knowledge of advanced

statistical technique.

Wei et al. (1997), discuss in their paper about the neural network for the supplier

selection. Comparing to conventional models for decision support system, neutral

networks save a lot of time and money for system development. The supplier-selecting

system includes two functions: one is the function measuring and evaluating performance

of purchasing (quality, quantity, timing, price, and costs) and storing the evaluation in a

database to provide data sources to neural network. The other is the function using the

neural network method saves money and time of system development. The weakness of

this method is that it demands software and requires a qualified personnel expert on this

subject.

Dickson (1966), reports 23 different criteria for vendor’s evaluation. Of these

criteria, he states that cost, quality and delivery times are among the most important

performance measures in the selection of vendor’s. Since that time, numerous papers

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have cited his work approaching the vendor selection problem mainly from three

perspective; conceptual, empirical, and mathematical (Talluri and Narasimhan 2003).

Chan (2003) reported seven performance measures as the key elements of vendor

selection-cost, resource utilization, quality, flexibility, visibility, trust and innovativeness.

For each measure, he identified factor commonly used for vendor selection.

Sarode et al. (2008) reported total twelve measures which includes qualitative and

quantitative type-quality, visibility, flexibility and responsiveness, resource utilization,

cost, asset, technological capability, service and time to market apart from these twelve

measure total fifty eight items/ variables identified.

Noorul and Kannan (2006) identified seven performance measures- quality,

delivery, production capability, service, engineering/ technical capabilities, business

structure and price and their thirty two sub factors for the vendor selection. Weber et al.

(1991), present a comprehensive review of the literature providing the most important

criteria in the choice of suppliers. According to their investigation, they rank price as the

most important factor in the selection process followed by lead time and quality factors.

Patton (1996) sampled 1500 buyers to identify the effects of human judgment

models on vendor selection. His findings suggest that it is not as much the difference in

attributes between vendors that affect the outcome, but it is the type of human model used

that lead to the variance in the selection of vendors. Stanley and Wisner (2001), collect

data from 118 executives to study the outcome of previous research concepts regarding

this problem. One of the important results of their study suggests that greater emphasis

should be given to strategic activities in the process of supplier’s selection. Verma and

Pullman (1998), propose the supplier selection process using the two methods namely

Likert scale set of questions and a discrete choice analysis (DCA) experiment. According

to them quality is an important factor to select the supplier.

Lambert et al. (1998), in their book, describe a method for evaluating and

comparing several suppliers. A rating factor is assigned to each supplier followed by a

weight to determine the importance of each factor. To make the comparison feasible, a

weighted composite measure is developed by multiplying the rating factor by the weight.

However, how to assign the weights has not been clearly described in their approach.

Weber et al. (1997), utilized data envelopment analysis (DEA) in the quest for

vendor selection. Their approach is based on defining several attributes of vendor such

as defects, price, and lateness in delivery. Based on this, a negotiation position is

developed for those vendors that would like to be on the list of future supplier for the

considered firm.

Sheu (2004), proposed a methodology in his research that would stimulate

research in the related fields of global logistics, and may help address issues regarding

the uncertainty and complexity of global logistics operations. Chan and Chung (2004),

develop a multi-criterion genetic optimization for solving distribution network problems

in supply chain management. In this work they combine analytic hierarchy processes

with genetic algorithms to capture the capability of multi-criterion decision-making

which will reduce the computation time. Vaidya and Kumar (2004), presents a literature

review of the applications of the analytic hierarchy process (AHP) and also provided the

various application area where the AHP is used as a multiple criteria decision-making

tool. Handeld et al. (2002), integrate environment issues in their supplier assessment

decisions with the help of AHP.

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Tam and Tummala (2001) discussed the vendor selection for the telecommunication

systems and based on the proposed model the time taken to select the vendor has been

reduced. Based on the above literature, most of the previous researchers have considered

only four to five main factors (quality, service, price and delivery) and about 8 to 32 sub

factors for selection of vendors. This paper describes twelve main factors and 58 sub

factors for the supplier/vendor selection.

