 Open Access
 Authors : Arul Thayammal Ganesan
 Paper ID : IJERTV8IS110229
 Volume & Issue : Volume 08, Issue 11 (November 2019)
 Published (First Online): 26112019
 ISSN (Online) : 22780181
 Publisher Name : IJERT
 License: This work is licensed under a Creative Commons Attribution 4.0 International License
Lean Manufacturing based Interpretive Structural Modeling using Fuzzy Analytical Hierarchy Process
Arul Thayammal Ganesan 1
1Professor, Department of Mechanical Engineering,
St Marys Engineering College, Deshmukhi, Pochampalli, Nalgonda District, Hyderabad 508 284, Telangana, India.
Abstract: Manufacturing industries are the very source coun trys development which spaces economic growth are impolite, absorbs and recreates the same several times over in the econo my. To improve capacity and competitiveness and bring down waste, lean manufacturing is a genial aspect tool. Even though largescale sectors have started implementing it, the Indian Mi cro, Small and MediumEnterprises (MSMEs) still find it hard to implement. In this novel research offered the success factors that are critical for more successful lean implementation. Using In terpretative structural modeling and structural equation model ing the strength of each factor are determined. The outcome of research work clearly indicates that Strong Management and Leadership (SML), Electronics and Communication of the Transformation process and goals (EC), Skills and Expertise (SE), and Employee Trust (TE) are at the higher level of im portance in the ISM model considered. It is noted that Plan and Strategy (PS), Education and Training (ET), Customer Focus (CF) and Thinking Development (TD) are relatively lower level preference. PS has the highest driving power and EC has the least dependence powers. Based on eleven criteria, SML is the best for all Indian MSMEs. The proposed model evaluated using Analytical Hierarchy Process (AHP), fuzzy AHP, are made to find the extent of Lean Manufacturing (LM) which has been implemented for successful performance in six Indian MSMEs
Keywords: Lean Manufacturing, Strong Management Leadership, Analytical Hierarchy Process, Interpretive Structural modeling
NOMENCLATURE
MCDM Small and MediumEnterprises AHP Analytical Hierarchy Process ISM Interpretive Structural Modeling
MISM Modified Interpretive Structural Modeling SSIM Structural SelfInteraction Matrix

INTRODUCTION
In various real life problems, Small and Medium Enterprises (MCDM) plays an optimal note. In one way or the other local or federal government industry or business activity is involved in the evaluations of a group of alternatives in terms of a set of decision measure. These measures are con flicting to one another often. It is very expensive to collect the pertinent data often. J.Warfield in 1974 planned philosophy to investigate the complicated social and financial system. It is a computerbased learning method during which specific people or teams develop a map of the complicated relations be
tween several criteria concerned in any things [11]. the essen tial plan is to resolve a sophisticated system into many subsys
tems (elements) and construct a construction structural model by exploitation the experts sensible experiences and data. The concepts involve taking a set of criteria, comparing it with those measure in the dual relation defined, and construct a reachability matrix from the companions [1, 9].
In many engineering industrial applications, the ulti mate call depends on the evaluations of variety of alternatives in terms of variety of criteria of any downside, which can become sophisticated once the factors is denoted in sever al units or the pertinent information area unit dissimilar to be measured [2]. In dealing with this kind of decision problem, the AHP is a cluster based resolution making technique which guides the decision makers to find the best criteria that meets their goal [10]. By formulating a series of one on one compar ison it provides one framework to decrease qualitative and quantitative difficult constraints. It does not only provide justi fication for the choice of the best alternatives. Fuzzy AHP: To solve multiplecriteria decision making problem in both aca demic research and industrial practice, generally, the AHP has been used. But because of the vagueness and uncertain with a conventional AHP, it may not capture accurately the decision makers judgments. Therefore, to compensate for this deficien cy in the conventional AHP, fuzzy logics is initiated into the pairwise assessment in the AHP. This is termed as fuzzy AHP.
In several engineering industrial applications, the final de cision depends on the evaluations of a number of alternatives in terms of a number of criteria of any problem, which may become complicated when the criteria is denoted in different units or the pertinent data are difficult to be quantified[2]. In dealing with this kind of decision problem, the AHP is a group decision making technique which guides the decision makers to find the best criteria that meets their goal [10]. By formulat ing a series of one on one comparison it provides one frame work to reduce qualitative and quantitative complex con straints. It does not only provide justification for the choice of the best alternatives. Fuzzy AHP: To solve multiplecriteria decision making problem in both academic research and in dustrial practice, generally, the AHP has been used. But be cause of the vagueness and uncertain with a conventional AHP, it may not capture accurately the decision makers judg ments. Therefore, to compensate for this deficiency in the conventional AHP, fuzzy logics is introduced into the pair wise comparison in the AHP. This is termed as fuzzy AHP.

