statistical validation of interpretive structural … · interpretive structural modeling (ism)...

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STATISTICAL VALIDATION OF INTERPRETIVE

STRUCTURAL MODEL WITH REFERENCE TO

BARRIERS IN IMPLEMENTATION OF TPM

Prasanth Sankar Poduval,

Research Scholar, Department of Management, Karpagam Academy of Higher Education, Coimbatore

Dr. R. Karthikeyan, Associate Professor, Department of Management,

Karpagam Academy of Higher Education, Coimbatore

Abstract

Maintenance is defined as selection of appropriate techniques to improve equipment

reliability to ensure that the output of equipments is maximized and quality products are

produced. Traditionally, the maintenance strategies include Breakdown, Preventive,

Corrective and Predictive maintenance the ultimate goal being reduction of costs and

increase in productivity and profitability. As industries struggle in an unpredictable

business environment, there is a constant need for continuous improvement to further

reduce costs. A strategy that an organization can embrace for profit maximization / cost

reduction is Total Productive Maintenance (TPM) which is a philosophy to complement

an organization‟s profitability by improving machine reliability and availability and

minimizing waste to reduce costs. The concept revolves around operators carrying out

routine maintenance while maintenance department carries out upgrades and

modifications to enhance equipment life. Organizations have failed to implement TPM

due to various factors be it behavioural, cultural or bureaucratic. In this paper,

Interpretive Structural Modeling (ISM) will be used to analyze the barriers in

implementation of TPM and the model will be statistically validated using Path

Analysis of Structural Equation Modeling.

Keywords: TPM, ISM, Path Analysis, SEM

International Journal of Pure and Applied MathematicsVolume 119 No. 15 2018, 3563-3587ISSN: 1314-3395 (on-line version)url: http://www.acadpubl.eu/hub/Special Issue http://www.acadpubl.eu/hub/

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1.0 Introduction

Industries compete in an environment of brutal competition and a climate of business

uncertainty. Organizations have to become competitive globally and excel in areas of

specialization giving them an edge over the competition. Organizations need to be

innovative to meet customer expectations. One strategy that an organization can

embrace as a management tool for improving profitability is Total Productive

Maintenance (TPM) which is a philosophy to reduce costs by improving productivity,

cutting down waste and developing quality products. This philosophy requires

operations group to carry out routine maintenance of equipments leaving the

maintenance department free to develop specialized maintenance techniques,

modification programmes and upgrades to improve reliability thereby reducing costs

and improving profitability. TPM is an equipment management programme involving

all employees of the company in the maintenance and repair of the company‟s assets

(Wireman, 1992). Machine operators are trained to perform simple maintenance and

fault finding tasks (UK Essays, 2015). It seeks to maximize equipment effectiveness

throughout its useful life period to prevent unexpected failures and quality defects

arising from process activities (Ahuja and Khamba, 2008). TPM is not a stop gap

arrangement but requires determination and diligence on part of the total work force of

an organization to bear results. Organizations have failed in their attempts to implement

TPM due to behavioural, cultural and bureaucratic challenges. The aim of this paper is

to explore the crucial elements that aid in implementation of TPM and define actions to

be taken by the management to sustain the TPM movement.

2.0 Scope of Paper

1. Determine the barriers in implementation of TPM.

2. Develop an Interpretive Structural Model analyzing the interrelationship of the

barriers.

3. Validate the model using Path Analysis of Structural Equation Modeling.

4. Formulate managerial action plan for implementation of TPM.

3.0 Interpretive Structural Modeling

ISM was created by J. N. Warfield to structure complex issues in terms of directed

graphs. Attri et al. (2013) defines ISM as a process which transforms unclear, poorly

articulated mental models of systems into well defined models. A problem is identified

and the interrelationships among the various factors in the problem are defined based on

pair wise relationship of the factors, interpretation of which is based on judgement of a

group of experts. Following a sequence of steps after identification of

interrelationships, a model is developed which shows the intensity of factors and on

which factors, more focus is to be had. By analyzing the model, the following issues

were analyzed: critical factors that affect implementation of TPM and inter- linkage of

these factors.

International Journal of Pure and Applied Mathematics Special Issue

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4.0 Study Design and Methodology

The study on barriers in implementation of TPM was carried out in the following steps:

1. A list of factors inhibiting implementation of TPM was developed by using a

questionnaire survey. The responses were statistically analyzed and top eight

impediments in implementing TPM were listed.

2. Contextual relationships among the variables were determined by statistical

analysis of a combination of questionnaire and brainstorming. A 5-point Likert

questionnaire (50 questions pruned down from 87) was administered to

candidates in the refinery sector out of which responses were obtained from 400.

The contextual relationships between the eight variables were determined by

finding out the correlation and its direction between the variables. The direction

of correlation between the variables was corroborated by having brainstorming

sessions with industry personnel. This formed the foundation blocks for the

modeling technique - Interpretive Structural Modeling

3. Using ISM, a model was developed showing the interrelationship among the

various barriers thereby helping in analyzing the critical factors that affect

implementation of TPM.

4. The model was validated, specified and properly identified by Path Analysis

using AMOS. Based on the model, managerial action plan for mitigating the

factors inhibiting implementation of TPM was formulated.