Development of Decision framework for evaluating and selecting

supplier Performance in supply chain:

The company chosen for this work plan to build a supply chain for its automobile

production. Raw materials or components can be outsourced to vendors. The question

arise which vendor are to be selected. The attributes and sub-attributes have to be most

prevalent and important in the vendor selection process. Choosing the possible criteria

for the vendor selection involves a decision making team which includes experts from the

industry side (purchasing manager, purchasing director, sales manager, product manager,

quality manager and production manager). The attributes and sub attributes involved in

the vendor selection have been chosen by conducting a survey. A questionnaire

consisting of these factors was designed for the survey. The respondents for the survey

are selected randomly from different functional areas that are directly involved with the

materials supplied by the vendors. Based on the survey conducted the major attributes

and sub-attributes involved in the vendor selection are given in Table 1.

Development of analytical process for qualitative analysis

Analytical hierarchy process (AHP) was developed in 1972 as a practical

approach in solving relatively complex problems (Saaty 1980). It is used for multicriteria

problems in a number of application domains (Roger 1987, Saaty 2000 and Kodali and

Chandra 2001, Kodali and Routroy 2006). The general approach of the AHP is to

decompose the problem and make pair-wise comparison of all elements on a given level

with related elements in the level just above it belong. A highly user friendly computer

model is developed which assists the user in evaluating his/her choices. The schematic of

AHP for selection of supplier in supply chain is shown in Fig 1.

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Selection of best Supplier

Level 1

Attributes

Level 2

QULT VSBL TRST INVN DLRL FLRS RSUZ COST ASST TECB SERV

TTMK

TLMC

PCPL DFCT DBCT

MFGC

INVC

WHEC

ICSS ITGC

OHDC

SLTC

Supplier 1 Supplier 2 Supplier 3

Sub-attributes

Level 3

Level 4

Fig.1. Schematic of AHP for selection of best supplier

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Description of the model

A thorough analysis of the problem is required along with the identification of the

important attributes/ criteria involved. Sarode et al (2008) presented a through analysis

of identification of the important attributes/ criteria measures for performance

measurement in supply chain. The attributes and sub-attributes used in the AHP for

analysis for selection of supplier in supply chain are as follows.

Table-1 Performance measures (Sarode 2008)

1. Quality

[QULT]

• Customer satisfaction [CUST]

• Customer response time [CURT]

• Lead time [LDTM]

• On-time delivery [OTDL]

• Fill rate [FLRA]

• Stock out probability [STOP]

• Accuracy [ACCY]

• Base of communication [BACM]

• Process flexibility [PRFL]

• Percentage rejections [PGRJ]

• Inspection methods and plans [INMP]

• Warranty claims [WACL]

• Availability of test equipment [AVTE]

• Adherence of total quality management concept [ATQM]

2. Visibility

[VSBL]

• Time [TIME]

• Accuracy [ACCY]

3. Trust

[TRST]

• Consistency [CNTY]

4. Innovativeness

[INVN]

• New launch of product [NLPD]

• New use of technology [NUTE]

5. Delivery reliability

[DLRL]

• Delivery performance [DLPM]

• Fill rate [FLRA]

• Order fulfillment lead time

[OFLT]

• Perfect order fulfillment [POFM]

6. Flexibility and responsiveness

[FLRS]

• Supply chain response time [SCRT]

• Production flexibility [PDFB]

7. Resource utilization

[RSUZ]

• Manufacturing resources [MFGR]

• Storage resources [STRS]

• Logistics resources [LGRS]

• Human resources [HMRS]

• Financial resources [FLRS]

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8. Cost

[COST]

• Total logistics management cost [TLMC]

• Process capability [PCPL]

• Defects [DFCT]

• Distribution cost [DBCT]

• Manufacturing cost [MFGC]

• Inventory cost [INVC]

• Warehouse cost [WHEC]

• Incentives cost and subsidies [ICSS]

• Intangible cost [ITGC]

• Overhead cost [OHDC]

• Sensitivity to long term cost [SLTC]

9. Assets

[ASST]

• Cash-to-cash cycle time [CCCT]

