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ANALYSIS OF BARRIERS TO LEAN IMPLEMENTATION IN

MACHINE TOOL SECTOR

Vikram Sharma*

Assistant Professor, Mechanical Engineering Department, Galgotias College of Engineering and Technology, India Email: [email protected]

Amit Rai Dixit

Department of Mechanical Engineering, Indian School of Mines, Dhanbad, India

Mohammad Asim Qadri Mechanical Engineering Department, Galgotias College of Engineering and Technology, IndiaTechnology, India

A B S T R A C T K E Y W O R D S

A R T I C L E I N F O

Lean implementation barriers, machine tool sector, ISM, IRP

Received 18 June 2014 Accepted 04 August 2014 Available online 1 December 2014

Acute global competition has forced organization to

adopt Lean manufacturing strategy in order to improve

competitive potential. Top managements should

examine the barriers to lean in order to ensure its

effective implementation. This paper aims to analyze

the barriers to implementing lean manufacturing

practices based on the investigation of machine tool

industry in the National Capital Region of India. Two

distinct modeling approaches namely Interpretive

Structural Modeling (ISM) and Interpretive Ranking

Process (IRP) have been employed to examine the

contextual relationship among the lean implementation

barriers, and to rank them with reference to key

performance areas, respectively. Lean implementation

barriers and performance indicators were identified

through literature review and opinion of experts from

industry and academia. ISM methodology is used to

understand the mutual influences among the lean

barriers and then classify these barriers on the basis of

their driving and dependence powers. As is obvious,

the barriers with high driving power and dependency

need more attention than the others. Development of

a structural model for barriers to lean implementation

can be considered as one of the major contribution of

this study. The novel IRP methodology is used to

examine the dominance relationship and ranks the

barriers with respect to key performance indicators

related to the machine tool industry.

________________________________

* Corresponding Author

VIKRAM SHARMA, AMIT RAI DIXIT, MOHAMMAD ASIM QADRI/ International Journal of Lean Thinking Volume 5, Issue 1(December 2014)

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1. Introduction

The Indian machine tools industry manufactures a broad range of metal-cutting

and metal-forming machine tools. The industry specializes in making conventional

machines as well as customized computer numerically controlled (CNC) machines.

There are other variants such as machining centers and special purpose machines

offered by Indian manufacturers. The machine tool industry is now aiming at adding

value at lower costs, while meeting high quality standards. In keeping with the current

trends, the increase in demand of CNC machines is working as driver of growth for the

machine tools industry in India (DHI, n.d.). The CNC machine tool industry in India is

largely concentrated at Mumbai and Pune in Maharashtra, Jalandhar and Ludhiana in

Punjab, Ahmedabad, Baroda, Jamnagar and Rajkot in Gujarat, Coimbatore and

Chennai in Tamil Nadu, Bangalore and Mysore in Karnakata, and Ghaziabad in Utter

Pradesh in India.

According to the World machine tool output and consumption survey, conducted

by Gardner research (2013), the Indian machine tool industry is miniscule ($720 million)

as compared to the global machine tool producing industry ($ 93205 million), but it

supports a multi-billion dollar engineering industry. The factors like lack of capacity,

neglect of investment in technology up-gradation, and lack of capability to design and

produce flexible manufacturing systems have resulted in the loss of market to US,

European, and Japanese machine tool makers (IMTMA, n.d.). Some other issues of

critical importance to the machine tool industry are listed as:

There has been consistent volatility of demand in automobiles and consumer

goods sector in India due to reasons such as rise in inflation. This, in turn, has

affected the machine tool industry which finds its major customers in automobile

and consumer goods industry.

A large number of parts and sub-assemblies have to be brought together at one

place and assembled to make a machine tool. Hence, the quality of overall

product relies on quality efforts of supply chain partners.

Due to the fragmented nature of the industry and the small size of the firms, most

of the players have not implemented any of the latest soft technologies like Six

Sigma, Kaizen, Lean Manufacturing, and TPM. The benefits of economies of

scale have not accrued to the machine tool firms due to highly fragmented

market structure.

Indian educational curriculum in educational institutions is not geared to impart

the all round technical knowledge required by the engineers and operators in this

sector.

Most of the small scale manufacturers have failed to capitalize on the available

market opportunities largely due to financial constraints.

Despite many limitations, the standalone CNC machine tools and special purpose

machines being produced in India have attained technological maturity (IMTMA, n.d.1).

The Indian machine tools industry has the potential to provide low-cost high quality

manufacturing solutions. Last few years have seen India emerging as a new


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manufacturing destination. The firms are seeking new ways to increase the value of their

products and services through elimination of unnecessary processes from their

production systems (Panizzolo et al., 2012). Indian Machine Tool manufacturers are

relocating along with the supply chain units, and service providers to well designed

industry parks in order to improve competitive potential of their value chain (IMTIMA,

n.d.2).

The status of lean implementation in India is unclear. According to Garza–Reyes et

al., (2012) lean manufacturing is not popular operational or quality improvement

methodology adopted by Indian organizations. The results of a survey conducted by

Eswaramoorthi et al. (2011) show that the lean implementation in the Indian machine

tool sector is still in the stage of infancy. Therefore, in this paper, we analyze the barriers

to lean implementation in the Indian machine tool sector.