5.0 Determination of barriers in implementation of TPM

A questionnaire survey to determine ranking of barriers and contextual relationships

among the barriers was carried out by distributing questionnaire to a select group of 75

individuals (responses obtained from 50 / response rate: 66%). The respondents were

asked to rate in the order of importance the barriers in implementation of TPM with 1 –

topmost importance and 8 – last importance. The mean rankings are given below:

Variable Description Mean Ranking

Lack of Top Management Commitment 1.08

Lack of Commitment by Employees 1.92

Absence of Reward/Incentives programme 3.26

Opaque Work Culture 4.10

Insufficient Training 4.62

Operations not carrying out routine maintenance 6.44

Low Manpower 7.02

Absence of Teams 7.54

Contextual relationships among the barriers

The next step was to determine contextual relationships among the barriers by finding

out correlation between the variables. The null hypotheses were defined as below:


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Null Hypotheses

H1 There is no correlation between Top Management and Commitment by Employees

H2 There is no correlation between Top Management and Teams

H3 There is no correlation between Top Management and Manpower

H4 There is no correlation between Top Management and Work Culture

H5 There is no correlation between Top Management and Training

H6 There is no correlation between Top Management and Operations carrying out routine maintenance

H7 There is no correlation between Top Management and Rewards

H8 There is no correlation between Commitment by Employees and Teams

H9 There is no correlation between Commitment by Employees and Manpower

H10 There is no correlation between Commitment by Employees and Work Culture

H11 There is no correlation between Commitment by Employees and Training

H12 There is no correlation between Commitment by Employees and Operations carrying out routine maintenance

H13 There is no correlation between Commitment by Employees and Rewards

H14 There is no correlation between Teams and Manpower

H15 There is no correlation between Teams and Work Culture

H16 There is no correlation between Teams and Training

H17 There is no correlation between Teams and Operations carrying out routine maintenance

H18 There is no correlation between Teams and Rewards

H19 There is no correlation between Manpower and Work Culture

H20 There is no correlation between Manpower and Training

H21 There is no correlation between Manpower and Operations carrying out routine maintenance

H22 There is no correlation between Manpower and Rewards

H23 There is no correlation between Work Culture and Training

H24 There is no correlation between Work Culture and Operations carrying out routine maintenance

H25 There is no correlation between Work Culture and Rewards

H26 There is no correlation between Training and Operations carrying out routine maintenance

H27 There is no correlation between Training and Rewards

H28 There is no correlation between Operations carrying out routine maintenance and Rewards

The null hypotheses were tested by calculating the correlation coefficients and

significance of correlation between the constructs. Prior to carrying out this step, the 50

questions and the corresponding responses were first grouped under the eight constructs

(identified as the top eight variables affecting implementation of TPM). Number of

questions under each construct is given below:

Variable Description Number of questions

Lack of Top Management Commitment 9

Lack of Commitment by Employees 9

Absence of Reward/Incentives programme 5

Opaque Work Culture 8

Insufficient Training 5

Operations not carrying out routine maintenance 3

Low Manpower 6

Absence of Teams 5


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Correlation between the constructs was determined by using the median of the

responses corresponding to the questions under the respective construct was calculated

for each respondent. If the “p” value is lesser than either 0.01 or 0.05, then the

correlation is said to be significant at either of those levels. In such cases, the null

hypothesis is rejected i.e. there is significant correlation between the two variables as

the correlation coefficient is significantly different from zero. The output correlation

matrix is shown below

Correlations

Top Mgmt

Commit.

Commit. by

Employees Teams Manpower

Work

Culture Trng

Opns

Carrying out

Routine

Maintenance Rewards

Top

Management

Commitment

Pearson

Correlation 1 .271

** .157

** .211

** .199

** .159

** .232

** .188

**

Sig. (2-

tailed) .000 .002 .000 .000 .001 .000 .000

Commitment

by

Employees

Pearson

Correlation .271

** 1 .195

** .183

** .231

** .273

** .161

** .173

**

Sig. (2-

tailed) .000

.000 .000 .000 .000 .001 .001

Teams

Pearson

Correlation .157

** .195

** 1 .160

** .194

** .160

** .085 .059

Sig. (2-

tailed) .002 .000

.001 .000 .001 .089 .236

Manpower

Pearson

Correlation .211

** .183

** .160

** 1 .033 .147

** .056 .088

Sig. (2-

tailed) .000 .000 .001

.514 .003 .260 .078

Work

Culture

Pearson

Correlation .199

** .231

** .194

** .033 1 .276

** .162

** .172

**

Sig. (2-

tailed) .000 .000 .000 .514

.000 .001 .001

Training

Pearson

Correlation .159

** .273

** .160

** .147

** .276

** 1 .257

** .031

Sig. (2-

tailed) .001 .000 .001 .003 .000

.000 .539

Opns

Carrying out

Routine

Maintenance

Pearson

Correlation .232

** .161

** .085 .056 .162

** .257

** 1 .078

Sig. (2-

tailed) .000 .001 .089 .260 .001 .000

.118

Rewards

Pearson

Correlation .188

** .173

** .059 .088 .172

** .031 .078 1

Sig. (2-

tailed) .000 .001 .236 .078 .001 .539 .118

**. Correlation is significant at the 0.01 level (2-tailed).

It can be inferred from the correlation matrix that there is no correlation between the

following constructs and the null hypotheses (given in brackets) can be accepted

Teams and Operations carrying out routine maintenance (H17)

Teams and Rewards (H18)

Manpower and Work Culture (H19)

Manpower and Operations carrying out routine maintenance (H21)

Manpower and Rewards (H22)

Training and Rewards (H27)

Operations carrying out routine maintenance and Rewards (H28)


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6.0 Developing Interpretive Structural Model