• Inventory days of supply [ITDS]

• Assets turns [ASTT]

10. Technological capability

[TECB]

• Product and process facilities [PDPE]

• Skill and manpower [SKMD]

• Customised services [CMSE]

• Cost evaluation [CTEL]

11. Service

[SERV]

• On-time delivery [OTDL]

• Base of communication [BCMN]

• Response to changes [PRCH]

• Process flexibility [PRFL]

• Customer satisfaction [CUSF]

12. Time to market

[TTMK]

• Delivery product to market quickly [DPMQ]

• First in the market in introducing new product [FMIP]

• Time to market lower than industry average [TMIA]

• Fast development cell [FDMC]

Analytical hierarchy process methodology

AHP (Saaty, 1982) was developed as a practical approach in solving relatively

complex problems. AHP enables the decision maker to represent the simultaneous

interaction of many factors in complex, unstructured situation. For the effectiveness of

supply chain, the judgments based on observations are fed into AHP for each criterion

and sub-criterion of all levels of hierarchy. Pair-wise comparisons of criterion at each

level are done on a scale of relative importance, 1 reflecting equal weightages and 9

reflecting absolute importance (see appendix for detailed information).

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The steps to be followed in the AHP are given below (Roger, 1987):

1) Define the problem and determine the objective.

2) Structure the hierarchy from the top through the intermediate levels to the

lowest level. Refer to figure (Schematic of AHP).

3) Construct a set of pair-wise comparison matrices for each of the lower levels.

An element in the higher level is said to be a governing element for those in the

lower level, since it contributes to it or affects it. The elements in the lower

level are then compared to each other based on their effect on the governing

element above. This yields a square matrix of judgments. The pair-wise

comparisons are done in terms of which an element dominates another. These

judgments are then expressed as integers. If element A dominates over element

B, then the whole number integer is entered in row A, column B and reciprocal

is entered in row B, column A. If the elements being compared are equal, a

one is assigned to both positions. Table 2 shows the pair-wise comparison

matrix for level 2 criteria.

Table 2. Criteria pair-wise comparison matrix (level 2)

QULT VSBL TRST INVN DLRL FLRS RSUZ COST ASST TECB SERV TTMK

QULT

1/2 1 1/5 3 3 3 1 1 2 2 2

VSBL

1 2 3 2 4 1 2 1 2 2

TRST

1 1

1/2 1 3 2 2 1 1 1 2

INVN

5 1/2 2

5 3 6 3 2 2 3 3

DLRL

1/3 1/3 1 0.2

1 1 2 1/5 1/2 1/3 1

FLRS

1/3 1/2 1/3 1/3 1

2 1 1/3 1/2 1/4 3

RSUZ

1/3 1/4 1/2 1/6 1 1/2

1/2 1/5 1/9 1/2 1

COST

1 1 1/2 1/3 1/2 1 2

1/2 1/3 1/2 1

ASST

1 1/2 1 1/2 3 3 3 2

1/2 1 2

TECB

1/2 1 1 1/2 2 2 9 3 2

2 1

SERV

1/2 1/2 1 1/3 3 4 2 2 1 1/2

TTMK

1/2 1/2 1/2 1/3 1 1/3 1 1 1/2 1 1/2

4) There are n (n-1)/2 judgments required to develop the set of matrices in step 3

(reciprocals are automatically assigned in each pair-wise comparisons).

5) Having done all the pair-wise comparisons and entered the data, the consistency

is determined using the eigenvalue. To do so, normalize the column of

numbers by dividing each entry by the sum of all entries. The sum each row of

the normalized values and take the average. This provides Principal Vector

[PV]. Table 3 illustrates the normalized comparison matrix.

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The check of the consistency of judgments is as follows:

• Let the pair-wise comparison matrix be denoted M

and principal vector

be denoted M

• Then define M

= M

; and M

= M

• Calculate λmax = average of the elements of M

• Calculate Consistency Index (CI) = (λmax - N) / N – 1

• Consistency Ratio (CR) = CI/RCI corresponding to N

where RCI : Random Consistency Index and

N : Number of elements

Random index table

N 1 2 3 4 5 6 7 8 9 10

RCI 0 0 0.58 0.9 1.12 1.24 1.32 1.41 1.45 1.49

o If CR is less than 10%, judgments are considered consistent. And if CR is

greater than 10%, the quality of judgments should be improved to have

CR less than or equal to 10%.

