topsis based swot analysis for the lean strategy selection...

For Peer Review

An integrated analytic network process and modified

TOPSIS based SWOT analysis for the lean strategy selection in foundry industry

Journal: Part B: Journal of Engineering Manufacture

Manuscript ID: JEM-15-0508

Manuscript Type: Original article

Date Submitted by the Author: 31-Jul-2015

Complete List of Authors: Prasad, Suresh; National Institute of Technology, Kurukshetra (Research

Scholar), Mechanical Engineering Department Sharma, Surrender; National Institute of Technology, Kurukshetra (Former Professor), Mechanical Engineering

Keywords: Manufacturing Management < Optimisation, Multiple Criteria Decision-Making, SWOT Analysis, ANP, TOPSIS

Abstract:

The purpose of this article is to deal with the lean strategy selection process in Indian foundry industry by using Strengths, Weaknesses, Opportunities and Threats (SWOT) analysis aimed at determining strategies and for providing an initial decision framework. It involves specifying of the objective of an industry and identification of internal and external factors, its sub-factors and strategies, which are either favourable or unfavourable to achieve the stated objective. However, the SWOT

method does not provide any logical way to assess the priorities of the identified strategies. In order to overcome this limitation, this study presents two multiple criteria decision-making (MCDM) methods, analytical network process (ANP) and modified technique for order of preference by similarity to ideal solution (TOPSIS), for providing a quantifiable basis to analytically ascertain the ranking of criteria, sub- criteria and strategies in SWOT analysis. At first, lean strategies are determined on the basis of SWOT analysis, followed by the calculation of priorities of the SWOT criteria and sub-criteria using ANP, and finally the priorities of strategies are analysed through the modified TOPSIS. The results shows that the quantitative SWOT analysis based approach is a feasible and exceedingly capable method that provides vital sensitivity for selecting lean strategy in

the Indian foundry industry, and can be employed as an effective method for many other complex decision-making processes as well.

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Journal of Engineering Manufacture

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An integrated analytic network process and modified TOPSIS based

SWOT analysis for the lean strategy selection in foundry industry

Suresh Prasada,*, Surrender K. Sharmab,

a Research Scholar, Mechanical Engineering Department, National Institute of Technology,

Kurukshetra, India b Mechanical Engineering Department (Former Professor), National

Institute of Technology, Kurukshetra, India

*Corresponding author. E-mail: [email protected]

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An integrated analytic network process and modified TOPSIS based

SWOT analysis for the lean strategy selection in foundry industry

Abstract

The purpose of this article is to deal with the lean strategy selection process in Indian foundry

industry by using Strengths, Weaknesses, Opportunities and Threats (SWOT) analysis aimed

at determining strategies and for providing an initial decision framework. It involves

specifying of the objective of an industry and identification of internal and external factors,

its sub-factors and strategies, which are either favourable or unfavourable to achieve the

stated objective. However, the SWOT method does not provide any logical way to assess the

priorities of the identified strategies. In order to overcome this limitation, this study presents

two multiple criteria decision-making (MCDM) methods, analytical network process (ANP)

and modified technique for order of preference by similarity to ideal solution (TOPSIS), for

providing a quantifiable basis to analytically ascertain the ranking of criteria, sub- criteria and

strategies in SWOT analysis. At first, lean strategies are determined on the basis of SWOT

analysis, followed by the calculation of priorities of the SWOT criteria and sub-criteria using

ANP, and finally the priorities of strategies are analysed through the modified TOPSIS. The

results shows that the quantitative SWOT analysis based approach is a feasible and

exceedingly capable method that provides vital sensitivity for selecting lean strategy in the

Indian foundry industry, and can be employed as an effective method for many other

complex decision-making processes as well.