Interpretive structural modeling (ISM) can be used to identify and summarize the

relationships among specific variables that define a problem or an issue (Warfield, 1974,

and Sage, 1977). The methodology provides us with a means of imposing order on the

complexity of such variables (Mandal and Deshmukh, 1994; Jharkharia and Shankar,

2005). Interpretive ranking process (IRP) can be used to develop a knowledge base

and rank the lean barriers based on their impact on certain performance criteria (Sushil,

2005). Therefore, we propose the use of ISM and IRP methodology for analyzing

various barriers to lean implementation related to machine tool sector. The literature

review on lean practices in machine tool industry, together with the opinion of experts, is

used to identify the critical lean implementation barriers. An inter-barrier relationship

matrix is established on the basis of nature of mutual influence the barriers leave on

each other. The matrix so formulated is then used to develop an ISM model to

understand the linkages between barriers of lean implementation in machine tool

industry. The main objectives of this paper are:.

To identify major barriers to lean implementation in the Indian machine tool sector.

To identify the relationship and hierarchy, if any, and the ranking of lean implementation barriers in machine tool sector.

To find out the outcome of interaction among identified barriers through ISM and IRP.

The remainder of this paper is structured as follows: First we provide literature review on various barriers for lean implementation. The subsequent section covers the ISM methodology and model development. This, in turn, is followed by MICMAC analysis. The next section covers the IPR methodology. The results, managerial implications, limitations, and further scope of this research are presented in the concluding section.

2. Barriers of lean in the machine tool industry

Why have many companies not been able to achieve the perceived benefits from lean

strategies, or in some cases abandoned the efforts altogether? There are reports that

most of the lean implementation efforts are not reaching the goal (Halling, 2013). There


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is a lack of knowledge about failed lean attempts and barriers to application of lean,

since research is still limited. In a study of 68 UK manufacturing firms, Bhasin (2012)

found that barriers to Lean are strongly connected to the size of an organization, and as

every organization is unique, cultural issues are of importance. Research on lean

implementation in aerospace industry indicates that top managers' involvement is

important, since they have the vital role of presenting a coherent vision that clearly

communicates how lean is suited and related to their business strategy (Crute et al.,

2003). Research on lean manufacturing implementation in Malaysian automotive

components manufacturing shows the importance of skilled people with their own

experience with lean, as lean teachers and coaches. It was further concluded that

support and clear directions from top managers are imperative (Muslimen et al., 2011).

Major barriers to lean implementation as found by various researchers are given in

Table 1 and are discussed in the following section.

Table 1. Lean implementation barriers

S.No. Lean Implementation hurdles Researchers

1. Resistance to change Yang and Yu (2010), Eswaramoorthi et. al. (2011), Bakås et. al. (2011), Kumar and Kumar (2012), Panizzolo et al. (2012)

2. Misunderstanding of Lean Yang and Yu (2010), Bakås et. al. (2011), SCDigest, (2013)

3. Lack of support from top management

Kumar and Kumar (2012), Panizzolo et al. (2012)

4. Lack of Broad Organizational involvement

SCDigest, (2013), Bakås et. al. (2011)

5. Poor communication system Yang and Yu (2010), Kumar and Kumar (2012),

6. Conflicts with Other Initiatives SCDigest, (2013)

7. Low volume of demand Eswaramoorthi et. al.,(2011)

8. Disparate Manufacturing Environments

Yang and Yu (2010) , SCDigest, (2013)

9. Consultants’ apathy SCDigest, (2013), Eswaramoorthi et. al., (2011)

10. Lack of Perseverance SCDigest, (2013), Eswaramoorthi et. al., (2011)

11. Lack of resources Eswaramoorthi et. al., (2011), Bakås et. al., (2011)

12. Inadequate training Eswaramoorthi et. al., (2011), Kumar and Kumar (2012), (Bakås et. al., 2011)

13. Frequent changes in design Eswaramoorthi et. al., (2011)

14. Uncertain vendor response Sharma (2012)

Resistance to change Lean implementation involves changing the ways some people

in the organization work. But this has never been an easy task. There is an inherent resistance

to change in most humans (Bakås et. al., 2011). Natural tendencies of some employees, such

as bad personal habits, personal insecurity, and hesitation can lead to staff’s resistance to lean

implementation (Yang and Yu, 2010). A lean improvement initiative can also be perceived by

employees as a way to get rid of work force (Panizzolo et al., 2012). Resistance to change is

very common phenomena as it raises fear of failure, and fear of high initial investment cost

(Kumar and Kumar, 2012).


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Misunderstanding of Lean There are many misunderstandings about lean may

be due to knowledge constraints. Yang and Yu (2010) report that people still believe that

1) The implementation of lean production needs large investment and is only suitable for

large enterprises;

2) Lean production is only suitable for specific industries, not for all businesses;

3) Lean production originated in Japan, and it is not suitable for all countries.

There exist some misconceptions that Lean requires significant financial

investments or is only fit for specific industries (Bakås et. al.,2011). Some companies

take Lean as a set of tools and techniques instead of an enterprise wide system

(SCDigest, 2013). Consultants are in dilemma regarding which lean tool to use in what

conditions. Confusion prevails regarding lean implementation in the machine tool

industry as some practiconers feel that lean is suitable for mass production industry

such as automobile original equipment manufacturers (OEMs) and their component

manufacturers only.