6) Steps 3-5 are performed to have relative importance of each attribute for all

levels and clusters in the hierarchy. Table 4 illustrates the sub-criterion analysis

of criterion ‘cost’ (level III).

Table 3. Criteria pair-wise comparison matrix (level 2) normalized.

QULT VSBL TRST INVN DLRL FLRS RSUZ COST ASST TECB SERV TTMK SUM PV

QULT

0.074 0.066 0.092 0.031 0.122 0.126 0.083 0.051 0.085 0.191 0.142 0.095

1.160

0.097

VSBL

0.148 0.132 0.092 0.313 0.122 0.084 0.111 0.051 0.170 0.096 0.142 0.095

1.557

0.130

TRST

0.074 0.132 0.092 0.078 0.041 0.126 0.056 0.103 0.085 0.096 0.071 0.095

1.048

0.087

INVN

0.370 0.066 0.185 0.156 0.204 0.126 0.167 0.154 0.170 0.191 0.213 0.143

2.145

0.179

DLRL

0.025 0.044 0.092 0.031 0.041 0.042 0.028 0.103 0.017 0.048 0.024 0.048

0.541

0.045

FLRS

0.025 0.066 0.031 0.052 0.041 0.042 0.056 0.051 0.028 0.048 0.018 0.143

0.600

0.050

RSUZ

0.025 0.033 0.046 0.026 0.041 0.021 0.028 0.026 0.017 0.011 0.036 0.048

0.356

0.030

COST

0.074 0.132 0.046 0.052 0.020 0.042 0.056 0.051 0.043 0.032 0.036 0.048

0.631

0.053

ASST

0.074 0.066 0.092 0.078 0.122 0.126 0.083 0.103 0.085 0.048 0.071 0.095

1.044

0.087

TECB

0.037 0.132 0.092 0.078 0.082 0.084 0.250 0.154 0.170 0.096 0.142 0.048

1.365

0.114

SERV

0.037 0.066 0.092 0.052 0.123 0.168 0.056 0.103 0.085 0.048 0.071 0.095

0.995

0.083

TTMK

0.037 0.066 0.046 0.052 0.041 0.014 0.028 0.051 0.043 0.096 0.036 0.048

0.557

0.046

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Table 4. Cost sub-criteria analysis (level 3)

TLMC PCPL DFCT DBCT MFGC INVC WHEC ICSS ITGC OHDC SLTC

TLMC

1

1 1 1 1 3 4 2 1 1 3

PCPL

1

1 1 1 4 4 3 2 1 7

DFCT

1 1

1

1 1 3 4 2 8 2 8

DBCT

1 1 1

1

1 3 4 2 1 1 2

MFGC

1 1 1 1

1

3 3 2 1 1 5

INVC

1/3 1/4 1/3 1/3 1/3

1

1 1 1/2 1/4 1

WHEC

1/4 1/4 1/4 1/4 1/3 1

1

1/2 1/3 1/4 4

ICSS

1/2 1/3 1/2 1/2 1/2 1 2

1

1/2 1/3 1/3

ITGC

1 1/2 1/8 1 1 2 3 2

1

1/2 2

OHDC

1 1 1/2 1 1 4 4 3 2

1

SLTC

1/3 1/7 1/8 1/2 1/5 1 1/4 3 1/2

1/3

SUM

8.417 7.476 6.833 8.583 8.367 26 30.25 21.500 17.833 8.667

36.333

7) The alternative analysis for the lowest level of sub-criteria to be carried out in

the similar manner as above. Table 5 illustrates the alternative analysis of

Consistency. The remaining alternative analyses are to be carried out similarly.

