chapter 4 status of e-business application...
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CHAPTER 4
STATUS OF E-BUSINESS APPLICATION SYSTEM AND
ENABLERS IN SCM OF MSMEs
4.1 PREAMBLE
This chapter deals with analysis of data gathered through
questionnaire survey to bring out
• The profile of MSMEs
• Status of e-business application system
• E-business enablers in SCM of MSMEs
• Status of SC enablers
• Status of factors available to support SCM
The profile of the MSMEs was established in terms of number of
years experience of the firm in business and the category to which each one of
them belong. The status of e-business application systems, e-business
enablers and SC enablers to support MSMEs was analyzed using Friedman
test. Factors that support the SCM include bench marking of SCM activities
with that of the best in class of organization, emphasis of company strategy in
SCM, emphasis of top management in SCM, emphasis on SCM infrastructure
and attributes are measured by applying Friedman test.
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To test the generic hypothesis formulated a Chi square test was
applied. It is an important non-parametric test, which does not require rigid
assumption about the type of population. This test was employed to find the
significance of association between the following : usage of e-supply chain
systems and MSMEs, between the success of managing SCM and the e-
business systems in use, between the benefits of usage of e-supply chain
components and MSMEs. Multiple regression analysis was used to measure
the influence of top management in SCM, emphasis on bench marking of
SCM activities with that of the best in class of organizations and the influence
of SC enablers on benefits of using the enablers in MSMEs.
4.2 PROFILE OF MSMEs
4.2.1 Categorization and Experience of MSMEs
According to the published reports of Government of India there
were 1.56 million micro, small and medium enterprises in 2011-12.
Categorywise distribution of the MSMEs is given below in Table 4.1.
Table 4.1 Percentage analysis of nature of MSMEs
Type of MSME
Percentage as per the MSMEs report 2011-12
Percentage of firms as per survey for research
Micro 94.94 87.79
Small 4.89 9.92
Medium 0.17 2.29
For the survey, though 400 firms were targeted and approached,
finally the response for the schedule could be obtained from 131 firms (which
forms 32%), the distribution of category-wise MSMEs respondents is also
given in the Table 4.1. Comparison of the distributions of actual number of
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firms and the distribution of firms responded to the questionnaire schedule,
they do not closely agree. The reason for the mismatch in both the column
value is due to the fact that one is an all India figure (population figure)
comprises of manufacturing and service MSMEs, while the MSMEs covered
in the sample survey are mainly manufacturing ones and belong to
geographically small area compared to the other. If the sample size is more
and cover a wider area, the mismatch will get reduced.
To assess the experience of firms responded from the data gathered,
the number of years of existence has been grouped as indicated in Table 4.2.
Table 4.2 Analysis of years existence of firms in business
Years of experience No of firms Percentage of firms
Less than 5 years 31 23.7
5 - 10 years 50 38.2
10-15 years 25 19.1
more than 15 years 25 19.1
Total 131 100.0
From Table 4.2 it may be seen that 38.2% firms have experience
between 5-10 years followed by 19.1% respondents between 10-15 years and
the remaining 19.1% respondents having more than 15 years of experience.
Thus, as high as 76% of firms surveyed have more than 5 years of experience,
while the average life of the MSMEs surveyed is around 9 years. Five years
can be considered as a reasonable span of life of firms especially MSMEs, by
which time, it should be possible for them to achieve stabilization and be in a
position to adopt ICT tools to improve their business prospects.
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4.2.2 Success of Managing the Supply Chain of MSMEs
To assess the level of success of managing the supply chain of
MSMEs, a percentage distribution analysis was made. The Table 4.3 gives the
details.
Table 4.3 Percentage analysis of success of managing the supply chain
in MSMEs
Level of Success No of firms Percentage of firms
Not successful 2 1.5
Somewhat successful 49 37.4
Successful 61 46.6
Very successful 19 14.5
Total 131 100.0
From the Table 4.3 it is evident that, 61.1 % of the firms are either
successful or very successful in managing the SCM, while 37.5% of firms are
somewhat successful in managing SCM. The percentage of firms not
successful is hardly 1.5%. Obviously this indicates that firms with good
infrastructure and good education background of the owner are managing
their supply chain successfully. The firms, that are somewhat successful in
managing the supply chain, are in the process of adoption and implementation
of the e-business tools for the success of SCM.
4.2.3 Status of the e-business Systems in MSMEs
As high as 12 e-business systems were considered in the analysis to
ascertain the percentage of firms using which type of package or none. The
status established based on the survey is given in Table 4.4.
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This analysis indicate that 67.9% of the MSMEs are using MRP
either standard or custom made package. Comparatively lesser percentage,
54.9% of the MSMEs are using MRPII either standard or custom made
package because of its vast application.
