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Page 1 of 24
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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Page 2 of 24
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:
International Journal of Pure and Applied Mathematics Special Issue
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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
International Journal of Pure and Applied Mathematics Special Issue
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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
Commitment1 1 1 1 1 1 1 0
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
Commitment1 1 1 1 1 1 1 1
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
International Journal of Pure and Applied Mathematics Special Issue
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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
7 Opns carrying out Routine Maint. 7 1, 2, 5, 6, 7, 8 7 Level-1
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
7 Opns carrying out Routine Maint. 7 1, 2, 5, 6, 7, 8 7 Level-1
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
7 Opns carrying out Routine Maint. 7 1, 2, 5, 6, 7, 8 7 Level-1
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
International Journal of Pure and Applied Mathematics Special Issue
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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:
Regression Weights: (Group number 1 - Default model)
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
Regression Weights: (Group number 1 - Default model)
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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