Keywords: Lean strategy selection; MCDM; SWOT analysis; ANP; TOPSIS; foundry

industry

1. Introduction

The twentieth century was marked by the development of several advanced manufacturing

strategies which were beginning to transform the traditional approaches due to intense global

competition, rapid technological changes, and advances in manufacturing and information

technology for improving quality and productivity, and for the optimisation of manufacturing

processes which will enable manufacturers to deliver high-quality products in a short period

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of time.1 Lean philosophy, which originated from the Toyota production system (TPS), is one

of the initiatives that many businesses have been trying to implement so as to minimise

wastage of resources, eliminate non-value added activities and focus on cost reduction.2, 3 In

India, many foundries have been trying to adopt lean manufacturing (LM) in order to stay

alive in todays competitive marketplace by improving productivity and operational

performance. The foundries can become economically and environmentally sustainable

industry only when the strategies proposed by LM system are implemented in an appropriate

way.4

Bhasin 5 stated that any strategy, regardless of its strengths, will not be accepted if it is

outside the bounds of an organisations culture. Existing methods for selecting the

appropriate lean strategy relies on the manufacturers common sense of judgement rather than

any sequence of analytical justification.1 SWOT analysis is a commonly implemented method

to systematically analyse an organisations internal and external factors, and is capable of

formulating strategies based on these factors. Therefore, for identification of lean strategies,

SWOT analysis of the industry can be helpful since almost every function within the

organisation is influenced by the internal and external factors. However, the qualitative

SWOT analysis method is not without limitations as it does not provide an analytical means

to determine the relative importance of the decisive factors or the ability to assess the

relevance of defined alternatives based on these factors.6, 7 In order to overcome this

limitation, researchers employed strategic decision-making models which would consider

multiple criteria in their analysis, such as analytic hierarchy process (AHP) 8 or the technique

for order of preference by similarity to ideal solution (TOPSIS).9 AHP is an undeniably

efficient method as it makes the best judgement between both tangible and intangible aspects

of a decision. However there is a major drawback, it is incapable of measuring possible

interdependencies among the criteria, as these criteria are often dependent with each other.6, 10

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The analytical network process (ANP) is a comparatively new and improved version of AHP

which could be employed for solving various complex decision-making problems involving

feedback approach and can easily represent many complicated relationships.

In this article, we applied SWOT analysis to determine lean strategies based on the

internal and external factors, and two multiple criteria decision-making (MCDM) methods,

i.e., the ANP and the modified TOPSIS, for obtaining the relative importance of each

strategy. The approach in this article is to use ANP for the calculation of priorities of the

SWOT criteria and sub-criteria, and modified TOPSIS for assessing the priorities of lean

strategies in the Indian foundry industry. Thus, we developed a methodology that facilitates

finding the priorities of the SWOT criteria, sub-criteria and lean strategies. Therefore, to the

best of our knowledge, we applied integrated ANP and modified TOPSIS for the first time to

assess the lean strategies of foundry industry in India.

This article is structured as follows. The literature review is provided in section

Literature review. Section Multiple criteria decision-making (MCDM) methods describes

the research methodology of ANP and modified TOPSIS. Section Proposed research

methodology for lean strategy selection analyses the lean strategies of Indian foundry

industry so as to provide decision aid to these foundries in developing their strategies.

Finally, the conclusions of this article is discussed in Section Conclusion.

2. Literature review

With the advent of liberalization and globalization, the products and processes have

undergone a lot of changes and manufacturing companies are facing a tough competition in

all aspect of business. In todays competitive environment, lean manufacturing (LM)

strategies are the most powerful strategies for achieving operational and service excellence in

manufacturing industries. Lean concept has evolved as a philosophy with the motto to do

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more with less aimed at elimination of non-valued added activities in every aspects of

industry while adding value to the product with a systematic and continuous approach.11

According to Shah and Ward 12, LM can be defined as an integrated socio-technical system

whose main objective is to eliminate waste by concurrently reducing or minimizing supplier,

customer, and internal variability. LM has originated from Toyota with the title given by few

researchers as Toyota Production System (TPS),13 or just-in-time (JIT) production14, 15 in the

1960s. The reason behind this might be that both these systems aims to increase the value-

added work by eliminating waste from the systems and operations, reducing incidental work,

and extracting as much output as they can acquire from lesser inputs,14 which makes LM

highly synonymous with JIT production. Monden 15 stated that any process inside a

manufacturing facility can be classified as incidental activity, value-adding (VA) activity

and/or non-value-adding (NVA) activity. Russell and Taylor 16 defined waste as anything

other than the minimum amount of equipment, effort, materials, parts, space and time that is

essential to add value to the product, and for which the customer is unwilling to pay for. The

most commonly identified NVA waste categories in any industry include over-production,

waiting of equipment and human resources, transportation, inventory, motion, defects, and

over-processing.17

In the pursuit for improvement in quality and equipment productivity, organizations

have practiced revolutionary programs or quality initiatives such as total quality management