Table 5. Alternative analysis for consistency (CNTY)

Supplier 1 Supplier 2 Supplier 3

Supplier 1

1

4 1

Supplier 2

1/4

1

1/7

Supplier 3

1 7

1

SUM

2.25 12.042 2.142

Table 6. Case Situation

Industry Type Automotive production

Supply chain strategy Somewhat responsive

Product type standard

Product strategy Moderate profit margin and supplier risk

Company vision To be a world-class company and market leader

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8) The desirability index for each alternative is calculated by multiplying each

value in ‘weight of sub-criteria’ column by the respective value of ‘criteria

weight’ column, then multiplying the obtained value, by the value for each

respective alternative and summing the results.

Highly user-friendly software, the multiattribute decision model, i.e. AHP is

developed in VC

to aid the user for pair-wise comparison of the attributes as well as

for the alternatives and for analysing the user inputs. The attributes are compared

with other in a pair-wise comparison with respect to the case situation discussed in

Table 6.

From the analysis, it is clear that Supplier 1 is the best under the circumstances of

the developed case situation (see Tables 7-9). The reliability of the judgments

supplied by the user can be estimated from graphs (Figs.2-4) that are generated for

each alternative and its corresponding deciding criteria. Fig 5 would depict the

composite overall scores for the alternative suppliers.