Usage of ERP package by MSMEs appear significant from
Table 4.4. It is to be noted that as high as 72.5% of the MSMEs are using
ERP either in the standard form or custom made due to its availability at
lesser cost. Totally 57.2% of MSMEs are only using either standard or custom
made package of Warehouse Management System (WMS).
From the point of view of MSMEs, maintaining of proper
relationships both with the customer and supplier are equally important. The
survey reveals that 52.7% and 61.1% of MSMEs are using CRM and SRM
packages respectively either standard or custom made. The use of custom-
made packages is more prevalent than the use of standard package.
From Table 4.4, it is evident that the usage of JIT and APS packages
are not significant. Only 30.5% of firms use JIT supply aiding package and
37.5% use APS package . The MSMEs may have to look in to this aspect
seriously.
The usage of RFID package by MSMEs is very low 11.5% only.
Similarly only 40.5% of them use DSS systems either standard or custom-
made form. Further, it may be noted that 35.8% of the MSMEs use EDI
either standard or custom made package while 51.9% of the MSMEs are
using barcode.
This analysis reveal that the usage of APS, IT, DSS, RFID, EDI and
Bar coding is not quite high as more the 50% of firms do not use any of these
softwares.
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4.2.4 Association between the e-business Systems Available and
Category of MSMEs
Chi-square test was used to find the association between e-business
systems available and the category of MSMEs. A Cross tabulation was
prepared to test whether there is a significant difference between observed
frequency distribution and a theoretical frequency distribution. In this
manner, the fitness of the distributions between two groups of variables can
be found out. Through this test, the dependency between MSMEs and the
usage of e- business systems can be established. The null hypothesis
formulated is,
H0: E-business systems currently available in MSMEs depend on the
category of MSMEs ( Micro, small and Medium Enterprises)
The Pearson Chi-square value and the significance value is given in
Table 4.5 between each category of MSMEs and e-business systems.
From the Table 4.5, it is evident that the P value is less than 0.05
only in respect of two e-business systems viz., ERP and WMS and the
category of MSMEs, hence the null hypothesis is rejected at 5 percent level of
significance. Thus, there is statistical evidence to confirm the association
between Enterprise Resources Planning (ERP), Warehouse Management
System (WMS) and category of MSMEs. This may be due the size of the
firm having direct influence with use of WMS and ERP for the whole
industry. All the rest of the components don’t have any association with the
category of industry. But, irrespective of the category, all of them use e-
business systems.
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Table 4.5 The Pearson Chi-square values for association between the
e-business systems currently available in MSMEs and
category of MSMEs
Sl.NoBetween MSME category and e-
business system
Pearson
Chi-Square
Asymp. Sig.
(2-sided)
1 Material Requirement Planning (MRP) 2.645 0.619
2 Manufacturing Resources Planning(MRPII
3.037 0.552
3 Enterprise Resources Planning (ERP) 11.144 0.025*
4 Warehouse Management System (WMS) 15.872 0.003**
5 Supply Chain Management module (SCM)
5.748 0. .219
6 Customer Relationship Management (CRM)
7.633 0.106
7 Supplier Relationship Management (SRM)
8.079 0.089
8 Advanced Planning System (APS) 5.094 0.278
9 Just in time (JIT) 8.011 0. .091
10 E-business 1.790 0.774
11 Decision support / expert System 2.178 0.703
12 Radio frequency Identification(RFID) 2.357 0.670
13 Electronic data interchange (EDI) 4.592 0.332
14 Bar coding 5.382 0 .250
** denotes significance at 1% level * denotes significance at 5% level
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4.2.5 Association between Success in Managing SCM and e-business
System Currently Available
As discussed in the previous section, Chi-square test was employed
to find the association between the success in managing the SCM in MSMEs
and the e-business system currently available. The null hypothesis formed for
this purpose is,
H0: Success in managing the SCM in MSMEs depends on the e-
business system
Table 4.6 Chi-square value for association between SCM components
and success in managing SCM
Sl.NoSuccess in managing SCM vs
e-business system
PearsonChi-
Square
Asymp. Sig.
(2-sided)1 Material Requirement Planning (MRP) 14.994 0.020*2 Manufacturing Resources Planning
(MRPII) 12.845 0.0456*
3 Enterprise Resources Planning (ERP) Successful in Managing (SCM) 17.170 0.008**
4 Warehouse Management System (WMS) 12.876 0.0450*5 Customer Relationship Management
(CRM) 13.151 0.0407*
6 Supplier Relationship Management (SRM) 10.716 0.098
7 Advanced Planning System (APS) 7.788 0.2548 Just in time (JIT) 17.898 0.006**9 Decision support (DSS) 4.761 0.57510 Radio frequency Identification (RFID) 13.975 0.030*11 Electronic data interchange (EDI) 18.699 0.0048**12 Bar coding (BC) 18.094 0.006*
** denotes significance at 1% level * denotes significance at 5% level
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From the Table 4.6, it is evident that the P value is less than 0.05 in
respect of all the e-business systems except SRM and DSS and hence the null
hypothesis rejected at 5 percent level of significance in respect of these.