The steps in developing the Interpretive Structural Model are given below:

1. Identification of the problem, variables and pair wise relationships

2. Establishing Structural Self Interaction Matrix (SSIM)

3. Developing Initial Reachability Matrix

4. Incorporating Transitivity and developing Final Reachability Matrix

5. Defining Reachability and Antecedent sets

6. Developing level partitions

7. Developing Conical Matrix

8. Building Digraph

9. Building Interpretive Structural Model

10. Classification of power of variables

Establishing Structural Self Interaction Matrix

After completion of the first step, a Structural Self Interaction Matrix (SSIM) is to be

developed. The SSIM depicts the pair wise relationship between the variables which is

built on the correlation matrix. The direction of relationship is determined by

brainstorming sessions with Subject Matter Experts (SMEs). The SSIM is given below:

F: Forward relationship from factors on Y-Axis to X-Axis i.e. Y-Axis factors influence X-Axis factors

R: Reverse relationship from factors on X-Axis to Y-Axis i.e. X-Axis factors influence Y-Axis factors

FR: Dual directional relationship between factors on Y-Axis and X-Axis i.e. Y-Axis and X-Axis factors

influence each other

X: No relationship exists between factors on Y-Axis and X-Axis

Reachability Matrix

The SSIM was converted into an Initial Reachability Matrix by employing the binary

convention as shown:

Vi: Factors on the Y-Axis and Vj: Factors on the X-Axis

8 7 6 5 4 3 2

Rewards

Opns carrying out

Routine

Maintenance Training

Work

Culture Manpower Teams

Employee

Commitment

1

Top Management

Commitment F F F F F F FR

2 Employee Commitment R F F F F F

3 Teams X X R R R

4 Manpower X X R X

5 Work Culture R F F

6 Training X F

7

Opns carrying out

Routine Maintenance X

X - Axis

Y - A

xis

Structural Self Interaction Matrix


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The Initial Reachability Matrix was converted to Final Reachability Matrix by

incorporating transitivities.

Reachability, Antecedent and Interaction Sets

From the Reachability Matrix, Reachability, Antecedent and Intersection sets are

developed as given below:

Reachability, Antecedent and Intersection Sets

Variables Reachability Set Antecedent Set Intersection Set

1 Top Management Commitment 1, 2, 3, 4, 5, 6, 7, 8 1, 2, 8 1, 2, 8

2 Employee Commitment 1, 2, 3, 4, 5, 6, 7, 8 1, 2, 8 1, 2, 8

3 Teams 3 1, 2, 3, 4, 5, 6, 8 3

4 Manpower 3, 4 1, 2, 4, 5, 6, 8 4

5 Work Culture 3, 4, 5, 6, 7 1, 2, 5, 8 5

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

7 Opns carrying out routine maint. 7 1, 2, 5, 6, 7, 8 7

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

Level Partitions

The next step was to establish Level Partitions. The iterative process took 5 iterations

to establish all the levels.

1 2 3 4 5 6 7 8

Top

Management

Commitment

Employee

CommitmentTeams Manpower

Work

CultureTraining

Opns carrying

out Routine

Maintenance

Rewards

1Top Management

Commitment1 1 1 1 1 1 1 1

2Employee


3 Teams 0 0 1 0 0 0 0 0

4 Manpower 0 0 1 1 0 0 0 0

5 Work Culture 0 0 1 0 1 1 1 0

6 Training 0 0 1 1 0 1 1 0

7

Opns carrying out

Routine

Maintenance

0 0 0 0 0 0 1 0

8 Rewards 0 1 0 0 1 0 0 1

Initial Reachability Matrix

1 2 3 4 5 6 7 8

Top

Management

Commitment

Employee

CommitmentTeams Manpower

Work

CultureTraining

Opns carrying

out Routine

Maintenance

Rewards

1Top Management


2Employee

Commitment1 1 1 1 1 1 1 1#

3 Teams 0 0 1 0 0 0 0 0

4 Manpower 0 0 1 1 0 0 0 0

5 Work Culture 0 0 1 1# 1 1 1 0

6 Training 0 0 1 1 0 1 1 0

7

Opns carrying out

Routine

Maintenance

0 0 0 0 0 0 1 0

8 Rewards 1# 1 1# 1# 1 1# 1# 1

Final Reachability Matrix


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Level-1 Partition

Variables Reachability Set Antecedent Set Intersection Set 1 Top Management Commitment 1, 2, 3, 4, 5, 6, 7, 8 1, 2, 8 1, 2, 8

2 Employee Commitment 1, 2, 3, 4, 5, 6, 7, 8 1, 2, 8 1, 2, 8 3 Teams 3 1, 2, 3, 4, 5, 6, 8 3 Level-1

4 Manpower 3, 4 1, 2, 4, 5, 6, 8 4

5 Work Culture 3, 4, 5, 6, 7 1, 2, 5, 8 5 6 Training 3, 4, 6, 7 1, 2, 5, 6, 8 6

7 Opns carrying out Routine Maint. 7 1, 2, 5, 6, 7, 8 7 Level-1

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

Level-2 Partition Variables Reachability Set Antecedent Set Intersection Set

1 Top Management Commitment 1, 2, 4, 5, 6, 8 1, 2, 8 1, 2, 8 2 Employee Commitment 1, 2, 4, 5, 6, 8 1, 2, 8 1, 2, 8 3 Teams 3 1, 2, 3, 4, 5, 6, 8 3 Level-1