Table 7. Weightages of attributes for suppliers

Subcrit L3-wt L2-wt Supplier 1 Supplier 2 Supplier 3

CUST

0.0940 0.0967 0.2970 0.5396 0.1634

CURT

0.0990 0.0967 0.3333 0.3333 0.3333

LDTM

0.1000 0.0967

0.2402 0.5499 0.2098

OTDL

0.1060 0.0967

0.1192 0.7238 0.1570

FLRA

0.0840 0.0967

0.1744 0.6337 0.1919

STOP

0.0470 0.0967

0.4444 0.1111 0.4444

ACCY

0.0380 0.0967 0.3333 0.3333 0.3333

BACM

0.0580 0.0967

0.2500 0.5000 0.2500

PRFL

0.0880 0.0967 0.5400 0.3000 0.1600

PGRJ

0.1070 0.0967 0.5470 0.2630 0.1890

INMP

0.0540 0.0967 0.5840 0.2310 0.1840

WACL

0.0630 0.0967 0.4830 0.3480 0.1670

AVTE

0.0307 0.0967 0.5270 0.3320 0.1390

ATQM

0.0260 0.0967 0.6300 0.2180 0.1510

TIME

0.7500 0.1230

0.4099 0.3504 0.2397

ACCY

0.2500 0.1230 0.3333 0.3333 0.3333

CNTY

1.0000 0.0870

0.4145 0.0860 0.4995

NLPD

0.6660 0.1780

0.4126 0.3275 0.2599

NUTE

0.3340 0.1780

0.4430 0.3870 0.1690

DLPM

0.5579 0.0450

0.1365 0.2385 0.6250

FLRA

0.2316 0.0450

0.2158 0.6817 0.1025

OFLT

0.1187 0.0450 0.5930 0.2490 0.1570

POFM

0.0915 0.0450

0.6480 0.2270 0.1220

SCRT

0.7500

0.0500

0.2500 0.5000 0.2500

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PDFB

0.2500 0.0500

0.4286 0.1429 0.4286

MFGR

0.2560 0.0296

0.2000 0.6000 0.2000

STRS

0.1860 0.0296

0.2000 0.6000 0.2000

LGRS

0.2000 0.0296

0.5950 0.2760 0.1280

HMRS

0.2180 0.0296 0.6300 0.2610 0.1080

FLRS

0.1400 0.0296

0.3660 0.4970 0.1350

TLMC

0.1120 0.0525

0.1667 0.6667 0.1667

PCPL

0.1340 0.0525

0.2500 0.5000 0.2500

DFCT

0.1700 0.0525

0.1111 0.7778 0.1111

DBCT

0.1550 0.0525

0.5490 0.2400 0.2090

MFGC

0.1130 0.0525

0.2500 0.2500 0.5000

INVC

0.0360 0.0525

0.1700 0.4430 0.3870

WHEC

0.0380 0.0525 0.3333 0.3333 0.3333

ICSS

0.0470 0.0525 0.1670 0.3480 0.4830

ITGC

0.0790 0.0525

0.1700 0.4430 0.3870

OHDC

0.1180 0.0525

0.1420 0.4280 0.4280

SLTC

0.0400 0.0525

0.1690 0.3870 0.4430

CCCT

0.0700 0.0870 0.3400 0.3333 0.3333

ITDS

0.6040 0.0870 0.3400 0.3333 0.3333

ASTT

0.3250 0.0870 0.2210 0.3180 0.4900

PDPF

0.2800 0.1130

0.1700 0.6667 0.1667

SKMP

0.2840 0.1130 0.3400 0.3333 0.3333

CMSE

0.1550

0.1130 0.3400 0.3333 0.3333

CTEL

0.1800 0.1130

0.5500 0.2090 0.2400

NIDP

0.1100 0.1130 0.5840 0.1840 0.2310

OTDL

0.2700 0.0820

0.5840 0.1840 0.2310

BCMN

0.2850 0.0820

0.4120 0.3270 0.2590

RPCH

0.1550 0.0820

0.5490 0.2090 0.2400

DRFL

0.1800 0.0820

0.3270 0.4120 0.2590

CUSF

0.1090 0.0820

0.3100 0.4930 0.1950

DPMQ

0.3850 0.0463

0.6860 0.1860 0.1260

FMIP

0.2550 0.0463

0.3760 0.4740 0.1490

TMIA

0.1570 0.0463

0.3580 0.5170 0.1240

FDMC

0.2010 0.0463

0.1890 0.5470 0.2630

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Table 8. Data Summary

Subcrit Supplier 1 Supplier 2 Supplier 3

CUST

0.0027 0.0049 0.0015

CURT

0.0032 0.0032 0.0032

LDTM

0.0023 0.0053 0.0020

OTDL

0.0012 0.0074 0.0016

FLRA

0.0014 0.0051 0.0016

STOP

0.0020 0.0005 0.0020

ACCY

0.0012 0.0012 0.0012

BACM

0.0014 0.0028 0.0014

PRFL

0.0046 0.0026 0.0014

PGRJ

0.0057 0.0027 0.0020

INMP

0.0030 0.0012 0.0010

WACL

0.0029 0.0021 0.0010

AVTE

0.0016 0.0010 0.0004

ATQM

0.0016 0.0005 0.0004

TIME

0.0378 0.0323 0.0221

ACCY

0.0102 0.0102 0.0102

CNTY

0.0361 0.0075 0.0435

NLPD

0.0489 0.0388 0.0308

NUTE

0.0263 0.0230 0.0100

DLPM

0.0034 0.0060 0.0157

FLRA

0.0022 0.0071 0.0011

OFLT

0.0032 0.0013 0.0008

POFM

0.0027 0.0009 0.0005

SCRT

0.0094 0.0188 0.0094

PDFB

0.0054 0.0018 0.0054

MFGR

0.0015 0.0045 0.0015

STRS

0.0011 0.0033 0.0011

LGRS

0.0035 0.0016 0.0008

HMRS

0.0041 0.0017 0.0007

FLRS

0.0015 0.0021 0.0006

TLMC

0.0010 0.0039 0.0010

PCPL

0.0018 0.0035 0.0018

DFCT

0.0010 0.0069 0.0010

DBCT

0.0045 0.0020 0.0017

MFGC

0.0015

0.0015 0.0030

INVC

0.0003 0.0008 0.0007

WHEC

0.0007 0.0007 0.0007

ICSS

0.0004 0.0009 0.0012

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ITGC

0.0007 0.0018 0.0016

OHDC

0.0009 0.0027 0.0027

SLTC

0.0004 0.0008 0.0009

CCCT

0.0021 0.0020 0.0020

ITDS

0.0179 0.0175 0.0175

ASTT

0.0062 0.0090 0.0139

PDPF

0.0054 0.0211 0.0053

SKMP

0.0109 0.0107 0.0107