(TQM) and continuous improvement methodologies such as Kaizen.18 Bayazit and Karpak 19

described TQM as an integrated management philosophy aimed at continuously refining the

performance of products, processes and services for meeting or exceeding on customers

expectations. The aim of a continuous improvement program is to continuously recognize

and reduce the extent of waste in a system since it is essential to identify and separate waste

from incidental and VA work.20 LM is governed by a pull type production control system

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where the parts are pulled from upstream work centres to downstream work centres as they

are needed. Pull production system is a production monitoring technique for JIT production

which makes full use of workers capabilities.21 Eswaramoorthi, Kathiresan 22 stated that the

main cause for low level of lean implementation is anxiety in changing the mind-set of

workers for adaption to varying manufacturing circumstances. Porter and van der Linde 23

proposed in their study that organizations can develop some capabilities through their

environmental effort, which translates into competitive advantage leading to higher

profitability. Moreover, Yang, Hong 24 investigated the relationships between LM practices,

environmental management and business performance outcomes in the context of

manufacturing industry. Their research reveals that, on the one hand, LM practices have

positive effects on environmental management practices, while on the other hand, isolated

operation of environmental management practices have negative effects on market and

financial performance. Furthermore, the firm level strategic commitment for LM and

environmental management requires well-communication and understanding by issuing

comprehensive sustainability reports.

However, evaluating LM strategies is a complex task which does not only involve a

trade-off between strengths and weaknesses entailed but also takes opportunities and threats

into consideration. Hisrich and Peters 25 stated that responding to internal strengths and

weaknesses is a fundamental constituent of the strategic management process. Several

strategic management approaches have been developed to solve such sort of real-life

problems. SWOT analysis, which was originally described by Learned, Christensen 26, is one

of the essential methods to address complex strategic situations by reducing the magnitude of

information which improves decision-making. SWOT analysis is a prevalent method of

strategic planning and is commonly implemented to provide a basic framework for

identifying the internal and external factors, and formulating strategies.27 Using the SWOT

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analysis, any entity or individual can determine the way to leverage its strength, overcome its

weaknesses, seize opportunities and elude hypothetically detrimental threats or nonetheless

scrutinise them through more consistent perusing.28-30 Despite its advantages and usages to

strategists, SWOT analysis is often criticised because of its inability of not providing any

analytical method for ascertaining the relative importance of the decisive SWOT criteria or

the ability to assess the importance of defined feasible alternatives based on these criteria.

Therefore, researchers employed strategic decision-making models which would assess the

relative importance of SWOT criteria and sub-criteria on the strategies by incorporating

AHP, known as the SWOT-AHP method.8, 31, 32 Since the selection of criteria for alternatives

may interact with each other and not be independent in some cases, few researchers like

Ekmekioglu, Can Kutlu 9 and Yang 33 employed the SWOT-TOPSIS method. Although the

AHP and the classical TOPSIS method have proven their efficacy in dealing with the MCDM

and their simplicity of implementation, they do possess some sort of limitations. First of all,

the AHP provides a quantifiable basis and hierarchical structure to the SWOT analysis

framework, it lacks the ability of encapsulating potential interactions, interdependencies, and

feedbacks amongst the SWOT criteria. In order to overcome this drawback, the researchers

developed an ANP based SWOT-model.6, 7, 34 Secondly, on the other hand, the SWOT-

TOPSIS method possesses an inherent difficulty of assigning reliable subjective preferences

to the criteria even though the concept of TOPSIS is rational and reasonable, and the

computation involved is uncomplicated. Therefore, in order to overcome this limitation, the

modified TOPSIS method proposed by Deng, Yeh 35 could be applied. The modified TOPSIS

uses a new defined weighted Euclidean distance and ranks the alternatives in terms of their

overall performance with respect to the weighted criteria.