Therefore, it is concluded that there is a statistical evidence for an association
between successful in managing SCM and the following e-business systems
Material Requirement Planning (MRP)
Manufacturing Resources Planning (MRPII)
Enterprise Resources Planning (ERP)
Customer Relationship Management (CRM)
Warehouse Management System (WMS)
Just in time (JIT)
Radio frequency Identification (RFID)
Electronic data interchange (EDI).
4.3 E-BUSINESS ENABLERS AND MSMEs
4.3.1 Status of e-business Enablers in MSMEs
To analyze the status of e-business enablers in MSMEs, the
distribution of firms in percentage that are currently using, planned to use,
under consideration and will never use was made. The Table 4.7 provide the
details.
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Table 4.7 Percentage analysis of status of e-business enablers with
respect to SCM
Sl. No
e-business enablers
Count&%
Never Considering Planned Currently using Total
1 e-procurement Count 13 59 23 36 131% 9.92 45.04 17.56 27.48 100.00
2 E-auctions for procurement
Count 23 47 29 32 131% 17.56 35.88 22.14 24.43 100.00
3 Retail e-payments
Count 14 40 18 59 131% 10.69 30.53 13.74 45.04 100.00
4 Retail transfer e-payments
Count 10 42 23 56 131% 7.63 32.06 17.56 42.75 100.00
5 Certifications for security of payments
Count 7 34 41 49 131% 5.34 25.95 31.30 37.40 100.00
6 Wholesalers e-payments
Count 17 36 28 50 131% 12.98 27.48 21.37 38.17 100.00
7 Electronic signature
Count 28 36 30 37 131% 21.37 27.48 22.90 28.24 100.00
8 Electronic ID Count 15 47 20 49 131% 11.45 35.88 15.27 37.40 100.00
9 Electronic document management
Count 11 36 41 43 131% 8.40 27.48 31.30 32.82 100.00
10 Collaborative tools for e-business
Count 27 35 25 44 131% 20.61 26.72 19.08 33.59 100.00
11 Internet Count 0 0 2 7 122 131% 0.0 1.52 5.34 93.12 100.00
12 Order processing
Count 1 9 29 92 131% 0.76 6.87 22.14 70.23 100.00
13 Follow up Count 1 10 34 86 131% 0.76 7.63 25.95 65.65 100.00
14 Onlinemarketing
Count 9 24 32 66 131% 6.87 18.32 24.43 50.38 100.00
Total Count 176 457 380 821 1834% 9.60 24.92 20.72 44.77 100.00
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From Table 4.7, it is evident that 27.5% of firms are using the
e- business enabler e-procurement currently while 24.4% of the firms resort to
e- business enabler e-auctions for procurement. The number of firms planning
to use the e-business enabler e-auctions for procurement form 22.1%.
The analysis reveal that 42.7% of firms are using the e- business
enabler retail e-payments currently; 37.4% for certifications for security of
payments and 31.3% of the firms are planning to use retail transfer
certifications for security of payments.
Only 37.4% of the firms are found using the e-business enabler
wholesalers e-payments, while 28.2% use the electronic signature and 22.9%
of the firms are planning to use the e-business enabler electronic signature.
Evidence also indicate that currently 37.4% of the firms are using
e-business enabler electronic ID; 32.8% of the firms use electronic document
management; the remaining 31.3% of the firms are planning to use electronic
document management.
Analysis reveal that currently 33.6% of the firms are using
collaborative tools for e-business currently. The number of firms found using
the e-business enabler internet currently account for 93.12%, while 5.34% of
the firms are planning to use internet.
From Table 4.7, it may be seen that currently 70.2% of the firms are
using enabler order processing; 22.1% of the firms are planning to use for
order processing and 6.9% of the firms are only aware of the benefits of
order processing and not using.
It may be seen from Table 4.7, 65.6% of the firms are adopting
follow up currently; 50.4% of the firms use for online marketing and 24.4%
of the firms are planning to use it.
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4.3.2 Status of SC Enablers in MSMEs
The Friedman test is normally applied to data with repeated-
measures designs or matched-subjects designs. With repeated-measures
designs, each item is a case in the data file and has scores on ‘k’ variables.