Lack of support from top management Some times, decision to implement

lean is taken under pressure from the customer, and the management lends only half

hearted support (Kumar and Kumar, 2012).If the management does not lend support to

a new program being launched or is uncommitted to the resources needed, it can create

hurdle to the success of the program. Lean, in India, is a relatively new paradigm. Some

practitioners complain of vague support from the top managements for lean

implementation as they fail to perceive the potential benefits. Management often

underestimates the time and work involved. Some managements give lean

implementation a low priority and do not present an adequate reason for lean

implementation (Panizzolo et al., 2012).

Lack of broad organizational involvement involving all employees proactively

in improvement efforts is an essential element in any change process. Successful

companies are those with a culture of broad organizational involvement. Lean

implementation is sometime made the responsibility of select managers of the firm with

little support from top management or other employees (SCDigest, 2013). Ensuring

strong management involvement and developing thorough employee participation is

critical to the success of lean implementation (Bakås et. al., 2011). Involving and

empowering the employees in the change process remains an issue of concern in the

Indian manufacturing industry.

Poor communication system Lack of communication can be one of the prime

obstacles in lean manufacturing implementation (Kumar and Kumar, 2012). When the

companies lack information sharing system on lean production, poor inter-organizational

and intra-organizational communication system can act as a significant barrier. Two

things are needed to meet this; one start networks together with other companies in

order to learn and practice lean principles and methods and second is to develop an

effective internal communication platform (Yang and Yu, 2010).


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Conflicts with other initiatives Managers face challenges in simultaneously

implementing several models, tools and techniques such as TQM, Six Sigma, TPM and

SCM for continuous improvement (SCDigest, 2013). These attempts are made in a

scattered manner and hence face frequent failures (Devadasan et al., 2012). In India

lean implementation is not universal. In some companies, other improvement

methodologies, such as Six Sigma and TQM create internal conflicts with Lean

initiatives. At several occasions, lean implementation also conflicts with implementation

of ERP system (SCDigest, 2013).

Low volume of demand Many firms, world over, find their lean strategies

thwarted by the increasingly unpredictable demand environment. The lower volume of

demand and highly fluctuating/varying customer orders are the serious hurdles to lean

implementation faced by the machine tool industries in India (Eswaramoorthi et. al.,

2011).

Disparate manufacturing environments Lean production has gradually

developed based on Toyota specific environment, socio-economic and cultural

backgrounds. But many firms implement lean production without fine tuning and

customizing it to their needs.. Also, it is difficult to implement lean in some complex

manufacturing environments such as process or hybrid manufacturing industries

(SCDigest, 2013). When Lean is implemented, it needs to be adapted to the specific

requirements of that company and the requirements of the customers of that specific

company (Bakås et. al., 2011). Indian machine tool manufacturers perceive that the lean

implementation procedures are too general and not industry specific (Eswaramoorthi et.

al., 2011).

Consultants’ Apathy Plenty of consultants offer their services to companies on

lean paradigm. However, it is found that most of the consultants offer the service to

implement only a few of the lean tools and techniques (SCDigest, 2013). These

consultants seldom implement lean in a holistic manner. Not following the systematic

approach built into each lean tool can result in an inadequate outcome. High cost of

consultation is also a cause of concern in machine tool small and medium enterprises

(Eswaramoorthi et. al., 2011).

Lack of Perseverance There have been cases of dodging the idle lean

implementation process due to impatience, lack of infrastructure, poor planning, no

preparation, poor assumptions, limited participation, and a flawed approach (SCDigest,

2013). Machine tool manufacturers in India do not consider lean implementation

enthusiastically as too much time and efforts are required to implement lean

(Eswaramoorthi et. al., 2011). All these factors can make it challenging for some

companies to get lean right from the outset. If, at the start, a lean initiative increases

cost, a company may cancel the program rather than investigating the causes

(SCDigest, 2013).


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Lack of resources not all firms have sufficient resources to be allocated for lean

improvement projects (Bakås et. al., 2011). Resource constraints with reference to

volume of production have discouraged Indian machine tool manufacturers from

adopting lean principles (Eswaramoorthi et. al., 2011).

Inadequate Training Inadequate lean training opportunities, too general

procedures and less lean awareness programs in India are among the significant factors

leading to low priority in lean implementation (Eswaramoorthi et. al., 2011). Training

budgets and staff development programs are often limited due to a focus on reaching

short-term objectives (Bakås et. al., 2011). A firm which lacks knowledge of lean, cannot

implement it successfully (Kumar and Kumar, 2012).

Frequent changes in design The major customers of the machine tool industry

are the automobile industry and the consumer goods industry. These industries are

trying to come out with customer centric, highly customized products. Trends indicate a

shift in demand from general purpose machines to special purpose machines. This

leads to frequent changes in design which practitioners consider as a barrier to lean

implementation (Eswaramoorthi et. al., 2011).

Uncertain vendor response The machine tools are built by assembling a large

number of components and sub assemblies. The machine tool manufacturers in India

rely on bought out items, thus work in a complex supply chain environment. Some

experts believe that due to factors such as absence of strategic partnership with

suppliers, uncertainty of orders from machine tool manufacturers to suppliers, small

order sizes and lack of technical or financial support, component manufacturers find it

difficult to commit to lean initiatives taken by machine tool manufacturers. To achieve the

benefits of lean throughout the supply chain, it is essential for a manufacturing company

to build a partnership with its suppliers, as if they were departments within their own

company (Sharma, 2012).