BACKGROUND
Due to the increasing competition, supplier selection at tained the state of highest importance for most of the compa nies. Noorul & Kannan [7] developed a supply chain for its five manufacturing products. A few issues emerge because of picking the best provider and for the association between the criteria and subcriteria. In light of the master study, the crite ria and subcriteria are picked. A poll comprising of the ele ments was set up for overview. To rank the criteria and sub criteria utilizing the provider choice and rank the connection among those utilizing Interpretive Structural Modeling (ISM) is a definitive point.
Parthiban et al. [8] made a new attempt to solve the ques tions on the interactions between the criterion, which criteria influences the supplier selection more, finding the best and weak supplier and providing performance improvement meth ods to enhance the quality for considering the associations between the criteria and organizing them utilizing the Modi fied Interpretive Structural Modeling (MISM). Yang et al.[12] (2008) adopted interpretive structural modeling to determine the relationships among the subcriteria. To compute the rela tive weights for each criteria fuzzy analytical hierarchy pro cess is used. The huge yet complex to comprehend and organ izes the interrelationships between singular hazard compo nents. On analyzing all the risks in a company as an integrated system, to compute asset, a learning technique ISM is utilized to build a basic connection and characterize those hazard in terrelationships.
Gorvett and Liu [6] embraced ISM procedures to overview firms general hazard profile is a superior way. By a few crite ria's and subcriteria's the provider basic leadership process is in this way turned out to be a muddled procedure, which may fluctuate crosswise over various item classes and circumstanc es. Cannon and Perreault [3] proposed and dissected the inter relation of criteria which is utilized to choose the provider SML — Strong Management & Leadership
OC — Organizational Culture TE — Employee Trust
SE — Skills and Expertise FC Financial Capabilities
EC –Effective Communication
3.2 Structural SelfInteraction Matrix
In building up the calculated relationship among the crite ria, ISM philosophy suggests the utilization of exspunky sentiments in light of different administration systems. In this examination, specialists from the business are counseled for recognizable proof of the applied relationship among the ele
who decide the ecological performance utilizing ISM and AHP utilizing a vehicle organization in the southern piece of India the adequacy of the ISM and AHP model is character ized.

PROPOSED MODEL
The ISM concept was basically introduced by Warfield in 1974. It was emphasized that ISM approach facilitates classi fication and directions of the complex relationships among components of a socioeconomic system. ISM deciphered is connected on the gathering's judgments and choices, regard less of whether and how the framework components are con nected. It depends on relationship establishment and the last structure is expelled from an unpredictable arrangement of frameworks. It can likewise be named displaying since the last relationship is said in a divide graphical model. Several steps involved in the ISM techniques are as follows. To identify the key element based on the experts survey. To establish a con ceptual relationship in between the elements in reference to which parts of elements are to be examined. To build a Struc tural SelfInteraction Matrix (SSIM) of components are mean ing the match savvy connection between the components. To build up a reachability network from the Structural Self Interaction Matrix (SSIM) by twofold relations and assess the framework for transitivity, which is a fundamental presump tion in ISM that states If component A is identified with B and B is identified with C, at that point An is identified with C from the matrix level segment of the component are made. In light of the ISM show the supplanting levels ought to be fin ished. To figure the standardized weights for the components the model is investigated to check for irregularity.
3.1 Identification of the Performance Criteria
By taking opinions from the industrial experts, the perfor mance criteria are identified. The factors, which are finalized for the study, are mentioned below
CF –Customer Focus
PM –Performance Measures ET –Education and Training PS –Plan and Strategy
TD –Thinking Development
ments for the provider determination. To mean the bearings of the connection between the variables (p and q) the accompa nying documentations are utilized.
V: Criterion p will help alleviate criterion q; A: Criterion q will be alleviated by criterion p;
X: Criterion p and q will help achieve each other; and O: criterion p and q are unrelated
Table1. Structural SelfInteraction Matrix illustrating Linguistic Variables for the power of each Criterion
Table 2.Initial Reachability Matrix illustrating Driving Powers and Dependence Performance Criterion

Reachability Matrix
The SSIM is transformed into a binary matrix on substitut ing V, A, X, O by 1 and 0 as per the case. The rules for the substitution of 1 and 0 are the following:

If the (p, q) entry in the SSIM is V, then the (p,q) entry in the reachability matrix becomes 1 and the (q, p) entry becomes 0.

If the (p, q) entry in the SSIM is A, then the (p,q) entry in the reachability matrix becomes 0 and the (q, p) entry becomes 1.

If the (p, q) entry in the SSIM is X, then the (p,q) entry in the reachability matrix becomes 1 and the (q, p) entry also becomes 1.

If the (p, q) entry in the SSIM is O, then the (p,q) entry in the reachability matrix becomes 0 and the (q, p) entry also becomes 0.


Hierarchy of Factors
Figure1. Performance factors in a hierarchy
Fig.1. Hierarchy of Factors

Final Reachability matrix
The transitivity is checked for conclusive reachability matrix utilizing the connection as takes after: If A is identified with B and B is identified with C, at that point A is identified with C
Table 3. Final Reachability Matrix Showing Driving Powers and Dependence Power for each Criterion

Analytical Hierarchy Process implementation
AHP is characterized a numerical basic leadership method which permits thought of both subjective and quantitative parts of choices [4]. Differently subcriteria providing justifi cation for the choice, it allows the decision makers to select the best alternatives
AHP calculation is performed in three phases: (1)Building of the hierarchy
(2)Assessment of pairwise comparison matrices (3)Estimation of priority weights of alternative

Construction of the Hierarchy

Identify the objective of the issue

Identify the criteria and sub criteria which bolster the satisfaction of the ensuing levels

Identify the elective recommended to satisfy the ob jective all levels following the criteria levels.