Ho 36 reviewed the literature appearing in the international journals from 1997 to

2006, providing the evidence that the integrated AHPs are better than the stand-alone AHP

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and also expressed that the five commonly integrated tools with the AHP include

mathematical programming, quality function deployment (QFD), meta-heuristics, data

envelopment analysis (DEA) and Strengths, Weaknesses, Opportunities and Threats (SWOT)

analysis. More recently, Azimi, Yazdani-Chamzini 10 had developed a SWOT model in three

stages. In their study, they used SWOT analysis to determine alternative strategies, then ANP

was applied in order to obtain the priority of SWOT criteria and sub-criteria, and ranked the

strategies using the TOPSIS. The outcome of their study was distinguishing between the

efficient and inefficient strategies. Although these methods have produced new insights into

the literature and deserve merit in terms of their analytical means for ascertaining the ranking

of SWOT criteria, they still possess a major limitation: ignoring the intrinsic intricacy of

determining consistent subjective preferences to the criteria. To simultaneously overcome

these limitations, we propose an integrated ANP and modified TOPSIS based SWOT

methodology which would fill the above mentioned limitation in the literature. The proposed

methodology may provide organisations a systematic approach to formulate and enhance on

adequate criteria, and minimise the risk of selecting sub-optimal strategies.

3. Multiple criteria decision-making (MCDM) methods

MCDM refers to the process of problem solving for finding the best alternative that is

employed to solve decision problems involving selection from among a set of feasible and

finite number of alternatives. These methods often involve experts to provide qualitative

and/or quantitative judgements for defining the performance of each alternative with respect

to criteria, and the relative importance of criteria with respect to the overall objective or goal.

The advantage of most MCDM methods is that they possess the ability of simultaneously

analysing both qualitative and quantitative evaluation criteria. The ANP and modified

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TOPSIS are both logical and rational decision-making methods, which deal with problems of

selecting the best alternative from a set of feasible alternatives.

In this study, the main objective is to develop an integrated methodology by using

SWOT based MCDM methods for solving the lean strategy selection problem. Therefore,

these methods are briefly described in the following subsections.

3.1 Analytic network process

ANP is the generalised form of the AHP and it is used to solve various complex decision

problems involving feedback, interactions and interdependencies in the decision-making

system with more accuracy and precision. In the AHP approach, the analytical aspects of a

decision problem are decomposed into an independent unidirectional hierarchy structure with

overall goal or objective at the top level of the hierarchy, followed by criteria and sub-criteria

at the middle level and feasible alternatives at the bottom level. However, several decision

problems cannot be designed hierarchically as they involve dependence and interaction of

higher-level elements on a lower-level element.37 ANP does not presume this independence

among distinct levels of criteria and within the level of a hierarchy. It structures a network

without levels, which signifies that certain element may exhibit influence over certain others.

A comparison of structure and super matrix between AHP and ANP methods is presented in

Figure 1. ANP is effective in assisting the mind of analysts to systematise its experiences and

views to elicit judgements recorded in memory and quantify them in form of priorities.38

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Figure 1. A comparison of the structure and super matrix between AHP and ANP.

As is apparent from Figure 1, ANP involves a system-with-feedback approach since it

includes both internal and external relationships with feedbacks, and thus making it possible

for the elements in a cluster to either influence some or all of the elements of same or another

cluster. External relationship indicates dependence of elements of a cluster with same or other

clusters elements. Internal relationship, which is shown by a looped arc, relates to the

dependence of an element of a cluster among other elements in the same clusters integrated

with feedback. Eliciting priorities of elements of each cluster requires pairwise comparison of

elements of clusters with respect to their upper level control criterion. The priorities of

elements of each cluster for internal relationship are obtained by comparing it with respect to

their influence on other elements within their own cluster. Pairwise comparisons of elements

in a cluster are made for external relationship by comparing them among elements of other

clusters to which they are connected. These pairwise comparison are made systematically by

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using the fundamental scale of absolute numbers, as tabulated in Table 1.39 By means of the

super matrix, the influences of interdependence that exists between the criteria and sub-

criteria of the system can be determined. The super matrix is a partitioned matrix which

represents priorities obtained from the pairwise comparison of the elements that are arranged

hierarchically into the appropriate columns of the matrix. As is represented in Figure 1, is the super matrix whose elements represents clusters, where, is an element which signifies the influence of the goal on the criteria, is an element which signifies the influence of the criteria on the sub-criteria, is an element which signifies the influence of the sub-criteria on the defined alternatives, and I represents the identity matrix. The dependence and

feedback amongst the elements of criteria and sub-criteria are signified by and , respectively.

Table 1. Pairwise comparison scale of absolute numbers.