From the rating score obtained on each of the “k” occasions, one can
determine whether there are significant difference in the rating of items based
on the mean rank, standard deviation and Chi-square values.
Table 4.8 Mean rank and standard deviation towards the status of SC
enablers
Sl.No SC enabler MeanRank
Std. Deviation
1 Close partnership with customers 3.25 0.8822 Close partnership with suppliers 3.18 0.9273 Holding safety stock 2.88 1.0154 Many suppliers 2.77 1.042
5Strategic Planning in Procurement and Distribution 2.73 0.991
6 JIT supply 2.66 1.0517 Third Party Logistics(3PL) 2.6 1.0588 Sub contracting 2.56 1.0779 Few suppliers 2.51 0.995
10 E-procurement 2.5 1.12611 Vertical Integration 2.47 1.01812 Supply chain benchmarking 2.4 0.98213 Use of external consultants 2.31 1.08214 Electronic data interchange (EDI) 2.28 1.03215 Out sourcing 2.23 1.147
Range of mean rank : (1- 1.75) Not appropriate ; (1.76 – 2.5) Improve;
(2.51 – 3.25) -Start implementing; (3.26 – 4) Satisfied already
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To assess the status of supply chain enabler in MSMEs the
Friedman test was employed to arrive at the mean rank and standard
deviation. The range of mean rank values to identify the status of SC enablers
was calculated based on the average value as interval width. In this case the
minimum rank value is 1, the maximum rank is 4 and the interval width is
0.75. Totally four class intervals adopted, since a four point rating scale was
used. The standard deviation values can be used to supplement the inference
of mean rank values.
From Table 4.8, it is clear that the following SC enablers of MSMEs
have mean rank between 2.51 to 3.25. Obviously they have started to
implement them
close partnership with customers
close partnership with suppliers
holding safety stock
many suppliers
strategic Planning in Procurement and Distribution
JIT supply
third Party Logistics(3PL)
sub contracting
few suppliers
The enablers mentioned below have secured mean rank between
1.76 to 2.5 and hence, they should improve their position.
e-procurement
vertical Integration
supply chain benchmarking
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use of external consultants
electronic data interchange (EDI)
out sourcing
4.3.3 Benefits of SC Enablers
To find out the benefits of SC enabler in MSMEs, the Friedman test
was applied to establish the mean rank and standard deviation. The range of
mean rank values to identify the benefits of SC enablers was calculated based
on the average value as interval width.
Table 4.9 Mean rank and standard deviation towards benefits SC
enablers
Sl.No SC enablers Number Mean
RankStd.
Deviation1 Increased coordination with
customers 131 3.49 0.807
2Increased coordination with suppliers 131 3.36 0.929
3 Increased sales 131 3.26 0.764 Better quantity of information 131 3.21 0.9015 Flexibility in operation 131 3.17 0.938
6Reduced lead-time in manufacturing 131 3.13 1.091
7 Better operational efficiency 131 3.13 0.7988 Better quality of information 131 3.12 0.9379 Cost saving in manufacturing 131 3.08 0.942
10Increased coordination between departments 131 3.06 1.072
11 Improved Forecasting 131 3.05 0.8812 More accurate costing 131 2.98 0.88113 Improved Resource planning 131 2.95 0.97114 Reduced inventory Level 131 2.92 0.92
Range of mean rank : (1-1.8) - Not at all; (1.9-2.7) - Little;
(2.8-3.5) - Average; (3.6-4.2) – Greatly (4.2-5) - A lot
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In this case the minimum rank value is 1, the maximum rank is 5
and the interval width is 0.8. Totally five class interval adopted since a five
point rating scale was used.
It is clear that all the benefits of SC enablers in MSMEs listed in the
above table have mean rank between 2.8 to 3.5. Obviously all the above
factors influence the MSMEs at an average level.
4.3.4 Association between SC enablers and the Category of MSMEs
As discussed in the section 4.2.4, the Chi-square test was used to
find the association between the SC enablers and the category of MSMEs.
The null hypothesis formed for this purpose was,
H0: Benefits gained by using SC enablers depends on the category of
MSMEs
Table 4.10 Chi-square value for association between category of
industry and SC enablers
Sl:No Between MSME category and SC enabler
PearsonChi-Square
Asymp. Sig. (2-sided)
1 Better quality of information 24.826 0.002**
2 Better quantity of information 19.848 0.011*
3 Flexibility in operation 20.078 0.010*
4 Reduced lead-time in manufacturing 10.988 0.202
5 Cost saving in manufacturing 18.718 0.016*
6 Improved Forecasting 4.732 0.579
7 Improved Resource planning 15.532 0.049*
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Table 4.10 (Continued)
Sl:No Between MSME category and SC enabler
PearsonChi-Square
Asymp. Sig. (2-sided)
8 Better operational efficiency 5.321 0.503
9 Reduced inventory Level 16.424 0.037*
10 More accurate costing 19.846 0. 276
11 Increased coordination between departments 7.352 0.499
12 Increased coordination with suppliers 15.993 0.043*
13 Increased coordination with customers 16.862 0.032*
14 Increased sales 5.359 0.719
From Table 4.10, it is evident that the P value is less than 0.05 in
respect of 8 items and hence the null hypothesis was rejected at 5 percent
level of significance. Thus, there is statistical evidence establishing
association between category of MSMEs and the following SC enablers.