4 Manpower 4 1, 2, 4, 5, 6, 8 4 Level-2

5 Work Culture 4, 5, 6 1, 2, 5, 8 5 6 Training 4, 6 1, 2, 5, 6, 8 6 7 Opns carrying out Routine Maint. 7 1, 2, 5, 6, 7, 8 7 Level-1

8 Rewards 1, 2, 4, 5, 6, 8 1, 2, 8 1, 2, 8 Level-3 Partition Variables Reachability Set Antecedent Set Intersection Set

1 Top Management Commitment 1, 2, 5, 6, 8 1, 2, 8 1, 2, 8 2 Employee Commitment 1, 2, 5, 6, 8 1, 2, 8 1, 2, 8 3 Teams 3 1, 2, 3, 4, 5, 6, 8 3 Level-1

4 Manpower 4 1, 2, 4, 5, 6, 8 4 Level-2

5 Work Culture 5, 6 1, 2, 5, 8 5 6 Training 6 1, 2, 5, 6, 8 6 Level-3


8 Rewards 1, 2, 5, 6, 8 1, 2, 8 1, 2, 8 Level-4 Partition Variables Reachability Set Antecedent Set Intersection Set

1 Top Management Commitment 1, 2, 5, 8 1, 2, 8 1, 2, 8

2 Employee Commitment 1, 2, 5, 8 1, 2, 8 1, 2, 8

3 Teams 3 1, 2, 3, 4, 5, 6, 8 3 Level-1

4 Manpower 4 1, 2, 4, 5, 6, 8 4 Level-2

5 Work Culture 5 1, 2, 5, 8 5 Level-4

6 Training 6 1, 2, 5, 6, 8 6 Level-3


8 Rewards 1, 2, 5, 8 1, 2, 8 1, 2, 8 Level-5 Partition Variables Reachability Set Antecedent Set Intersection Set

1 Top Management Commitment 1, 2, 8 1, 2, 8 1, 2, 8 Level-5

2 Employee Commitment 1, 2, 8 1, 2, 8 1, 2, 8 Level-5

3 Teams 3 1, 2, 3, 4, 5, 6, 8 3 Level-1

4 Manpower 4 1, 2, 4, 5, 6, 8 4 Level-2

5 Work Culture 1, 2, 5 1, 2, 5, 8 1, 2, 5 Level-4

6 Training 6 1, 2, 5, 6, 8 6 Level-3


8 Rewards 1, 2, 8 1, 2, 8 1, 2, 8 Level-5


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Conical Matrix

Based on the level partitions, Conical Matrix was developed as shown below:

Conical Matrix (without Transitivities)

3 7 4 6 5 1 2 8

Driving

Power

Variables Teams

Opns Routine Maint Manpower Trng

Work Culture

Mgmt Commit

Emp Commit Rew

3 Teams 1 0 0 0 0 0 0 0 1 Level-1

7

Opns Routine Maint 0 1 0 0 0 0 0 0 1 Level-1

4 Manpower 1 0 1 0 0 0 0 0 2 Level-2

6 Trng 1 1 1 1 0 0 0 0 4 Level-3

5 Work

Culture 1 1

1 1 0 0 0 5 Level-4

1 Mgmt

Commit 1 1 1 1 1 1 1 1 8 Level-5

2 Emp

Commit 1 1 1 1 1 1 1

8 Level-5

8 Rew

1

1 1 8 Level-5

Dependence

Power 7 6 6 5 4 3 3 3 37

Digraph

Digraph was drawn based on the Conical Matrix

Interpretive Structural Model

From the Digraph model, the Interpretive Structural Model was derived

Level-1

Level-2

Level-3

Level-4

Level-5

TEAMS

OPNS CARRYING OUT ROUTINE

MAINTENANCE

MANPOWER

TRAINING

TOP MANAGEMENT COMMITMENT

EMPLOYEE COMMITMENT

WORK CULTURE

REWARDS


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Mic-Mac Graph

The model shows the relationship type between the factors. To know the degree to

which how each variable influences and gets influenced by the other variables, Mic-

Mac analysis was carried out. Based on the driving power (level of influence variable

has on other variables) and dependence power (level to which variable is influenced by

other variables) derived from the Conical matrix, the variables are classified into four

categories – Autonomous, Linkage, Independent and Dependent. This is depicted on a

Mic-Mac graph

Inferences from Mic-Mac graph

1. Variables 1, 2 and 8 (Top Management Commitment, Employee Commitment

and Rewards) are the independent variables having high driving power and low

dependence power. Lack of Top Management Commitment, Lack of Employee

Commitment and Absence of Rewards are the key barriers in the

implementation of TPM. It is also seen from the model that these variables are

at the bottom of the hierarchy indicating their strong driving power.

2. Variables 4, 7 and 3 (Manpower, Operations carrying out routine maintenance

and Teams) are the dependent variables having low driving power and high

dependence power. This means that Low Manpower, Operations not carrying

out routine maintenance and Absence of Teams are dependent on other

variables. In the model, these variables are at the top of the hierarchy

(Manpower is below the other two variables as its driving power is better than

that of the other two variables).

8 1, 2, 8

7

6

5

4 5 6

3

2 4

1 7 3

1 2 3 4 5 6 7 8

MICMAC Graph

D

r

i

v

i

n

g

P

o

w

e

r

Dependence Power

Autonomous

IndependentLinkage

Dependent

Driving Power Dependence Power

Variables Autonomous Low Low

3 Teams Linkage High High

7 Opns carrying out Routine Maintenance Independent High Low

4 Manpower Dependent Low High6 Training

1 Top Management Commitment

2 Employee Commitment

5 Work Culture

8 Rewards

Legend


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3. Variables 5 and 6 (Work Culture and Training) are those variables which do not

exactly fit in any of the quadrants. From the model it is seen that they form a

linkage between the bottom level and top level variables.