Intensity of importance Definition 1 Equal importance 3 Moderate importance 5 Strong importance 7 Demonstrated importance 9 Absolute importance 2,4,6,8 Intermediate values for compromise between the above values Reciprocals of above

If activity i has one of the above non-zero numbers assigned to it when compared with activity j, then j is assigned the reciprocal value when compared with i

Note that the elements of the super matrix have to be raised to arbitrarily large powers

by taking the necessary limit in order to obtain the limit matrix. This matrix is inclusive of the

final priority required to attain a set of long-lasting stable weights. Higher values in the final

priorities conveys the higher desirability of that alternative. The selection of the best

alternative depends on the calculation of the desirability index, for an alternative i and

determinant a, can be obtained as defined by Meade and Sarkis 40.

3.2 Modified TOPSIS

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The TOPSIS is a multiple criteria decision-making (MCDM) method proposed by Hwang

and Yoon 41, based on the concept that the chosen alternative should have the shortest

distance from the positive-ideal solution and the longest distance from the negative-ideal

solution. In the classical TOPSIS method, the elements of the normalised decision matrix are

weighted by multiplying each column of the matrix by its associated criterion weight. The

priority of an alternative is then determined by its Euclidean distances to the positive-ideal

and the negative-ideal solutions. Conversely, in the modified TOPSIS presented by Deng,

Yeh 35, these distances are interconnected with criterion weights and should be incorporated

in the distance measurement. Since all alternatives are compared with the positive-ideal and

the negative-ideal solutions, instead of directly comparing among themselves. The modified

TOPSIS method uses the weighted Euclidean distances instead of representing weighted

decision matrix. It is required to establish a decision matrix based on all the information

available on criteria, which can be structured with ith alternative in each row, = 1,2, ; each column is assigned to a criterion, = 1,2, , ; and represents a crisp value indicating the performance of an alternative with respect to a criterion.

Geometric mean could be employed to group multiple opinions of experts into a

single judgement. Considering a group of k experts, an element of decision matrix from each

expert can be aggregated by taking the geometric mean to attain the group importance weight

of that element, as shown in Eq. (7).

= / (7) The elements of the normalised decision matrix R (= ) can be calculated by using

the vector normalisation method as:

= !" !$% &' , = 1,2, ,, = 1,2, , (8)

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The positive-ideal solution () and the negative-ideal solution (* from the normalised decision matrix can be expressed as:

() = +max / 03, min / 036| = 1,2, ,8 = 9), ), , ): (9) (* = +min / 03, max / 03| = 1,2, ,8 = 9*, *, , *: (10) where, 3 =

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The proposed research methodology of this article is based on the integrated approach of

ANP and modified TOPSIS based SWOT analysis. Thus, the problem is decomposed into a

network decision-making model such that the priorities of lean strategies can be measured

based on the identified criteria and sub-criteria as shown in Figure 2. The overall goal of

selection of the best lean strategy was placed at the top level of the model, followed by

SWOT criteria and SWOT sub-criteria at the second and third level, respectively. As can be

seen from Figure 3, each SWOT criterion includes five sub-criteria. Therefore, a total of 20

sub-criteria were identified. The bottom level of the model consists of feasible eight lean

strategies developed for this study. For the sake of simplicity, eight potential lean strategies

identified in this study are abbreviated as S1, S2, S3, S4, S5, S6, S7, and S8 in following

discussion. As the key steps of this study involves the identification of SWOT criteria, sub-

criteria and lean strategies, it provides a framework for obtaining priorities of identified

criteria and sub-criteria by using the ANP. Finally, the ranking of identified lean strategies

can be obtained by using the modified TOPSIS. Figure 3 shows the flowchart for the

proposed decision-making model. Table 2 presents all the criteria, sub-criteria and lean

strategies used.

Three stages are proposed, in order to implement the proposed methodology, which

are described as follows. The first stage involves analysis of the organisation for SWOT. In

this manner, strategically important SWOT sub-criteria, i.e., the internal and external factors,

which significantly affect the success of the organisations future goals are identified and

determined. The second stage involves the determination of priorities of criteria and sub-

criteria by using the pairwise comparison of ANP. The last stage involves the ranking of lean

strategies and selection of the optimal strategy by using the modified TOPSIS method.

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Figure 2. Proposed decision-making model for lean strategy selection.

Figure 3. Flowchart of the proposed decision-making model.

Table 2. LM strategy selection framework for the foundry industry.