3. Research methodology

The objectives of this article are to examine the relationships among various

barriers of lean implementation in Indian machine tool sector and to rank them with

reference to various performance measures. Here, the ISM is used to examine the

contextual relationships among barriers and IRP is applied to rank the barriers with

regard to various performance measures. Since the number of such potential barriers to

lean implementation is large, each capable of influencing the most of others to varying

degree, it is very difficult, if not impossible, to consider them all (Haleem et al., 2012).

Here, in this study, a team of experts from industry and academia participated in a

brainstorming session and identified the following 10 barriers to lean implementation in

machine tool industry: Lack of management commitment (B1), Resistance to change

(B2), Misunderstanding of lean (B3), Lack of organizational culture (B4), Poor

communication system (B5), Frequent changes in design(B6) ,Uncertain vendor


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response (B7), Low volume of demand (B8), Longer lead time (B9), and Inadequate

training (B10).

4. Interpretive structural modeling

ISM is an interactive learning process (Soti et al., 2010). It was developed by

Warfield (1974) and Sage (1977) and is an adaptation of paired-comparison approach.

In this, a set of different and directly related variables affecting the system under

consideration is structured into a comprehensive systemic model (Soti et al., 2009). The

beauty of the ISM model is that it portrays the structure of a complex issues of the

problem under study in a carefully designed pattern employing graphics as well as

words (Ravi and Shankar, 2004). ISM can act as a tool for imposing order and direction

on the complexity of relationships among elements of a system (Sage, 1977; Singh et

al., 2003). A brief outline of applications of ISM is provided in Table 2.

Table 2. Application of ISM in various areas of research.

Area of application Authors

Technology assessment

Watson (1978), Linstone et al. (1979)

IT-enablers Thakkar et al. (2008), Batra (2006), Khurana et

al. (2010),

Balanced scorecard Thakkar et al. (2006)

Knowledge management Singh et al. (2003), Singh and Kant (2007)

Third-party logistics (3PL) Thakkar et al. (2005), Qureshi et al. (2008)

Reverse logistics Kannan et al. (2009), Govindan et al. (2012),

Sharma et al. (2011)

Environmentally conscious

manufacturing

Sarkis (2006)

Supplier development and selection Chidambaranathan et al. (2009), Govindan et al.

(2010), Punniyamoorthy et al. (2011)

Supply chain management Pfohl et al. (2011), Diabat and Govindan (2011),

Ramesh et al. (2010), Luthra et al. (2011),

Competitiveness Singh et al. (2007)

Six Sigma Soti et al. (2010; 2011)

Flexible manufacturing system Raj et al. (2008)

Total quality management Talib et al. (2011)

Technologies selection Lee et al. (2011)

Agile manufacturing Hasan et al. (2009)

Advanced Manufacturing Technologies

(AMTs)

Singh et al. (2007), Singh and Khamba (2011)

Manufacturing strategy Abbasi et al. (2010)

Business process reengineering Hahm and Lee (1994).

Steps that lead to the development of an ISM model for barriers to lean implementation

practices in machine tool industry are illustrated below:


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Step 1: Structural Self-Interaction Matrix (SSIM): The SSIM is developed based on

the opinion of experts, three from the machine tool industry and two from academia,

nominated for the purpose of identifying the nature of contextual relationship among the

variables. A ‘influences’ type contextual relationship i.e. one factor influences another,

was chosen for analyzing the nature of their interactions. The driving power of any

particular lean criteria is the total number of criterion (including itself) which it may help

achieve while the dependence is the total number of criterion that may help in achieving

it. Keeping in mind the contextual relationship for each criterion and the existence of a

relationship between any two factors (i and j), the associated direction of the relationship

is questioned in a pair wise manner.

Four symbols are used to denote the directional relationship among the enablers (Soti, 2011):

V. Barrier i will aggravate barrier j.

A. Barrier j will be aggravated by barrier i.

X. Barriers i and j will aggravate each other.

O. Barriers i and j are unrelated.

Figure 1. Flow chart for ISM

The structural self interaction matrix (SSIM) is developed considering one-on-one

relationship between all the ten identified barriers and is shown in Table 3.


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Table 3. SSIM

Barriers

1 2 3 4 5 6 7 8 9 10

Lack of management commitment 1

V X V V A X A O V Resistance to change 2

A X X A X A A A

Misunderstanding of lean 3

X X O V O O A

Lack of organizational involvement 4

X O O A V A Poor communication system 5

O V O V A

Frequent changes in design 6

O O O O Uncertain vendor response 7

A O A

Low volume of demand 8

O O Longer lead time 9

O

Inadequate training 10

Step 2: Development of initial reachability matrix: The SSIM established in the

previous step is converted into the initial reachability matrix as shown in Table 4 by

substituting the four notations (i.e., V, A, X or O) of SSIM by 1’s or 0’s as per the

following rule. If the (i, j) entry in the SSIM is V, then the (i, j) entry in the reachability

matrix becomes 1 and the (j, i) entry becomes 0. If the (i, j) entry in the SSIM is A, then

the (i, j) entry in the matrix becomes 0 and the (j, i) entry becomes 1. If the (i, j) entry in

the SSIM is X, then the (i, j) entry in the matrix becomes 1 and the (j, i) entry also

becomes 1. If the (i, j) entry in the SSIM is O, then the (i, j) entry in the matrix becomes 0

and the (j, i) entry also becomes 0.