7.0 Statistical Tests

Prior to statistical validation of the interpretive structural model, the responses from the

questionnaire (50 questions) were first treated using statistical analysis (SPSS23).

Replacing Missing Values

The “Series Mean” method was adopted to replace missing values.

Test for Validity

Validity of each question was tested by calculating Pearson correlation and 2-tailed

significance as explained in SPSS software. Question no. 37 was not valid and

therefore removed from the questionnaire which was pruned to 49 questions.

Test for Reliability

The updated questionnaire was tested for reliability. Cronbach Alpha value was

obtained as 0.814 which shows that the questionnaire has passed reliability test.

8.0 Path Analysis (Structural Equation Modeling)

Primary interest is to know whether the hypothesized relationships are to be accepted or

rejected and if the model fits the theoretical data. A theoretical model based on

hypothesis is specified and the raw data after cleaning is fed into the SEM software

which fits the data to the specified model and provides overall model fit statistics.

Broad steps in Path Analysis are as follows:

1. Model Specification: Specifying the model through a path diagram showing the

inter-relationships among the various variables. This is the theoretical model

based on how the researcher believes the model to be.

2. Model Identification: Refers to the relationship between free parameters (path

coefficients, variance of errors and variance of exogenous variables in the

model) and observed data (denoted as “distinct sample moments” in AMOS

calculated as (k x (k+1))/2 where k is the number of observed variables). For

SEM programme to generate results, the model needs to be properly identified.

A model is said to be identified if we are able to calculate a unique value for all

parameters of the model. The number of distinct sample moments shall equal

(just-identified model) or exceed number of free parameters (over-identified

model) in the model. “Degrees of freedom” (denoted as df) is the difference

between distinct sample moments and parameters. For a just-identified model,

df is zero while for an over-identified model, df > zero.


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3. Preparation and Input of Data: Raw data is cleaned by filling up missing

responses and removing invalid questions using any statistical package like

SPSS.

4. Model Estimation: Fitting the model to data by estimating its free parameters is

known as estimation of model. The most widely used method of estimation in

AMOS is the “Maximum Likelihood Estimation” that maximises model fit.

Even though generally used for normal data, this method can also be used for

non-normal data but with large sample sizes typically 400 or more.

5. Assessment of Model Fit: The next step is to find out how well the model fits

the observed data. This is done by calculating a variety of model fit indices like

CMIN/DF, RMSEA, NFI, CFI etc.

6. Re-specification of Model, if needed: This step is to improve model fit i.e.

modification of the model to fit the data. This is done by carrying out actions in

the model supporting the modification indices which is part of the text output of

the SEM analysis. This re-specified model is to be again assessed for model fit.

The software used for Path Analysis in this study is AMOS 22. The theoretical model is

the interpretive structural model developed earlier and the data fed into the software is

the questionnaire responses (400) to the 49 variables. These variables are grouped into

the eight factors and each factor consists of a set of variables (questions from the

questionnaire) as given earlier in this paper. The responses are then grouped according

to the grouping of variables under each factor. Median value of each response to the set

of variables under each factor is calculated. For example, under the factor “Top

Management Commitment”, median value of the responses to the nine variables is

calculated for each respondent thereby totalling 400 median responses. Similarly,

median values are calculated for the remaining seven factors to form a new data set.

This data set consisting of median response values for the eight factors is the input data

for AMOS software. The theoretical model to be validated (i.e. how well the model fits

the data reflecting the underlying theory) is the model developed during the ISM phase.

The model analysis throws up a large variety of fit indices which helps to take a

decision to either accept the model or re-specify the model. The major indices are the

following:

a. Notes for Model: This output page shows chi-square value, degrees of freedom

and probability level (p value). A high chi-square indicates an unacceptable

model fit. The value of probability level (p value) shall be greater than 0.05 for

an acceptable model fit. If the value is .05 or less, the departure of the data from

the model is significant at the .05 level (i.e. the model is not consistent with the

observed data) and the hypothesized model shall be rejected.

b. Relative chi-square: Chi-square is denoted as CMIN. Relative chi-square is

denoted as CMIN/DF where DF is the degrees of freedom. Closer CMIN value

to 1, better the model fit.


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c. Root Mean Square Residual: Smaller the RMR value, better the model fit.

Between two models, model with RMR value closer to zero has a better fit.

d. Goodness of Fit Index: Higher the GFI, better the model fit. Between two

models, model with GFI value closer to one has a better fit.

e. Comparative Fit Index and Normed Fit Index: Range of NFI value is from 0 to

1. NFI greater than 0.90 indicates a good model fit. NFI depends on sample

size and will be lower for smaller sample size. CFI is a modified form of NFI

and is independent of sample size. Closer the value is to 1, better the fit.

f. Root Mean Square Error of Approximation: RMSEA value less than 0.05

indicates a good fit while above 0.10 indicates a poor fit.

g. PCLOSE tests the null hypothesis that RMSEA is not greater than 0.05. If

PCLOSE is less than .05, the null hypothesis is rejected and we can conclude

that RMSEA is greater than .05, indicating lack of a close fit.