better quality of information
better quantity of information
flexibility in operation
cost saving in manufacturing
improved resource planning
reduced inventory level
increased coordination with suppliers
increased coordination with customers.
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The remaining six SC enablers out of the 14 considered do not have
any significant association with the category of MSMEs.
4.3.5 Establishment of Relationship between Benefits and SC
Enablers
In order to establish relationship between the benefit accrued and
SC enabler, a multiple regression analysis was adopted. Here, the benefits
accrued are the dependent variable while the SC enablers are the predictor or
independent variables. A stepwise regression approach was used by adding
variable one at a time and checking its contribution and the variable that
contributes to the model was retained. All other variables in the model are re-
tested to see if they are still contributing to the success of the model. If they
no longer contribute significantly they are removed. Thus, this method
ensures that the smallest possible sets of predictor variables that contributes
are included in the model.
Multicollinearity occurs when independent or predictor variables are
highly correlated with each other. It is difficult to establish with reliable
estimates of their individual regression coefficient using beta weight (Garson,
2008). To avoid occurrence of multicollinearity, tolerance indicator of more
than 0.1 and variation inflation factors (VIF) not greater than 10 (Ooi et al
2007) were used. The threshold value of condition index is 15-30, with 30 as
the most commonly used value.
Of the 15 supply chain (SC) enablers used in the stepwise multiple
regression, only two enablers found contributing to the benefits. The
dependent and independent variables finally established are,
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Dependent variable : Benefits of using SC enablers (Y)
Independent variables : i)Strategic planning in procurement and
distribution (X1)
: ii) Close Partnership with customers (X2)
Table 4.11 gives the value of coefficients, correlation coefficient
(R), R2 and adjusted R2 along with F value and P-values for each model
considered. The -coefficient, t-value, collinearity statistics and conditional
index are given for each model considered in Table 4.12.
Table 4.11 Model fit coefficient value for the analysis between benefits
and SC enablers in MSMEs
Model R R SquareAdjusted R
SquareF value P value
1 0.401 0.160 0.154 24.653 <0.001**
2 0.517 0.267 0.256 23.308 <0.001** ** denotes significance at 1% level
Table 4.12 coefficient, t value and significance value for the analysis
between benefits of using the SC enablers and availability of
SC enablers in MSMEs
Model Variable
Unstandardized Coefficients
Standard.Coeff. t -
valueSig.
Collinearity Statistics Condition
IndexBeta Std.Error Beta Tol. VIF
1 (Constant) 33.890 2.142 - 15.82 <0.001** - - 1.000(X1) 3.661 0.737 0.401 4.965 <0.001** 1.000 1.000 5.712
2 (Constant) 24.084 3.034 - 7.937 <0.001** - - 1.000(X1) 3.207 0.700 0.351 4.585 <0.001** 0.977 1.023 5.896(X2 ) 3.389 0.786 0.330 4.313 <0.001** 0.977 1.023 9.507
** denotes significance at 1% level
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From Table 4.11, it is evident that for model-1, the multiple
correlation coefficient (R value) is 0.401 which indicates the degree of
relationship between the strategic planning in procurement and distribution
with the benefits of using e-SCM components. It shows a positive relationship
between the dependent variable and the independent variable viz., strategic
planning in procurement and distribution.
For the model 2 a second variable namely close partnership with
customers (X2) was added. It may noted that the multiple correlation
coefficient now increased to 0.517. This implies that close partnership with
customers also contribute to the benefits of using e-SCM components. From
the final model with two independent variables the value of R square is 0.267
which explain that 26.7% of the variation in benefits of using e-SCM
components is on account of these two independent variables. The R square
value is also significant at 1% level. The multiple regression equation
developed is
Y = 24.087 + 3.207 X1 +0.786 X2
The -coefficient of X1 is 3.207, which represents the partial
positive effect of strategic planning in procurement and distribution (X1) on
benefits of using e-SCM components(Y), holding the variable close
partnership with customers(X2) constant and this coefficient value is
significant at 1% level.