Table 4. Initial Reachability Matrix

Barrier 1 2 3 4 5 6 7 8 9 10

1 1 1 1 1 1 0 1 0 0 1

2 0 1 0 1 1 0 1 0 0 0

3 1 1 1 1 1 0 1 0 0 0

4 0 1 1 1 1 0 0 0 1 0

5 0 1 1 1 1 0 1 0 1 0

6 1 1 0 0 0 1 0 0 0 0

7 1 1 0 0 0 0 1 0 0 0

8 1 1 0 1 0 0 1 1 0 0

9 0 1 0 0 0 0 0 0 1 0

10 0 1 1 1 1 0 1 0 0 1

The principle of transitivity is then applied to derive the final reachability matrix

from the initial reachability matrix. The transitivity is checked, by checking if element i

leads to element j and element j leads to element k than element i should lead to

element k (Soti, 2011). The final reachability matrix along with driving power and

dependence power is shown in Table 5. The values of driving power and dependencies

will be used in the MICMAC analysis subsequently, where the barriers will be classified

into four groups, namely autonomous, dependent, linkage, and independent.


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Table 5. Final Reachability Matrix with driving power and dependence power

Barrier 1 2 3 4 5 6 7 8 9 10 Driving Power

1 1 1 1 1 1 0 1 0 1* 1 8

2 1* 1 1* 1 1 0 1 0 1* 0 7

3 1 1 1 1 1 0 1 0 1* 1* 8

4 1* 1 1 1 1 0 1* 0 1 0 7

5 1* 1 1 1 1 0 1 0 1 0 7

6 1 1 1* 1* 1* 1 1* 0 0 1* 8

7 1 1 1* 1* 1* 0 1 0 0 1* 7

8 1 1 1* 1 1* 0 1 1 1* 1* 9

9 0 1 0 1* 1* 0 1* 0 1 0 5

10 1* 1 1 1 1 0 1 0 1* 1 8

Dependence Power 9 10 9 10 10 1 10 1 8 6

(Total = 74)

Note: * Based on transitivity checks

Step 3: Level partitioning: For assigning the levels to the identified barriers we

need to find the intersection of reachability set and antecedent set for each barrier

(Warfield, 1974). The reachability set of a criterion consists of the barrier itself and the

other barriers that it may impact, whereas the antecedent set consists of the barrier itself

and the other barriers that may impact it (Haleem et al., 2012). In the first iteration, the

barrier for which the reachability and the intersection sets are the same occupy the top

level in the ISM hierarchy (Soti, 2010). It is comprehended from Table 6 that barriers 2,

4 and 7 occupy the highest level referred to as level I in the ISM model. Once the top-

level factor is identified, it is removed from further consideration. This process is

continued until the levels of all the barriers are found. Level partitioning helps in building

the diagraph and the ISM model. The lean implementation barriers, along with their

reachability set, antecedent set, intersection set and the levels, are shown in Tables 6 to

9.

Table 6. Level partition-iteration 1

Barrier Reachability set Antecedent set Intersection Level

1 1,2,3,4,5,7,9,10 1,2,3,4,5,6,7,8,10

1,2,3,4,5,7,10

2 1,2,3,4,5,7,9

1,2,3,4,5,6,7,8,9,10

1,2,3,4,5,7,9

I

3 1,2,3,4,5,7,9,10

1,2,3,4,5,6,7,8,10

1,2,3,4,5,7,10

4 1,2,3,4,5,7,9

1,2,3,4,5,6,7,8,9,10

1,2,3,4,5,7,9

I

5 1,2,3,4,5,7,9

1,2,3,4,5,6,7,8,9,10

1,2,3,4,5,7,9

6 1,2,3,4,5,6,7,10

6 6

7 1,2,3,4,5,7,10 1,2,3,4,5,6,7,8,9,10 1,2,3,4,5,7,10 I


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8 1,2,3,4,5,7,8,9,10

8 8

9 2,4,5,7,9 1,2,3,4,5,8,9,10

2,4,5,9

10 1,2,3,4,5,7,9,10

1,3,6,7,8,10

1,3,7,10



1 1,3,5,9,10 1,3,5,6,8,10

1,3,5,10

3 1,3,5,9,10

1,3,5,6,8,10

1,3,5,10

5 1,3,5,9

1,3,5,6,8,9,10

1,3,5,9

II

6 1,3,5,6,10

6 6

8 1,3,5,8,9,10

8 8

9 5,9 1,3,5,8,9,10

5,9 II

10 1,3,5,9,10

1,3,6,8,10

1,3,10



1 1,3,10 1,3,6,8,10

1,3,10 III

3 1,3,10

1,3,6,8,10

1,3,10

III

6 1,3,6,10

6 6

8 1,3,8,10

8 8

10 1,3,10

1,3,6,8,10

1,3,10

III



6 6

6 6 IV


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8 8

8 8 IV

Step 4: Development of ISM: From the final reachability matrix, the hierarchical model

is generated. If a relationship exists between two lean criteria i and j, it is depicted by an

arrow pointing from i to j. In this model, the top level factor is positioned at the top of the

digraph and second level factor is placed at second position and so on, until the bottom

level is placed at the lowest position in the digraph. Digraph is finally converted into an

ISM as shown in Figure 2.