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SWOT criteria and its sub-criteria Lean strategies

Internal factors

Strengths (S) S1. Most of the raw materials are easily available

from the local suppliers S2. Company believes in cleanliness of the shop

floor and is quite concerned about the environment

S3. State-of-the-art production technologies are used, which leads to high productivity and less pollution

S4. High level of support from the top management for introducing lean concepts

S5. Committed manpower and team spirit

A1. Identify wastes in the manufacturing system and restructure manufacturing processes and layout

A2. Take management support for using better technology

A3. Collaborate with customer and supplier in product development processes

A4. Share production planning and forecasting knowledge with customers and suppliers

A5. Consider employee suggestions on products and processes improvement

A6. Undertake programs for quality improvement and control and for the improvement of equipment productivity

A7. Undertake programs to improve environmental performance of processes and products

A8. Undertake actions to implement pull production system

Weaknesses (W) W1. Lack of Management Information System

(MIS) for inventory control W2. Lack of communication between management

and workers W3. Lack of skill up-gradation training and formal

training for workers and managers W4. Anxiety in changing the mind-set of workers to

adapt varying circumstances W5. Lack of senior managements committed

leadership

External factors

Opportunities (O) O1. Rise in demand of casting products at local and

national level O2. Growing environmental concern in foundries of

western countries provides opportunities for sourcing of castings from developing countries like China and India.

O3. Low labour costs in India O4. Technology available for castings are well

established in India O5. Improvement in economic and political

relations with different countries Threats (T)

T1. Looming shortage of skilled workers and trained engineers in foundry industry

T2. Foundries are forced to invest more in R&D to increase productivity and reduce pollution

T3. Producing the castings with limited effects on the environment under present technological and economic challenges

T4. Other competitors, in particular China and United States are emerging as potential supplier in global market due to better technology and infrastructure

T5. Fluctuations of raw materials prices in the market

4.2 Analysis of criteria and sub-criteria priorities using ANP

In this section, ANP is used for evaluating priorities of SWOT criteria and sub-criteria.

Assuming independence among the SWOT criteria, a pairwise independent comparison

matrix is formed among the criteria with respect to the overall goal by using pairwise

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comparison scale. The pairwise comparison matrix has been analysed as shown in Table 3,

and the following priorities () are obtained.

21

0.410

0.269

0.212

0.109

S

Ww

O

T

= =

Table 3. Pairwise comparison of SWOT criteria with independence among them.

SWOT criteria S W O T Priorities of SWOT criteria

S 1 2 2 3 0.410 W 1 2 2 0.269 O 1 3 0.212 T 1 0.109

Internal dependence among the SWOT criteria is then determined by evaluating the influence

of each criterion on other criteria. Since, it is unrealistic to assume the SWOT criteria as

independent, the existence of internal dependence among these criteria is modelled more

realistically through the ANP approach. The pairwise comparison matrices are formed for the

SWOT criteria based on the internal dependencies by using pairwise comparison scale as

shown in Tables 4-7. The internal dependence matrix of the SWOT factors () is formed using the obtained internal dependence priorities of each criterion.

Table 4. The internal dependence matrix of the SWOT factors with respect to strengths.

S W O T Priorities W 1 1/5 0.094 O 1 3 0.627 T 1 0.279

Table 5. The internal dependence matrix of the SWOT factors with respect to weaknesses.

W S O T Priorities S 1 4 3 0.630 O 1 2 0.218 T 1 0.152

Table 6. The internal dependence matrix of the SWOT factors with respect to opportunities.

O S W T Priorities S 1 5 6 0.729 W 1 0.162

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T 1 0.109

Table 7. The internal dependence matrix of the SWOT factors with respect to threats.

T S W O Priorities S 1 7 4 0.705 W 1 1/3 0.084 O 1 0.211

22

1 0.630 0.729 0.705

0.094 1 0.162 0.084

0.627 1 0.211

0.279 0.152 0.109

0.2 8

1

1W

=

The interdependent priorities (CDEFGDEH) of the SWOT criteria are calculated as follows:

22 21

0.405

0.176

0.275

0.144

criteriaw W w

= =

The local priorities of the SWOT sub-criteria are calculated by using the pairwise

comparison matrices as follows:

( )

1 1/ 3 1/ 7 1/ 5 1/ 3 0.044

1 1/ 5 1/ 4 1/ 2 0.082

1 2 8 0.483

1 5 0.295

1 0.096

criterisub stra engthsw

= =

( )