Similarly, the -coefficient of X2 is 0.786 and represents the partial
positive effect of close partnership with customers(X2) on benefits of using
e-SCM components (Y), holding the variable strategic planning in
procurement and distribution (X1) constant and this coefficient is significant
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at 1% level. Also it may be noted from table 4.12 that, all the condition index
values are less than 30 and the VIF value is less than 10 indicating no
multicollinearity in this analysis.
Each sub factors of the dependent variable was measured with a five
point rating scale viz., 1-Not at all; 2- Little; 3-Average; 4-Greatly; 5-A lot.
Totally fifteen sub factors used for the independent variable. While
constructing the multiple regression model the rating values of all the sub
factors were summed up. Hence, for establishing the value of the dependent
variable in this model, the range of rating scale values adopted is; 1-15 not at
all; 16-30 little; 31-45 average; 46-60 greatly and 61-75 a lot.
4.4 SUPPORTS FOR SUPPLY CHAIN MANAGEMENT OF
MSMEs
4.4.1 Emphasis of Company Strategy in SC
To assess the support for SCM based on emphasis of company
strategy in MSMEs, the Friedman test was applied to find the mean rank and
standard deviation. The range of mean rank values to identify emphasis of
company strategy was calculated based on the average value as interval
width. In this case the minimum and maximum are 1 and 5 rank value
respectively and the interval width is 0.8. The number of class intervals
adopted was five, since a five point rating scale was used. The mean rank and
standard deviation for the items considered is given in Table 4.13.
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Table 4.13 The mean rank and standard deviation towards emphasis of
company strategy in SCM
Sl.No
Emphasis of company strategy in SCM Number
MeanRank
Std. Deviation
1 On offering products with the best quality and yet with a minimum price
131 3.24 0.851
2 On reducing the lead time in the supply chain 131 3.16 0.83
3 On producing innovative and technologically superior products 131 3.13 0.872
4 On ensuring the product are readily available on the shelf in the market
131 3.1 0.858
5 On offering returns management solutions 131 3.09 0.818
Range of mean rank: (1-1.8) - Not at all; (1.9 -2.7) - Little;
(2.8-3.5) -Average; (3.6-4.2) - Greatly; (4.2 – 5) - A lot.
From Table 4.14, it is clear that the following company strategy of
MSMEs have mean rank between 2.8 to 3.5. This indicates that the MSMEs
are averagely influenced by,
offering products with the best quality and yet with a
minimum price
on reducing the lead time in the supply chain
on producing innovative and technologically superior products
on ensuring the product are readily available on the shelf in
the market
on offering returns management solutions.
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4.4.2 Emphasis of Top Management in SCM
In order to establish the support for SCM based on emphasis of top
management in MSMEs the Friedman test was applied to find the mean rank
and standard deviation. The range of mean rank values to identify emphasis
of top management was calculated taking the average value as interval width.
In this case the minimum rank value is 1, the maximum rank is 5 and the
interval width is 0.8. A total of five class intervals were adopted since a five
point rating scale was used. Table 4.14 gives the details of mean values and
standard deviation.
Table 4.14 Mean rank and standard deviation towards emphasis of top
management in SCM
Sl.No Emphasis on top management factors Number
MeanRank
Std. Deviation
1 Has a very clear customer and shareholders focus 131 3.34 0.83
2 Ensures a good internal communication and dialogue process
131 3.34 0.73
3 Supports the acquisition and implementation of appropriate information system across departments and across the supply chain
131 3.23 0.837
4 Commits adequate resources for effective SCM 131 3.21 0.765
5 Ensures performance measures are aligned with the SCM Strategy 131 3.1 0.812
Range of mean rank : (1-1.8) - Not at all; (1.9 -2.7) - Little;
(2.8-3.5) – Average (3.6-4.2) - Greatly; (4.2-5) - A lot.
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All the factors taken in consideration for the emphasis of top
management in SCM have mean rank between 2.8 to 3.5. On the basis of this
it can be considered that the MSMEs are influenced averagely by these five
factors viz., clear customer and shareholders focus, ensuring a good internal
communication, support the acquisition and implementation of appropriate
information system across departments and supply chain, commitment of
adequate resources for effective SCM and ensuring performance measures
that are aligned with the SCM strategy.
4.4.3 Importance of SCM Attributes
As in the previous case, to find out the support for SCM based on
SCM attributes in MSMEs the Friedman test was applied to establish the
mean rank and standard deviation. The range of mean rank values to identify
emphasis of top management was calculated based on the average value as
interval width. The minimum and maximum rank value are 1 and 5
respectively with interval width as 0.8. Totally five class intervals were
adopted since a five point rating scale was used. The result of the Friedman
test is given in Table 4.15.