The model developed in the interpretive structural modeling phase is converted into

AMOS model as shown below:

The basic aim of the Path Analysis is to test

a. Whether Null hypothesis (Ho): “Model fits the observed data” is supported

b. If the following Null hypotheses within the model are supported

1. Employee commitment leads to open work culture for TPM implementation

2. Management Commitment leads to open work culture for TPM implementation

3. A policy of rewards leads to open work culture for implementation of TPM

4. In a work culture aimed at TPM, employees will learn and undergo training

5. Training installs confidence in an organization's manpower to implement TPM

6. Contribution from teams is directly proportional to manpower

7. Manpower has to be increased to implement TPM to dedicate Operations

personnel for routine maintenance


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The path diagram shows three independent variables (Top Management Commitment,

Employee Commitment and Rewards) and five dependent variables (Work Culture,

Training, Manpower, Teams and Opns Routine Maintenance). The three independent

variables are correlated (as depicted by the double headed arrows) while the single

headed arrows indicate linear causal dependencies (for example: Training depends on

Work Culture). Typically, the dependent variables are caused by the causal dependency

on other variables and some random or measurement error which encompasses all other

factors which can affect the dependent variable. This random error is not associated

with independent variables. The random errors associated with the five dependent

variables are designated as error variables e4 to e8. The text output of the AMOS

programme revealed the following:

Chi-Square: 118.145, DF: 18, Probability Level: 0.000

High chi-square with a probability level lesser than 0.05 indicates that the model is not a

good fit to the data i.e. the predicted model does not conform to the data or in other

words is significantly different at 0.05 level.

Estimates

The text output displays un-standardized and standardized regression coefficients. Un-

standardized coefficients and associated test statistics are given below:

Regression Weights: (Group number 1 - Default model)

Estimate S.E. C.R. P Label

VAR00005 <--- VAR00001 0.122 0.048 2.538 0.011 VAR00005 <--- VAR00008 0.099 0.037 2.661 0.008 VAR00005 <--- VAR00002 0.152 0.046 3.337 *** VAR00006 <--- VAR00005 0.287 0.050 5.726 *** VAR00004 <--- VAR00006 0.200 0.067 2.972 0.003 VAR00003 <--- VAR00004 0.132 0.041 3.241 0.001 VAR00007 <--- VAR00004 0.055 0.049 1.130 0.259

The un-standardized regression coefficient (Estimate in the above table) is defined as

change in the dependent variable for each one-unit change in the independent

(predictor) variable predicting it keeping other independent variables constant. For

example: when variable-2 (Employee Commitment) goes up by 1 unit, variable-5

(Work Culture) goes up by 0.152.

Standardized Regression Weights: (Group number 1 - Default model)

Estimate

VAR00005 <--- VAR00001 0.127

VAR00005 <--- VAR00008 0.132

VAR00005 <--- VAR00002 0.168

VAR00006 <--- VAR00005 0.276

VAR00004 <--- VAR00006 0.147

VAR00003 <--- VAR00004 0.160

VAR00007 <--- VAR00004 0.056


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The standardized regression weight (Estimate represented as β) represents amount of

standard deviation units change in the dependent variable as a fall out of a single

standard deviation unit change in the dependent variable. Higher the weight, greater is

the effect.

The above tables are used in conjunction to test structural relationships to determine

whether the hypothesized model fit data. The two data most relevant for hypothesis

testing are the p-value from the Un-standardized Regression Weights table and Estimate

from the Standardized Regression Weights table. There are seven relations and for

these relations to be significant, p-value should be less than 0.05. For the relation

variable-4 to variable-7, p-value is 0.259 (> 0.05) i.e. the hypothesis that variable-4

(Manpower) positively influences variable-7 (Operations carrying out routine

maintenance) is not supported at 0.05 level (two tailed).

Hypothesis β p

H1 Work Culture to implement TPM is influenced by Top Management Support 0.127 0.011 Accepted

H2 Rewards scheme inspires employees to commit themselves and change their work culture to implement TPM 0.132 0.008 Accepted

H3 Committed employees can bring about change in work culture to implement TPM 0.168 *** Accepted

H4 Employees are willing to learn and train in TPM in an organization having open work culture 0.276 *** Accepted

H5 Training installs confidence in organization's manpower to implement TPM 0.147 0.003 Accepted

H6 Contribution from teams is directly proportional to manpower 0.160 0.001 Accepted

H7

Manpower influenzes ability of operational personnel to carry out routine

maintenance 0.056 0.259

Not

Accepted

The aim is to obtain a model in which all the relations depicted in the model are

significant, which is not the case in this model as one hypothesis is not accepted.

Model Fit Summary CMIN

Model NPAR CMIN DF P CMIN/DF

Default model 18 118.145 18 0.000 6.564 Saturated model 36 0.000 0

Independence model 8 265.855 28 0.000 9.495

CMIN/DF (6.546) does not indicate a good fit as it should be closer to 1 for better fit.

RMR, GFI

Model RMR GFI AGFI PGFI

Default model 0.035 0.930 0.861 0.465 Saturated model 0.000 1.000

Independence model 0.049 0.811 0.758 0.631

Low value of RMR and values of GFI and AGFI closer to 1 indicates that the model is

satisfactory on this account.


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Baseline Comparisons

Model

NFI RFI IFI TLI

CFI Delta1 rho1 Delta2 rho2

Default model 0.556 0.309 0.596 0.345 0.579 Saturated model 1.000

1.000

1.000

Independence model 0.000 0.000 0.000 0.000 0.000

NFI (0.556) and CFI (0.579) being lower than 0.9, the model needs to be re-specified.