CMSE

0.0060 0.0058 0.0058

CTEL

0.0112 0.0043 0.0049

NIDP

0.0073 0.0023 0.0029

OTDL

0.0129 0.0041 0.0051

BCMN

0.0096 0.0076 0.0061

RPCH

0.0070 0.0027 0.0031

DRFL

0.0048 0.0061 0.0038

CUSF

0.0028 0.0044 0.0017

DPMQ

0.0122 0.0033 0.0022

FMIP

0.0044 0.0056 0.0018

TMIA

0.0026 0.0038 0.0009

FDMC

0.0018 0.0051 0.0024

Table 9. Decision index for desirability of each supplier

Decision index for supplier 1 0.373

Decision index for supplier 2 0.344

Decision index for supplier 3 0.283

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Fig 2. Data Summary graph for Supplier 1

0.0000

0.0100

0.0200

0.0300

0.0400

0.0500

0.0600

OTDL

PGRJ

VTE

ACCY

NUTE

OFLT

DFB

OHD

SKMP

NIDP

RPCH

DPMQ

FDMC

Sub-criteria

Principle vector

Fig. 3. Data Summary graph for Supplier 2.

0.0000

0.0050

0.0100

0.0150

0.0200

0.0250

0.0300

0.0350

0.0400

0.0450

NUTE

OFL

LGRS

TLMC

DBCT

WHEC

OHDC

ITDS

SKMP

Sub-criteria

Principle vector

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Fig.4. Data summary graph for Supplier 3.

0.0000

0.0050

0.0100

0.0150

0.0200

0.0250

0.0300

0.0350

0.0400

0.0450

0.0500

CUST

LDTM

FLRA

ACCY

PRFL

INMP

AVTE

TIME

CNTY

NUTE

FLRA

POFM

PDFB

STRS

HMRS

TLMC

DFCT

MFGC

WHEC

ITGC

SLTC

ITDS

PDPF

CMSE

NIDP

BCMN

DRFL

DPMQ

TMIA

Sub-criteria

Principle vector

Fig.5. Composite overall scores for alternative Suppliers

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

Supplier 1. Supplier 2. Supplier 3.

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Conclusion

The proposed approach forms a flexible and systematic decision framework for

selection of supplier in competitive supply chain. The proposed decision framework for

evaluating and selection of supplier enables decision makers to provide the base for the

final choice from the overall potential suppliers. In this paper, the framework is

evaluated by a case situation but the framework is applicable to different types of

industries by allowing managers to structure their problems into priority weights, which

can reflect their own priority considerations.

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Notes

Appendix

Scale of relative importance

Intensity Definition Explanation

2,4,6,8

Equal Importance

Weak importance of one over the

other

Essential or strong

Very strong

Absolute importance

Intermediate values

Two activities contribute equally to the

objective

Experience and judgment slightly favor one

another

Experience and judgment slightly favor one

another

An activity is strongly importance favored and

its dominance is demonstrated in practice

The evidence favoring one activity over

another is of the highest degree

When compromise is needed

Avinash D. Sarode is currently working as an Asst. Professor in the Mechanical

Engineering Department at Lokmanya Tilak College of Engineering, Koperkhairane,

Navi Mumbai, India, and has more than 10 years of teaching experience. He has

published many research papers in national and international conference as well as

journals. Currently he is pursuing doctoral program from Sant Gadge Baba Amravati

University, Amravati. His research area is supply chain management and production

technology, for two years he is recipient of research grant from University of Mumbai.

Prakash M. Khodke is currently serving as a Professor and Principal of Government

College of Engineering, Amravati, India. He has published number of papers in various

national and international journals and has participated and organized a number of

conferences. His research area is operation research, production technology, supply

chain management, he is having 20 years of graduate and post graduate experience in

teaching. He is guiding a number of students for their doctoral research.

Corresponding author: Avinash D. Sarode, Asst. Professor, Lokmanya Tilak College of

Engineering Koperkhairane, Navi Mumbai 400 709 (India), Phone: - (+91-22)

27541005/1006; Fax: - (+91-22) 27547787; E-mail: adsarode@rediffmail.com