1 5 1/ 2 1/ 5 3 0.137

1 1/ 7 1/ 9 1/ 3 0.033

1 1/ 3 6 0.247

1 8 0.523

1 0.060

criteriasub weaknessesw

= =

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( )

1 2 3 3 5 0.372

1 3 5 7 0.322

1 4 7 0.187

1 3 0.080

1 0.039

sub opportucrite nitiesriaw

= =

( )

1 3 5 1/ 3 7 0.264

1 2 1/ 5 5 0.118

1 1/ 7 3 0.068

1 9 0.516

1 0.034

sub thrcriteria eatsw

= =

The global priorities of the SWOT sub-criteria IJK*CDEFGDEH

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amongst the SWOT sub-criteria by inputs from each expert. The multiple opinions on

elements of decision matrix obtained are aggregated into the group importance rating by

taking the geometric mean, using the Eq. (7) and the aggregated rating of each lean strategies

with respect to the sub-criteria was obtained as shown in Table 9. Then, the elements of the

normalised decision matrix are calculated for each element of the aggregated decision matrix

using the vector normalisation method using Eq. (8), and the normalised decision matrix is

formed as shown in Table 10. By using the Eq. (9) and (10), the positive-ideal and the

negative-ideal solutions are determined for each SWOT sub-criteria from the normalised

decision matrix. The weighted Euclidean distances of each lean strategy are calculated from

the positive-ideal and the negative-ideal solutions by using the Eq. (11) and (12) and the

obtained distances are shown in Table 11. Finally, the relative closeness coefficient ? of each lean strategy to the ideal solutions are calculated by using the Eq. (13) and are relatively

listed in Table 11. The relative closeness coefficient indicates the most and the least

preferable lean strategies.

In addition to this, the same model has been applied and analysed with the ANP based

TOPSIS method. In order to compute the ANP based TOPSIS satisfaction values, the data

obtained from the weighted decision matrix has been used. It is specified that the results

obtained by the application of the modified TOPSIS may perhaps vary from those calculated

by the classical TOPSIS method. The modified method, however, maybe considered to

provide better and more reliable results, because of its analytical derivation in view of the

weighted Euclidean distances.

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Table 9. The aggregated rating of the lean strategies with respect to the sub-criteria.

S1 S2 S3 S4 S5 W1 W2 W3 W4 W5 O1 O2 O3 O4 O5 T1 T2 T3 T4 T5

A1 2.25 7.28 5.27 3.65 4.38 5.12 4.45 3.25 2.13 5.37 8.21 7.73 2.37 5.42 1.79 4.38 7.85 3.34 8.53 2.94

A2 1.67 3.56 8.64 5.53 4.27 7.83 6.25 5.38 7.13 8.17 6.52 6.43 4.89 8.35 2.47 3.77 4.92 6.23 8.06 3.67

A3 6.91 3.47 5.57 7.31 2.35 3.52 1.96 3.68 3.35 4.03 7.28 8.67 1.77 3.67 7.17 2.54 5.37 6.94 8.74 5.89

A4 6.57 2.65 6.38 7.45 2.19 6.37 2.15 2.57 3.97 3.53 6.23 7.93 2.85 7.95 5.15 3.65 4.67 4.84 7.23 8.35

A5 2.81 8.43 6.86 8.93 5.15 3.16 8.42 7.19 6.75 4.27 2.27 3.28 5.07 5.81 1.97 4.15 3.31 4.41 2.19 1.09

A6 3.62 7.35 8.38 6.75 4.78 7.87 5.37 7.03 8.26 6.67 7.15 6.49 4.37 8.78 4.63 7.35 6.53 7.27 7.79 2.43

A7 7.25 8.09 6.25 5.93 2.13 4.93 3.63 5.47 2.31 4.49 5.79 5.35 2.23 6.39 1.47 2.63 7.45 8.05 5.84 3.95

A8 8.71 6.55 7.29 8.78 5.47 8.09 5.68 6.92 7.56 6.59 8.45 7.82 4.76 7.93 3.35 6.72 4.59 6.97 8.35 8.18

Table 10. Normalised decision matrix.