Evaluation of the Pairwise Comparison
Estimate the judgment weight for every examination table for instance a scale from 1 to 5 is utilized to differentiate the significance of given rule contrasted and each other.
Table 4. Rating scale to balance one standard over another
Response
Grade
Excellent
5
Good
4
Normal
3
Satisfied
2
Unsatisfied
1
Ranking possible with the pair of alternatives results are done in several square matrixes for each criterion. The per formance factor obtained from 6 industries based on the weights of linguistic variables is shown in Table 5. Figure 2.
Plotted below, shows each criteria performance difference through various color lines. Also the normalized weights ob tained for the performance of 6 industries is shown in Table 6 for each criterion value.
Table 5.Linguistic Variable Weights for six Suppliers for the Performance Factors attained through Industry
Fig.2. Linguistic Variable Weights for six Suppliers for the Performance Factors Attained through The Industry.
Table 6. Normalized Weights for the Performance Criteria of six Suppliers.

Calculation of priority weights of alternative
For each alternate calculate the component and composite are relative to priority, usually the priority weights of each supplier multiplied by weights of the corresponding criterion which is the best global supplier for supply of the parts, thus
the given idea is to present the highest score of the supplier as shown in the following Table 7, Table 8, Table 9, Table 10 and the weight, rank values are depicted in Fig. 3(a), Fig. 3(b), Fig. 3(c)
Table 7. Priority Weights with the Ranking of each Supplier
Suppliers
Alternative Priority Weights
Rank
A
0.192
1
B
0.181
3
C
0.171
5
D
0.174
4
E
0.184
2
F
0.147
6


Fuzzy Analytical Hierarchy Process implementation
AHP is an intense method to resolve complex verdict prob lems. Any complex problem can be decayed into a number of subproblems using AHP in terms of hierarchical levels where each level symbolizes a set of criteria or attributes relative to each subproblem. However, the pure AHP model has easy way to rank the criteria [4, 5]. To defeat thse problems, sev eral researchers incorporate fuzzy theory with AHP to get better the uncertainty.
Then, the research will briefly bring in that how to carry out the fuzzy AHP in the following sections. The form make pair wise evaluation matrices in among all the suppliers simi lar to AHP but in linguistic terms (not in real numbers) against all the factors. In this research, the linguistic variable and the corresponding fuzzy number considered are shown in the Ta ble 8, 9 and 10.
Table 8.Contrast of the Performance Score of the Variable
Table 9.Alternative and Normalized Weights of Suppliers by AHP among their Ranks
Table 10. Crisp and Normalized Weights of Suppliers by Fuzzy AHP
Fig.3 (a) Fig.3 (b)
Fig.3 (c)
Fig.3 (a) Crisp Weights of Suppliers by Fuzzy AHP Fig.3 (b) Normalized Weights of Suppliers by Fuzzy AHP, Fig.3 (c) Rank of Suppliers by Fuzzy AHP

RESULTS
The selection of performance factors in the supplier selec tion process plays a vital role for many small scale industry customers. To break down the connection between them, por tions of the central point have been featured and put into an ISM demonstrate. Some valuables are in sighted into the rela tive importance and the dependence power.
SML, RC, SE, and TE are thought to be the conspicuous variables. From the ISM demonstrate, it is watched that PS,
ET, CF and TD are at the base level of the order. PS and EC have their most astounding driving and reliance controls indi vidually. Similarly the AHP technique is also done for those factors with the given six companies and finally the ranking is made to those companies based on their corresponding weights. The AHP results and fuzzy AHP results are shown in Table 11 Figure 4 The ranking is given as follows; COMPANYA and COMPANY F has the highest and lowest priority weights.
Table11. Comparisons of weights of AHP and fuzzy AHP
A comparison with the weights and rankings to decrease the uncertainly between AHP and fuzzy AHP is performed. Few results are mentioned below. Organization An and B
don't give any uncertainty while organization C gives critical changes in the weights bringing about their rankings.
Fig.4 Comparison of weight of AHP and fuzzy AHP

CONCLUSION
The prime intention of lean manufacturing is to provide su perior quality to the customer at an affordable cost and in a way to make certain the customer satisfaction. This concept tends people to be the best techniques like (ISM, AHP) to contribute. Hence, the outcome provided from the techniques (ISM AHP) proves to be of more practical significance. Be cause of the interaction of criteria with industry experts this practical result proves to be noteworthy. Through the perfor mance factors are found to be only significant in the interac tion between the criteria the ISM enhances its use with a per fect interrelationship. AHP makes it significantly clear with the inclusion of nonquantifiable factors like social, political factors & also some economic factors. These, for mitigating environmental, social factors fuzzy AHP also provides a very useful decision for making loop, hence these integrated ap proaches may consume less time and consuming these efforts in supplier selection can cause their potential application.
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