The barriers to lean implementation pose substantial challenge for supervisors,

middle management as well as the top management of the firms. The ISM model

highlights the major lean barriers and provides a means for analyzing the interaction

between these barriers. These barriers need to be tackled for the success in lean

implementation.

The ISM model shown in Figure 2 and the driver power-dependence diagram

shown in Figure 3 provide valuable insights into the lean implementation barriers for


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machine tool industry, and their relative importance and interdependencies. The ISM

model shows that frequent changes in design (B6) and low volume of demand (B8) are

the most significant barriers for the lean implementation process in machine tool firms,

as these two barriers form the base of the hierarchy. Then come the lack of

management commitment (B1), misunderstanding of lean (B3) and inadequate training

(B10) at second level in the hierarchy. These three barriers influence poor

communication system (B5) and longer lead time to manufacture machine tools (B9)

which lie at level three and also have bi-directional interactions. This outcome clearly

demonstrates that personnel should be imparted due training in issues related to

improving communication and managing the lead time to manufacture the machine

tools. All the three barriers can disrupt the lean criteria such as Value stream mapping,

Single minute exchange of die, Visual control, ERP, Job scheduling, and Automation.

The two barriers that occur at level 3 in the ISM model, that is B5 and B9 further bolster

the barriers that forms the top of the hierarchy at level 4 namely resistance to change

(B2), lack of organizational culture (B4) and uncertain vendor response (B7). This

brings out the fact that all other lean implementation barriers should be tackled first in

order to overcome the three barriers that form the pinnacle of the ISM model.

5. MICMAC analysis

The main objective of MICMAC analysis is to analyze the driving and

dependence power of lean implementation barriers. It gives better insights on lean

implementation to the organization so that they can proactively deal with these barriers.

The matrix of cross-impact multiplications applied to classification analysis is used to

analyze the driver power and the dependence of the variables. On the basis of

MICMAC analysis, the variables are classified into the following four clusters (Mandal

and Deshmukh 1994): autonomous, dependent, linkages and independent. The driver

power-dependence diagram shown in Figure 3 is constructed based on driving power

and the dependence of each of the barriers to lean implementation shown in final

reachability matrix (Table 5).

Driving power

Figure 3. Driving power and dependence diagram for lean implementation barriers.

10

{IV}

2,4,5,7

{III}

9

Dependent

1,3 Linkage

8

9

7

6

10

5

4

Autonomous

Independent

3

2

1

{I}

6 8 {II}

1 2 3 4 5 6 7 8 9 10


15

Figure 3 shows that there is no autonomous barrier seen in the driver-

dependence diagram. The absence of any barrier from autonomous category shows

that all the considered barriers influence the lean implementation in machine tool sector

significantly. The next cluster is that of independent barriers that include frequent

changes in design and low volume of demand. In this category, the barrier “low volume

of demand” has maximum driving power and minimum dependence and comes at

lowest level in the ISM model along with the barrier “frequent changes in design”

Therefore, it needs to be treated cautiously and has strong managerial significance.

The management should place a high priority in tackling the barrier, which have a high-

driving power, and thereby, possess the capability to influence other barriers of lean

implementation. It can be inferred that the barrier “low volume of demand” and “frequent

changes in design” are strong drivers and may be treated as the root cause of remaining

barriers. To overcome these two barriers in machine tool firms, a comprehensive

strategic plan should to be initiated to achieve effective lean implementation.

The third cluster is that of linkage barriers. They show strong driving power as

well as strong dependence. The barriers that lie in this category are relatively unstable

as any action on these barriers have an impact on other barriers and also a feedback

influence on itself. Seven barriers lie in this category namely resistance to change, lack

of organizational culture, poor communication system, uncertain vendor response, lack

of management commitment, misunderstanding of lean and inadequate training.

The last cluster is that of dependence and includes one barrier, namely longer

lead time of machine tools. This driver has weak driving power and strong dependence.

This barrier also play a key role in implementation of lean as its strong dependence

shows that all the other barrier need to be addressed for effectively overcoming this

barrier.

IRP is a ranking tool and can be applied to rank relevant factors in the light of

their performance outcomes as against ISM which limits itself to considering those

factors only. Thus, if both ISM and IRP are used for the same industry, IRP calls for

more information and yields qualitatively better and realistic results than ISM (Haleem et

al., 2012).

6. Interpretive ranking process (IRP)

IRP, a technique developed by Sushil (2009) is a novel ranking method that

combines the analytical logic of the rational choice process with the strengths of the

intuitive process at the elemental level. The methodology builds on the strengths of the

paired comparison approach (Warfield 1974, Saaty 1977) which minimises the cognitive

overload. It uses interpretative matrix as a basic tool and paired comparison of

interpretation in the matrix (Sushil 2009). The traditional AHP’s drawback that the

interpretation of judgments of the experts remains opaque to the implementer is

overcome in this method as the experts here are supposed to spell out the interpretive

logic for dominance of one element over the other for each paired comparison. Further,

IRP does not require the information about the extent of dominance. It also makes an


16

internal validity check via the vector logic of the dominance relationships in the form of a

dominance system graph. The steps of IRP (Sushil 2009) are enumerated as follows:

1: Identify two sets of variables – one to be ranked with reference to the other, e.g.

actions and performance, actors and processes, etc.