RMSEA

Model RMSEA LO 90 HI 90 PCLOSE

Default model 0.118 0.098 0.139 0.000 Independence model 0.146 0.130 0.162 0.000

RMSEA (0.118) > 0.10 and PCLOSE (0.000) < 0.05 indicates model is not a good fit.

Modification Indices

The Chi-square value and Model Fit indices reveal that the model is to be re-specified to

achieve a better fit which is done by carrying out changes reported in the Modification

Indices output. MI is related to statistical fit and not concerned with theory. Changes

are to be made giving due respect to theory and data and that can be explained logically.

Modification Indices (Group number 1 - Default model)

Covariances: (Group number 1 - Default model)

M.I. Par Change

e5 <--> VAR00002 16.981 0.050 e6 <--> VAR00001 8.523 0.047 e7 <--> VAR00002 6.816 0.036 e7 <--> e4 8.127 0.036

e8 <--> VAR00001 13.626 0.058 e8 <--> e4 4.302 0.032 e8 <--> e5 18.178 0.068

Variances: (Group number 1 - Default model)

M.I. Par Change

Regression Weights: (Group number 1 - Default model) M.I. Par Change

VAR00006 <--- VAR00002 18.935 0.198

VAR00006 <--- VAR00001 4.674 0.104 VAR00004 <--- VAR00008 5.195 0.120

VAR00004 <--- VAR00002 8.272 0.183

VAR00004 <--- VAR00001 14.415 0.256

VAR00003 <--- VAR00002 11.196 0.175

VAR00003 <--- VAR00001 6.220 0.138 VAR00003 <--- VAR00005 14.558 0.220 VAR00003 <--- VAR00006 7.626 0.153 VAR00007 <--- VAR00002 9.141 0.191 VAR00007 <--- VAR00001 19.434 0.294 VAR00007 <--- VAR00005 10.262 0.223

VAR00007 <--- VAR00006 24.812 0.333

The output reveals that by adding co-variances (between error variables and variables

and error variables) and regression paths between variables, the value of chi-square will

reduce by the number shown under MI of each relation (All variances being estimated,

the output is blank for model modification under Variances). For example, by adding


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regression path from var-6 to var-7, chi-square will come down by 24.812 units. As the

error variables are related to different factors, it is not logical to add co-variance path

between the error variables or between variable and error variable of another variable.

Model re-specification is carried out by performing the changes given under the

Modification Indices which are logically deemed fit in such a manner that model fit

indices are good, the estimates are significant and the chi-square value is low with a

high probability. The re-specified model is shown below:

Re-specified Model Chi-Square: 11.934, DF: 12, Probability Level: 0.451

Low chi-square with a probability level higher than 0.05 indicates that the model is a

good fit to the data i.e. the predicted model conforms to the data or in other words is

significantly not different at 0.05 level.

Re-specified Model Fit Summary

CMIN

Model NPAR CMIN DF P CMIN/DF

Default model 24 11.934 12 0.451 0.994 Saturated model 36 0.000 0

Independence model 8 265.855 28 0.000 9.495

Relative chi-square - denoted as CMIN/DF (0.994) which is close to 1 indicates a good

fit.


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RMR, GFI

Model RMR GFI AGFI PGFI

Default model 0.009 0.993 0.978 0.331 Saturated model 0.000 1.000

Independence model 0.049 0.811 0.758 0.631

Low value of RMR and values of GFI and AGFI very close to 1 indicates that the model

is a good fit.

Baseline Comparisons

Model

NFI RFI IFI TLI

CFI Delta1 rho1 Delta2 rho2

Default model 0.955 0.895 1.000 1.001 1.000 Saturated model 1.000

1.000

1.000

Independence model 0.000 0.000 0.000 0.000 0.000

As NFI and CFI values are greater than 0.9, the model is deemed to be a good fit.

RMSEA

Model RMSEA LO 90 HI 90 PCLOSE

Default model 0.000 0.000 0.051 0.946 Independence model 0.146 0.130 0.162 0.000

RMSEA (0.000) < 0.10 and PCLOSE (0.946) > 0.05 indicates that model is a good fit.

Modification Indices for Re-specified Model

The Chi-square value and Model Fit indices reveal that the model is a good fit. This

can be verified by the Modification Indices output given below which shows that there

are no further modifications to be done to improve Chi-square value.

Estimates

Un-standardized and standardized regression coefficients are given below:


Estimate S.E. C.R. P VAR00005 <--- VAR00001 0.122 0.048 2.538 0.011

VAR00005 <--- VAR00008 0.099 0.037 2.661 0.008 VAR00005 <--- VAR00002 0.152 0.046 3.337 ***

VAR00004 <--- VAR00001 0.238 0.069 3.469 *** VAR00004 <--- VAR00002 0.174 0.065 2.687 0.007 VAR00006 <--- VAR00002 0.191 0.046 4.150 ***

VAR00006 <--- VAR00005 0.235 0.050 4.703 *** VAR00006 <--- VAR00004 0.076 0.035 2.169 0.030 VAR00003 <--- VAR00004 0.107 0.040 2.668 0.008

VAR00007 <--- VAR00001 0.262 0.064 4.091 *** VAR00003 <--- VAR00005 0.185 0.058 3.207 0.001 AR00003 <--- VAR00002 0.142 0.053 2.669 0.008 VAR00007 <--- VAR00006 0.302 0.064 4.710 ***

Covariances: (Group number 1 - Default model)

M.I. Par Change

Variances: (Group number 1 - Default model)

M.I. Par Change


M.I. Par Change


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Standardized Regression Weights: (Group number 1 - Default model)

Estimate

VAR00005 <--- VAR00001 0.127

VAR00005 <--- VAR00008 0.132

VAR00005 <--- VAR00002 0.168

VAR00004 <--- VAR00001 0.175

VAR00004 <--- VAR00002 0.135

VAR00006 <--- VAR00002 0.202

VAR00006 <--- VAR00005 0.225

VAR00006 <--- VAR00004 0.103

VAR00003 <--- VAR00004 0.130

VAR00007 <--- VAR00001 0.197

VAR00003 <--- VAR00005 0.158

VAR00003 <--- VAR00002 0.134

VAR00007 <--- VAR00006 0.227

There are thirteen relations and all these relations are significant as p-value is less than

0.05.

