CHAPTER 4. STATUS OF E-BUSINESS APPLICATION SYSTEM AND ENABLERS IN SCM OF MSMEs

Transcription

1 70 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.

2 71 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 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 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 Percentage of firms as per survey for research Micro Small Medium 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

3 72 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 years years more than 15 years Total From Table 4.2 it may be seen that 38.2% firms have experience between 5-10 years followed by 19.1% respondents between 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.

4 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 Somewhat successful Successful Very successful Total 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 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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6 75 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 custommade 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 custommade 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.

7 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, H 0 : 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.

8 77 Table 4.5 The Pearson Chi-square values for association between the e-business systems currently available in MSMEs and category of MSMEs Sl.No Between MSME category and e- business system Pearson Chi- Square Asymp. Sig. (2-sided) 1 Material Requirement Planning (MRP) Manufacturing Resources Planning(MRPII Enterprise Resources Planning (ERP) * 4 Warehouse Management System (WMS) ** 5 Supply Chain Management module (SCM) 6 Customer Relationship Management (CRM) 7 Supplier Relationship Management (SRM) Advanced Planning System (APS) Just in time (JIT) E-business Decision support / expert System Radio frequency Identification(RFID) Electronic data interchange (EDI) Bar coding ** denotes significance at 1% level * denotes significance at 5% level

9 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, H 0 : 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.No Success in managing SCM vs e-business system Pearson Chi- Square Asymp. Sig. (2-sided) 1 Material Requirement Planning (MRP) * 2 Manufacturing Resources Planning (MRPII) 3 Enterprise Resources Planning (ERP) Successful in Managing (SCM) * ** 4 Warehouse Management System (WMS) * 5 Customer Relationship Management (CRM) 6 Supplier Relationship Management (SRM) * Advanced Planning System (APS) Just in time (JIT) ** 9 Decision support (DSS) Radio frequency Identification (RFID) * 11 Electronic data interchange (EDI) ** 12 Bar coding (BC) * ** denotes significance at 1% level * denotes significance at 5% level

10 79 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 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.

11 80 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 % E-auctions for Count procurement % Retail Count e-payments % Retail transfer Count e-payments % Certifications Count for security of payments % Wholesalers Count e-payments % Electronic Count signature % Electronic ID Count % Electronic document management 10 Collaborative tools for e-business Count % Count % Internet Count % Order Count processing % Follow up Count % Online marketing Count % Total Count %

12 81 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.

13 Status of SC Enablers in MSMEs The Friedman test is normally applied to data with repeatedmeasures 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 Sl.No enablers SC enabler Mean Rank Std. Deviation 1 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 E-procurement Vertical Integration Supply chain benchmarking Use of external consultants Electronic data interchange (EDI) Out sourcing Range of mean rank : (1-1.75) Not appropriate ; ( ) -Start implementing; (3.26 4) Satisfied already ( ) Improve;

14 83 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 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 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

15 84 use of external consultants electronic data interchange (EDI) out sourcing 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. Mean Std. SC enablers Number No Rank Deviation 1 Increased coordination with customers Increased coordination with suppliers Increased sales Better quantity of information Flexibility in operation Reduced lead-time in manufacturing Better operational efficiency Better quality of information Cost saving in manufacturing Increased coordination between departments Improved Forecasting More accurate costing Improved Resource planning Reduced inventory Level Range of mean rank : (1-1.8) - Not at all; ( ) - Little; ( ) - Average; ( ) Greatly (4.2-5) - A lot

16 85 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 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, H 0 : 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 Pearson Chi-Square Asymp. Sig. (2-sided) 1 Better quality of information ** 2 Better quantity of information * 3 Flexibility in operation * 4 Reduced lead-time in manufacturing Cost saving in manufacturing * 6 Improved Forecasting Improved Resource planning *

17 86 Table 4.10 (Continued) Sl:No Between MSME category and SC enabler Pearson Chi-Square Asymp. Sig. (2-sided) 8 Better operational efficiency Reduced inventory Level * 10 More accurate costing Increased coordination between departments 12 Increased coordination with suppliers 13 Increased coordination with customers * * 14 Increased sales 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.

18 87 The remaining six SC enablers out of the 14 considered do not have any significant association with the category of MSMEs 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 retested 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,

19 88 Dependent variable : Benefits of using SC enablers (Y) Independent variables : i)strategic planning in procurement and distribution (X 1 ) : ii) Close Partnership with customers (X 2 ) Table 4.11 gives the value of coefficients, correlation coefficient (R), R 2 and adjusted R 2 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 Table 4.11 Model fit coefficient value for the analysis between benefits and SC enablers in MSMEs Model R R Square Adjusted R Square F value P value <0.001** <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 Std. Beta Error Standard. Coeff. t - value Sig. Collinearity Statistics Beta Tol. VIF Condition Index 1 (Constant) <0.001** (X 1 ) <0.001** (Constant) <0.001** (X 1 ) <0.001** (X 2 ) <0.001** ** denotes significance at 1% level

20 89 From Table 4.11, it is evident that for model-1, the multiple correlation coefficient (R value) is 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 (X 2 ) was added. It may noted that the multiple correlation coefficient now increased to 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 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 = X X 2 The -coefficient of X 1 is 3.207, which represents the partial positive effect of strategic planning in procurement and distribution (X 1 ) on benefits of using e-scm components(y), holding the variable close partnership with customers(x 2 ) constant and this coefficient value is significant at 1% level. Similarly, the -coefficient of X 2 is and represents the partial positive effect of close partnership with customers(x 2 ) on benefits of using e-scm components (Y), holding the variable strategic planning in procurement and distribution (X 1 ) constant and this coefficient is significant

21 90 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; little; average; greatly and a lot. 4.4 SUPPORTS FOR SUPPLY CHAIN MANAGEMENT OF MSMEs 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.