From Table 4.15, it is obvious that only two SCM attributes of
MSMEs viz., team work and reduced inventory level have mean rank
between 3.6 to 4.2 indicating that they influence the MSMEs greatly. The
attributes listed below have secured the mean rank between 2.8 to 3.5 and
hence, their level of influence is only average in MSMEs .
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Table 4. 15 Mean rank and standard deviation towards importance of
SCM attributes
Sl.No
SCM attributes NMeanRank
Std. Deviation
1 Team Work 131 3.99 0.9572 Reduced inventory level 131 3.56 0.8953 Response time 131 3.44 0.8784 Strategic sourcing 131 3.39 0.8735 Use of SCM applications software 131 3.3 0.9426 Vendor managed inventory 131 3.25 0.7687 Information sharing with the supplier 131 3.21 0.8688 JIT Supply 131 3.15 0.9629 Electronic Data Interchange (EDI) 131 3.11 0.96310 Supply Chain Benchmarking 131 3.05 0.52412 Third Party Logistics 131 3.05 0.85813 E-procurement 131 3.02 0.80814 Subcontracting 131 2.95 0.727
Range of mean rank : (1-1.8) - Not at all; (1.9 -2.7) - Little;
(2.8-3.5) - Average; (3.6-4.2) - Greatly; (4.2-5) - A lot.
response time
strategic sourcing and
vendor managed inventory.
information sharing with the supplier
JIT Supply
electronic Data Interchange (EDI)
supply Chain Benchmarking
Third Party Logistics (3PL)
E-procurement
subcontracting
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4.4.4 Emphasis on e-business Infrastructure Requirement
The emphasis on e-business infrastructure requirement to support
MSMEs was found out through mean rank and standard deviation by
applying the Friedman test results. The range of mean rank values were
calculated taking on the average value as interval width. In this case also the
minimum rank value is 1, with the maximum rank as 5 and the interval width
is 0.8. A total of five class interval were adopted since a five point rating
scale was used. The results are reported in Table 4.16.
Table 4.16 Mean rank and standard deviation towards emphasis of e-
business infrastructure requirement
Sl.No
e-business infrastructures NumberMean
RankStd.
Deviation
1 Various departments, offices and branches are electronically linked for better coordination
131 2.88 1.116
2 Trading partners have access to the organization’s real Time dynamic information through secure extranet sites
131 2.66 0.991
3 The information system is periodically reviewed and technologically updated to respond to ever increasing requirements
131 2.79 0.977
4 Information systems are regularly updated with accurate and timely information
131 2.93 0.954
Range of mean rank : (1-1.8) - Not at all; (1.9 -2.7) - Little;
(2.8-3.5) - Average; (3.6-4.2) - Greatly; (4.2-5) - A lot.
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From Table 4.16, it is noted that the following two e-business
infrastructures of MSMEs have mean rank between 2.8 to 3.5 stressing the
fact that MSMEs are influenced averagely by these two viz.,
regular updation of information systems with accurate and
timely information
linking of various departments, offices and branches
electronically for better coordination.
The other two factors namely periodic review of the information
system and technological updation to respond to ever increasing
requirements, and accessibility by trading partners to organizations real time
dynamic information through secure extranet sites have mean values
marginally less than average.
4.4.5 Bench Marking of SCM Activities
The support for SCM based on Bench marking of SCM activities in
MSMEs was tested using the Friedman test to find the mean rank and
standard deviation. The range of mean rank values to identify bench marking
of SCM activities was calculated based on the average value as interval
width. In this case, the minimum rank value is 1, the maximum rank is 5 and
the interval width is 0.8. Totally five class intervals were adopted since a five
point rating scale was used. The results are given in Table 4.17.
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Table 4.17 Mean rank and standard deviation towards bench marking
of SCM activities
Sl.No Bench marks of SCM NumberMeanRank
Std. Deviation
1 Manufacturing 131 3.7 0.909
2 Customer focus 131 3.64 0.886
3 Performance metrics 131 3.63 0.862
4 Employee training and management
131 3.6 0.966
5 Managing information 131 3.58 0.96
6 Trading partner management 131 3.48 0.807
7 Returns management 131 3.4 0.942
8 Inventory management 131 3.37 0.879
9 Supply chain design 131 3.31 0.895
Range of mean rank : (1-1.8) - Not at all; (1.9 -2.7) - Little;
(2.8-3.5) - Average; (3.6-4.2) – Greatly (4.2-5) - A lot.
It may be seen from Table 4.17 that the first four items have mean
rank between 3.6 to 4.2. Thus, the MSMEs are influenced greatly by
manufacturing operations, customer focus, performance metrics, employee
training and managing information. The remaining five items 5, 6, 7, 8 and 9
have secured the mean rank between 2.8 to 3.5 and hence the influence of
them on the MSMEs is only average. The items include trading partner
management, returns management, inventory management and supply chain
design.