2: Clarify the contextual relationship between the two sets of variables.

3: Develop a cross-interaction matrix between the two sets of variables.

4: Convert the cross-interaction matrix into an interpretive matrix (Sushil 2005).

5: Convert the interpretive matrix into an interpretive logic of pair-wise comparisons and

dominating interactions matrix by interpreting the dominance of one interaction over the

other.

6: Develop ranking and interpret the ranks in terms of dominance of number of

interactions.

7: Validate the ranks thus derived.

8: Represent the obtained ranking diagrammatically in the form of an interpretive ranking

model.

9: Interpret the ranking order and use it as the base for recommending action.

The strong point of IRP is that it does not require the information about the extent

of dominance, which is difficult to be interpreted and generally remains questionable in

terms of validity. Also, it is easier to measure and compare the impact of interactions

rather than variables in abstract sense (Haleem et al., 2012).

IRP uses two sets of variables. One set of variables that are to be ranked, in this

case the barriers to lean implementation and the other set of reference variables that

provide the basis for ranking, in this case the performance measures (Haleem et al.,

2012). Based on inputs from industry experts, six key performance indicators have been

used in this study that include Improvement in financial profitability (P1), quality

improvement (P2), lead time reduction (P3), rise in green initiatives (P4), optimal

utilization of resources (P5), and rise in employee satisfaction (P6).

A cross-interaction matrix shows the existence or nonexistence of relationship

between each action and performance combination. Numeric ‘1’ defines a presence of

relationship exist and ‘0’ defines its absence. The cross-interaction matrix is developed

and shown in Table 10.

Table 10. Binary matrix

P1 P2 P3 P4 P5 P6

B1 0 1 0 1 0 1

B2 1 1 0 1 0 0

B3 1 0 0 0 0 0

B4 0 1 0 1 0 1

B5 1 1 1 1 1 0

B6 1 0 1 0 1 0

B7 1 1 1 0 0 0

B8 0 1 0 1 0 1

B9 0 0 1 0 0 0


17

B10 1 1 0 0 1 1

The cross-interaction matrix is converted into a cross-interaction interpretive matrix by

interpreting all the interactions with entry ‘1’ in terms of contextual relationships. For

example, (B1, P2) is interpreted as ‘Lack of management commitment to quality

improvement can lead to scarcity of resources required and a demoralized staff that

casts a negative impact on firm’s quality improvement initiatives’ as shown in Table 11.

Table 11. Interpretive matrix

P1 P2 P3 P4 P5 P6

B1

Scarcity of resources required, and demoralized staff

Scarcity of resources required, staff demoralized

No recognition, reward or incentive can increase stress

B2

Improvement initiatives demand fundamental change in approach and human behaviors

Suggestions for improvement are not well taken

Lack of enthusiasm

B3 Absurd implementation

B4

Lack of team work and coordination

Management and employee insensitivity

Can depress a self-motivated employee in long run

B5

Can lead to Delay, loss of orders

Vision and strategies are not well understood

Prolongs decision making and lead time

Reasons for initiatives and the process may not be well understood

Adversely effects resource allocation and usage

B6

Designs and process plans cannot be standardized and reused

Prolongs planning, scheduling, and tooling

Can increase waste

B7

Suboptimal supply chain performance Lack of trust

Uncertainty increases lead time

B8

Fails to enthusiast management and employees

Perceived benefits are overlooked

Demoralizes management and staff

B9

SPMs, Customized


18

machining centers have higher lead time

B10

Initiatives look like burden, waste of time and money

Objectives and tools may be wrongly used

Increased wastages

Lack of skill and knowledge leads to restlessness

In the above-paired comparisons, the ranking variables are not directly compared; rather their

interaction with respect to reference variable(s) is compared. All the dominating interactions are

summarized in the dominating interaction matrix, as shown in Table 12.

Table 12. Dominating interaction matrix

B1 B2 B3 B4 B5 B6 B7 B8 B9 B1

0

B

1

- P2,P4 P1,P4,

P6

P1,P2,

P3,P6

P2,P3,

P4

P2,P

4

P4 P4 P1 P1,

P2

B

2

P1,P

5

- P2,P4,

P5

- P1 P1,P

3

P1,P2,

P4

P1 P1 -

B

3

P2,P

5

P1 - P1,P4 P1 P1,P

2,P5

P2,P3,

P4

P2,P4,

P5

P2,P4,P5 P1,

P3

B

4

P4,P

5

- - - P1,P2,

P5,P6

P2,P

5,P6

P2,P5,

P6

P2,P3,

P4,P5

P2,P4,P6 P3,

P6

B

5

P5,P

6

P2,P3,

P5

P3,P5,

P6

P3,P4 - P3,P

4

P3,P4,

P5

P2,P3,

P5P6

P2,P3,P4

P6

-

B

6

P5,P

6

- P3,P4 P3,P4 P5 - P3,P5 P3,P4 P3,P4 P3,

P4

B

7

P2,P

3,P5

P3,P6 P4,P6 P3,P4 P2,P6 P4,P

6

- P2,P4 P3,P4 P3

B

8

P1,P

6

- P1,P6 P1,P6 P1,P4 P1,P

6

P1 - P1,P4,P6 P1

B

9

- - - - P5 P1,P

5

- P2,P3 - P5

B

10

P4,P

6

P1,P2,

P4,P6

P1,P2,

P4,P6

P1,P2,

P4

P1,P2,

P4,P6

P1,P

2,P6

P1,P2,

P4,P6

P2,P3,

P4,P6

P1,P2,P3

,P4,P6

-

Note: D- No. of cases dominating, B- No. of cases being dominated

The IRP model shown in figure 4 illustrates the ranks of various lean

implementation barriers with reference to their roles in negatively affecting different

performance areas. The arrows in the diagram signify the reference barrier(s) in the

cases where a particular ranking barrier is dominating the other ranking barrier.