22 91 Table 4.13 The mean rank and standard deviation towards emphasis of company strategy in SCM Sl. No Emphasis of company strategy in SCM 1 On offering products with the best quality and yet with a minimum price 2 On reducing the lead time in the supply chain 3 On producing innovative and technologically superior products 4 On ensuring the product are readily available on the shelf in the market 5 On offering returns management solutions Number Mean Rank Std. Deviation Range of mean rank: (1-1.8) - Not at all; ( ) - Little; ( ) -Average; ( ) - 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.

23 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 1 Has a very clear customer and shareholders focus 2 Ensures a good internal communication and dialogue process 3 Supports the acquisition and implementation of appropriate information system across departments and across the supply chain 4 Commits adequate resources for effective SCM 5 Ensures performance measures are aligned with the SCM Strategy Number Mean Rank Std. Deviation Range of mean rank : (1-1.8) - Not at all; ( ) - Little; ( ) Average ( ) - Greatly; (4.2-5) - A lot.

24 93 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 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 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.

25 94 Table Mean rank and standard deviation towards importance of Sl. No SCM attributes SCM attributes N Mean Rank Std. Deviation 1 Team Work Reduced inventory level Response time Strategic sourcing Use of SCM applications software Vendor managed inventory Information sharing with the supplier JIT Supply Electronic Data Interchange (EDI) Supply Chain Benchmarking Third Party Logistics E-procurement Subcontracting Range of mean rank : (1-1.8) - Not at all; ( ) - Little; ( ) - Average; ( ) - 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

26 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 Table 4.16 Mean rank and standard deviation towards emphasis of e- business infrastructure requirement Sl. No e-business infrastructures Number Mean Rank Std. Deviation 1 Various departments, offices and branches are electronically linked for better coordination 2 Trading partners have access to the organization s real Time dynamic information through secure extranet sites 3 The information system is periodically reviewed and technologically updated to respond to ever increasing requirements 4 Information systems are regularly updated with accurate and timely information Range of mean rank : (1-1.8) - Not at all; ( ) - Little; ( ) - Average; ( ) - Greatly; (4.2-5) - A lot.

27 96 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 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.

28 97 Table 4.17 Mean rank and standard deviation towards bench marking of SCM activities Sl.No Bench marks of SCM Number Mean Rank Std. Deviation 1 Manufacturing Customer focus Performance metrics Employee training and management Managing information Trading partner management Returns management Inventory management Supply chain design Range of mean rank : (1-1.8) - Not at all; ( ) - Little; ( ) - Average; ( ) 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.

29 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 (R 2 ), F value and P-value are given below: Dependent variable : SCM Benchmarking (Y) Independent variables : i) Commits adequate resources (X 1 ) ii) iii) iv) Has a very clear customer and shareholders focus (X 2 ) Ensures a good internal communication and dialogue process (X 3 ) Supports the acquisition and implementation of appropriate information system (X 4 ) v) Ensures performance measures are aligned with the SCM Strategy(X 5 ) R value : R Square value : F value : P value : <0.001** The value of -coefficient, t-value, significance value, collinearity statistics and condition index are detailed in Table 4.18.

30 99 Table coefficient, t value and significance value for the analysis between the benchmarking of SCM activities and top management of MSMEs Mod el Variabl e 1 (Consta nt) Unstandardi zed Coefficients Beta Std. Erro r Standardi zed Coefficien ts Beta t- valu e (X 1 ) (X 2 ) (X 3 ) (X 4 ) (X 5 ) Sig. <0.001 ** Collinearity Statistics Toleran ce VIF Conditi on Index <0.001 ** It may noted that the multiple correlation coefficient is 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 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 = X X X X X 5 The coefficient of X 1 is which indicates the partial positive effect of committing adequate resources (X 1 ) on SCM benchmarking (Y), holding the other four variables viz., X 2, X 3, X 4 and X 5 constant. This

31 100 coefficient value is not significant at 1% level, so its contribution need not be considered as valid in this model. For the variable X 2 the -coefficient value is which gives the partial positive effect of having a very clear customer and shareholders focus (X 2 ) on SCM bench marking (Y) holding X 1, X 3, X 4 and X 5 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 X 3 is representing the partial positive effect of ensuring a good internal communication and dialogue process (X 3 ) on SCM benchmarking (Y) holding X 1, X 2, X 4 and X 5 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 X 4 is indicating the partial positive effect of supporting the acquisition and implementation of appropriate information system (X 4 ) on SCM benchmarking (Y) holding X 1, X 2, X 3, X 5 constant and this coefficient value is significant at 1% level.. Likewise, the -coefficient of X 5 is which represents the partial positive effect of ensuring performance measures aligned with the SCM strategy(x 5 ) on SCM bench marking (Y) holding X 1, X 2, X 3 and X 4 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.

32 101 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; little; average; greatly and a lot.