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4.4.6 Association between Benchmarking of SCM Activities and the
Support by Top Management of MSMEs
A stepwise multiple regression analysis approach was adopted, to
establish the relationship between Benchmarking of SCM activities and the
support by top management of MSMEs. Out of 5 top management support
factors, after completing stepwise multiple regressions, it was found that all
the five factors contribute to the SCM bench marking. The dependent
variable and the independent or predictor variables considered, coefficient of
correlation (R), coefficient of determination (R2), F value and P-value are
given below:
Dependent variable : SCM Benchmarking (Y)
Independent variables : i) Commits adequate resources (X1)
ii) Has a very clear customer and shareholders focus (X2)
iii) Ensures a good internal communication and dialogue process (X3)
iv) Supports the acquisition and implementation of appropriate information system (X4)
v) Ensures performance measures are aligned with the SCM Strategy(X5)
R value : 0. 604
R Square value : 0.365
F value : 14.366
P value : <0.001**
The value of -coefficient, t-value, significance value, collinearity
statistics and condition index are detailed in Table 4.18.
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Table 4.18 -coefficient, t value and significance value for the analysis
between the benchmarking of SCM activities and top
management of MSMEs
Model
Variable
Unstandardized
Coefficients
Standardized
Coefficients
t-valu
eSig.
Collinearity Statistics Conditi
on Index
BetaStd. Erro
rBeta Toleran
ce VIF
1 (Constant)
14.986
2.407 - 6.226
<0.001**
- - 1.000
(X1) 0.728 0.754 0.094 0.965
0.337 0.534 1.872
10.648
(X2) 0.460 0.616 0.064 0.746
0.457 0.681 1.469
12.389
(X3) 1.114 0.796 0.137 1.399
0.164 0.527 1.898
14.678
(X4) 2.948 0.619 0.417 4.764
<0.001**
0.662 1.510
16.154
(X5) 0.867 0.627 0.119 1.383
0.169 0.686 1.457
17.922
It may noted that the multiple correlation coefficient is 0.604 and
this coefficient measures the degree of relationship of all the five independent
variables with SCM bench marks. From the model with five independent
variables the value of R square is 0.365 which implies that 36.5% of the
variation in SCM benchmarks due to the five independent variables. The R
square value is significant at 1 % level. Based on the analysis, the multiple
regression equation of model arrived at is,
Y = 14.986+ 0.728 X1 + 0.460 X2 + 1.114 X3 + 2.948 X4 + 0.867 X5
The coefficient of X1 is 0.728 which indicates the partial positive
effect of committing adequate resources (X1) on SCM benchmarking (Y),
holding the other four variables viz., X2, X3, X4 and X5 constant. This
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coefficient value is not significant at 1% level, so its contribution need not be
considered as valid in this model.
For the variable X2 the -coefficient value is 0.460 which gives the
partial positive effect of having a very clear customer and shareholders focus
(X2) on SCM bench marking (Y) holding X1 , X3 , X4 and X5 constant and this
coefficient value is also not significant at 1% level, so its contribution may
not be valid in this model.
The -coefficient of X3 is 1.114 representing the partial positive
effect of ensuring a good internal communication and dialogue process (X3)
on SCM benchmarking (Y) holding X1 , X2 , X4 and X5 constant. This
coefficient value is not significant at 1% level. so its contribution may not be
valid in this model.
The value of -coefficient for X4 is 2.948 indicating the partial
positive effect of supporting the acquisition and implementation of
appropriate information system (X4) on SCM benchmarking (Y) holding X1 ,
X2 , X3, X5 constant and this coefficient value is significant at 1% level..
Likewise, the -coefficient of X5 is 0.864 which represents the
partial positive effect of ensuring performance measures aligned with the
SCM strategy(X5) on SCM bench marking (Y) holding X1, X2, X3 and X4
constant. This coefficient value is also not significant at 1% level, so its
contribution may not be valid in this model.
It may also be noted that from table 4.18 that all the condition index
values are less than 30 and the VIF value also less than 10. This implies that
there is no multicollinearity in the predicator or independent variable.
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Each sub factors of dependent variable was measured with the five
point rating scale viz., 1-Not at all; 2 - Little; 3 - Average; 4 - Greatly; 5 - A
lot. Totally nine sub factors used for the independent variable. While
constructing the multiple regressions model the rating values of all the sub
factors are summed up. Hence for assigning value to the dependent variable
in this model, the range of rating scale values adopted are; 1-9 not at all;
10-18 little; 19-27 average; 28-36 greatly and 37-45 a lot.