S1 S2 S3 S4 S5 W1 W2 W3 W4 W5 O1 O2 O3 O4 O5 T1 T2 T3 T4 T5

A1 0.143 0.408 0.269 0.185 0.382 0.295 0.305 0.210 0.132 0.339 0.431 0.396 0.223 0.274 0.160 0.330 0.481 0.191 0.409 0.200

A2 0.106 0.200 0.441 0.280 0.372 0.450 0.429 0.348 0.443 0.516 0.342 0.329 0.460 0.423 0.220 0.284 0.301 0.355 0.386 0.250

A3 0.439 0.195 0.284 0.370 0.205 0.202 0.134 0.238 0.208 0.255 0.382 0.444 0.166 0.186 0.639 0.191 0.329 0.396 0.419 0.401

A4 0.417 0.149 0.326 0.377 0.191 0.366 0.147 0.166 0.247 0.223 0.327 0.406 0.268 0.402 0.459 0.275 0.286 0.276 0.346 0.568

A5 0.178 0.473 0.350 0.452 0.449 0.182 0.578 0.465 0.420 0.270 0.119 0.168 0.476 0.294 0.176 0.312 0.203 0.252 0.105 0.074

A6 0.230 0.412 0.428 0.342 0.417 0.453 0.368 0.455 0.514 0.421 0.375 0.332 0.411 0.444 0.413 0.553 0.400 0.415 0.373 0.165

A7 0.460 0.454 0.319 0.300 0.186 0.284 0.249 0.354 0.144 0.284 0.304 0.274 0.210 0.323 0.131 0.198 0.456 0.459 0.280 0.269

A8 0.553 0.367 0.372 0.444 0.477 0.465 0.390 0.448 0.470 0.416 0.444 0.400 0.447 0.401 0.299 0.506 0.281 0.398 0.400 0.556

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Table 11. Closeness coefficients and ranking of lean strategies with ANP based TOPSIS and

ANP based modified TOPSIS methods.

Alternatives ANP based TOPSIS method ANP based modified TOPSIS method Si

+ Si- Ci Rank Di

+ Di- Ci Rank

A1 0.0549 0.0550 0.5002 7 0.1847 0.1990 0.5185 5 A2 0.0466 0.0499 0.5167 5 0.1850 0.1631 0.4686 7 A3 0.0464 0.0550 0.5421 3 0.1649 0.2069 0.5565 2 A4 0.0393 0.0508 0.5639 2 0.1487 0.1896 0.5604 1 A5 0.0539 0.0503 0.4824 8 0.1922 0.1926 0.5005 6 A6 0.0490 0.0509 0.5095 6 0.1919 0.1687 0.4679 8 A7 0.0438 0.0488 0.5273 4 0.1622 0.1850 0.5329 3 A8 0.0450 0.0583 0.5643 1 0.1771 0.2020 0.5328 4

5. Conclusions

The purpose of this article has been to evaluate lean strategies as a MCDM problem by using

integrated ANP and modified TOPSIS based on the SWOT analysis. This research explores

and identifies sub-criteria in order to generate a basic hierarchical model for analysing lean

strategies by using SWOT analysis. To find out the best lean strategy for the foundry

industry, we proposed a new integrated method for the first time based on the ANP and

modified TOPSIS. Thus, an integrated evaluation system has been designed to provide

practitioners a point of view to construct a SWOT model for ascertaining the relative

importance of the SWOT sub-criteria and to assess the lean strategies based on these sub-

criteria. By quantitatively comparing our method with classical TOPSIS approach, we have

shown that the proposed method successfully contributes to the knowledge in the

development of a systematic methodology and enables decision makers to understand the

complete evaluation process of lean strategy selection problem. Managerially, this article

provides a novel approach to examine various lean strategy using decision-making methods.

Furthermore, this approach provides a more accurate, effective, and systematic decision

support tool. Finally, it is recommended that managers of the foundry industry can utilize this

model to evaluate their organisations strengths, weaknesses, opportunities and threats to

prioritise the strategies for further development and higher productivity.

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Regardless of the benefits summarised, there are some limitations. This paper could

be extended by using intuitionistic fuzzy sets in conjunction with ANP and modified TOPSIS

based SWOT methodology to capture possible uncertainty. The refinement of the proposed

approach for sophisticated modelling would be an appropriate approach for future research. It

is also suggested that future research may include the application of the proposed

methodology to other manufacturing industries. This work can also be further extended by

developing a mathematical software package for the selection of lean strategies.

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