Inadequate training receives the highest rank by IRP. This outcome clearly

demonstrates that any company who wants to successfully implement lean practices

must provide adequate training to its employees. Other barriers in descending order of


19

ranking are: both B4 and B5 hold rank 2, followed by B1 at rank 3. B2 and B3 hold rank

4 followed by B7, B6, B8 and B9 at ranks 5, 6, 7, and 8 respectively.

Figure 4: Interpretive ranking model of barriers to lean implementation in machine tool sector.

Conclusion

The manufacturing sector in India has shown little growth over last few decades

and firms face stiff competition from multinational companies. Periodic economic

recessions over the past several decades too, have added to the vows of the Indian

Rank I

Rank II

Rank III

Rank IV

Rank V

Rank VI

Rank VII

Rank VIII

B10- Inadequate training

Influencing: P1,P2,P3,P4,P6

B4- Lack of organizational culture

Influencing: P1,P2,P3,P4,P5,P6

B5- Poor communication system


B1- Lack of management commitment


B2- Resistance to change


B3- Misunderstanding of lean


B7- Uncertain vendor response


B6- Frequent changes in design

Influencing: P3,P4,P5,P6

B8- Low volume of demand

Influencing: P1,P4,P6

B9- Longer lead time

Influencing: P1,P2,P3,P5


20

machine tool sector. Under such conditions, eliminating all kinds of wastes assumes

high significance, making lean implementation a natural choice for the machine tool

sector. In this research study, an attempt has been made to identify the major barriers

that impede successful implementation of lean practices in the machine tool sector in

India. The study gives a comprehensive perspective regarding barriers of lean that can

be used by consultants and practiconers.

Through extensive literature review and discussions with industry practitioners,

this research identified 10 barriers for lean implementation in the Indian machine tool

sector. These include Lack of management commitment, Resistance to change,

Misunderstanding of lean, Lack of organizational culture, Poor communication system,

Frequent changes in design, Uncertain vendor response, Low volume of demand,

Longer lead time and Inadequate training. ISM methodology is used to develop the

structural model by creating SSIM, reachability matrix, level partitioning, and finally

formulation of the model. MICMAC analysis is employed to establish the driving power

and dependence powers of the 10 identified lean implementation barriers. Based on

driving power and dependence power, the barriers are assigned autonomous,

dependent, linkages and independent categories. From MICMAC analysis, low volume

of demand (B8) emerges as the barrier with highest driving power. Tackling B8 on

priority basis can have a salutary effect in managing other barriers too. IRP methodology

is used by developing binary matrix, interpretive matrix, dominating interaction matrix,

the dominance matrix and finally the IRP model. This study is perhaps among the first

few that focuses on two modeling procedures based on interpretive logic.

In the ISM model, low volume of demand has emerged as the critical driving

barrier while in the IRP, the barrier inadequate training occupies the highest rank. As is

visible from Table 11, the barrier inadequate training dominates the barrier low volume

of demand in four performance areas namely quality improvement, lead time reduction,

rise in green initiatives, and rise in employee satisfaction.

Identification of the barriers for lean implementation and development of ISM and IRP

models holds significant practical relevance and managerial implications. The research

provides the machine tool firms with critical models that can help them systematically

overcome lean implementation barriers. The proposed ISM and IRP models can aid the

machine tool firms in resetting their priorities so as to improve the lean performance.

The ISM and IRP models proposed in this work for identification of key barriers for lean

implementation can provide the decision makers a more pragmatic representation of the

problems in the course of lean implementation. A major contribution of this work lies in

the development of linkages among various barriers of lean implementation through a

systemic framework. The utility of the proposed ISM and IRP methodologies in

imposing order and direction on the complexity of relationships among elements of a

system assumes tremendous value to the decision makers. In addition, the study

reveals that rather than relying on a single tool, two or more modeling techniques can be

combined and made use of for ranking purposes.


21

Despite the fact that the ISM and IRP models developed in this work for the barriers

prominently seen in the machine tool sector, some generalization of results are still

possible. In this study, a relationship model among the barriers of lean implementation

in machine tool industries has been developed, using the ISM and IRP methodologies.

But these models have not been validated. Structural equation modeling or Step wise

multiple regression can be used for testing the validity of such models. A limitation of

ISM and IRP can be that the results may not be free from bias due to interpretive and

judgmental processes. Further, similar studies can be conducted based on other

modeling techniques such as analytical hierarchical process, analytical network process

and system dynamics. Number of lean barriers can also be extended. Similar work can

be carried out for the enablers of lean.

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