The initial model obtained through Interpretive Structural Modeling had to be re-

specified by adding regression paths to obtain a statistically validated model. The final

model was declared to be a model fit with the observed data as validated by the model

fit summary and statistics. The model has thirteen relations or paths and all the paths

are significant with p < 0.05.

Hypothesis β p

H1 Work Culture to implement TPM is influenced by Top Management Support 0.127 0.011 Accepted

H2 Incentives inspire employees to commit and change culture to implement TPM 0.132 0.008 Accepted

H3 Committed employees bring about change in work culture to implement TPM 0.168 *** Accepted

H4 Manpower to implement TPM is influenced by Top Management Support 0.175 *** Accepted

H5 Manpower to implement TPM is influenced by commitment of employees 0.135 0.007 Accepted

H6 Training to implement TPM is influenced by commitment of employees 0.202 *** Accepted

H7 Employees learn and train in TPM in an organization having open work culture 0.225 *** Accepted

H8 Training to implement TPM is influenced by manpower of an organization 0.103 0.030 Accepted

H9 Contribution from teams is directly proportional to manpower 0.130 0.008 Accepted

H10 Top Management support empowers operating personnel to carry out routine maintenance jobs for implementation of TPM 0.197 *** Accepted

H11 Open work culture facilitates development of teams for TPM implementation 0.158 0.001 Accepted

H12 Contribution from teams is determined by commitment of employees 0.134 0.008 Accepted

H13 If training is provided, operating personnel can carry out routine maintenance 0.227 *** Accepted

As all the thirteen paths are significant, the hypothesis associated with each of the paths

as given in the above Table are supported / accepted.


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9.0 Findings and Recommendations

The model revealed that commitment of top management, commitment of employees

and instituting rewards / incentive schemes are the driving forces towards

implementation of TPM. These three driving forces affect directly or indirectly the

other five factors in the model and the action plan for implementing TPM shall consist

of actions encompassing the directions mandated by the hypothesized relationships in

the statistically validated model.

Top Management should believe that investing in TPM pays off in the long run

benefitting business and employee growth. Budget and resources are to be committed

for the pursuit of TPM which includes an independent administrative set-up,

implementation of training programmes and establishing incentive schemes. The goals

and objectives of TPM have to be clearly communicated to the employees to bring them

on board for the implementation programme. The organization will find it easier to

implement TPM once the commitment of employees is secured. Employees will

proactively take part in skill up-gradation and develop new procedures to implement

TPM. Committed management and employees can bring about a change in

maintenance practices to mitigate issues faced in implementation of TPM.

The model reveals that rewards play an important role in achieving TPM. Rewards and

incentives are viewed as tools by employees to motivate them to innovate and adopt

new technologies. Incentive scheme for TPM implementation is directly influenced by

willingness of the organization to commit resources. By having a policy of incentives,

the organization seeks to adopt an open work culture which allows employees to learn

and undergo training. Training in turn installs confidence in an organization's

manpower to implement TPM.

To implement TPM, the operations personnel have to carry out the routine maintenance

allowing the maintenance group to carry out higher order maintenance and design

modifications. Cross-functional teams help in resolving issues that hinder TPM

implementation. Contribution from teams depends on the manpower of the

organization not necessarily the size but the attitude and expertise and on the work

culture in the organization. Management should implement a work culture where-in

suggestions put forward by the employees are discussed in earnest by the top

management. This open work culture is a direct facilitator of team work in the

organization. Training courses are to be designed to develop operating personnel to a

level at which they are aware of the basic maintenance requirements of equipments

thereby allowing them to take care of these equipments.

10.0 Suggestions for future work

TPM is a worldwide phenomenon not restricted to any particular industry. This study is

carried out with emphasis on refinery industry. It is a generic research aimed at


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highlighting the broad factors which inhibit implementation of TPM. As future

research, it is worthwhile to research on factors specific to the type of industries like

auto, medical, chemical, tourism etc. Each category of industry may have its own „pain

points‟ which when attended would lead to successful TPM implementation. TPM is

one of the lean manufacturing strategies which strive to eliminate waste from

manufacturing processes. It is worthwhile to carry out research on how the various lean

strategies (bottleneck analysis, JIT, Value Stream Mapping etc.) in conjunction with

TPM benefits an organization. TQM is a strategy to improve quality. Research can be

carried out to study the impact of TPM and TQM on production process when both the

programmes are implemented simultaneously. Fuzzy ISM (FISM) is a technique which

is an improvement over the conventional ISM technique. It involves giving a value to

the strength of contextual relationship, quantified on a 0-1 scale (Alawamleh and

Popplewell, 2011). This study can be carried out by using FSIM and validating the

model using SEM.

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