Relationship between Critical Success Factors and Performance Indicator Factors in Automobile Indust

IJIRST –International Journal for Innovative Research in Science & Technology| Volume 4 | Issue 2 | July 2017 ISSN (online): 2349-6010

Relationship between Critical Success Factors and Performance Indicator Factors in Automobile Industry of Gujarat Nareshkumar D. Chauhan Research Scholar Department of Mechanical Engineering C. U. Shah University, Surendranagar - Ahmedabad Highway, Nr. Kothariya Village, Wadhwan City - 363030.

Gujarat. India

Dr. Pranav H. Darji Professor & Head Department of Mechanical Engineering C. U. Shah University, Surendranagar - Ahmedabad Highway, Nr. Kothariya Village, Wadhwan City - 363030.

Gujarat. India

Dr. M. N. Qureshi Associate Professor Department of Industrial Engineering College of Engineering, King Khalid University, Guraiger, Abha - 62529, Saudi Arabia

Abstract The main purpose of this study is to identify the adoption of the lean manufacturing along with comparative study of critical success factor with performance indicator. This study helps to find the relation of both parameters in Lean Manufacturing practices. A survey is conducted within the automobile parts manufacturing organization of the Gujarat. The present study illustrates the normality test of the data obtained from the survey and multiple regression analysis of the available data in terms of the critical success factors and performance indicator within organization. The relationship between critical success factor and performance indicator has been examined through various segments of automotive industries. This study also emphasize on variation in awareness of Lean Manufacturing with number of employees, age of organization, tier of organization, product mix and operation type.

Keywords: Lean Manufacturing, Critical Success Factors, Performance Indicator, Normality Test, Multiple Regression Analysis, Krushkal Wallis Test _______________________________________________________________________________________________________ I.

INTRODUCTION

Lean manufacturing is a systematic method for the elimination of waste ("Muda") within a manufacturing system. Lean manufacturing makes obvious what adds value, by reducing everything else which not adding value. This management philosophy is derived mostly from the Toyota Production System (TPS). Lean Manufacturing is a philosophy and art of manufacturing; it is not a quantified tool. It is not providing any scale to depict the percentage of implementation; hence there are certain challenges which act a bottleneck while implementing the lean manufacturing.

This study consists of critical success factor and performance indicator amongst the rest of parameters such as barriers, environmental factors, executional factors, organizational factors etc. Critical success factor are the phase which favors the implementation of the lean manufacturing. It is a kind of practice which is mandatory in lean manufacturing implementation, while performance indicator is a measure of success factors. It can be scrutinize in various criteria such as financial, customer, process, people, future etc. Table 1.1 shows the methodology carried out for this research work.

Sr. No. 1 2 3 4 5

Table - 1.1 Methodology Steps Problem identification Data collection Normality test Krushkal Wallis test Multiple Regression Analysis Chi-square test

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II. PROBLEM DESCRIPTION Based on the existing gap in literature review, the co-relation have been found between Performance Indicator and Barriers in implementation of Lean manufacturing in Automotive industries with in Gujarat, It was decided to conduct a Quantitative study.

Individual concept has been analyzed and quantitative scales already exist in specialized literature.

Questionnaire Preparation: A data collection instruments was based on the structured questionnaire for collected data about the concepts those were previously reviewed. The questionnaire contains information on the characterization of respondent companies and four blocks of assertions: one for the “Structural Details”, second for “Lean manufacturing techniques”, third for “Barriers for implementation Lean manufacturing”, and last fourth for “Performance Indicator”. Altogether, the questionnaire presents six questions about structure of industries, four questions for Lean manufacturing implementation and remaining assertion for Barriers and performance indicator. A 5-point Likert scale was adopted, where 1 represents “totally disagree” and 5 represents “totally agree” or vice versa.

Data Collection: The first phase of data collection comprises of the collection of the various automotive industries details in terms of company address, Authority person’s E-mail id & contact collected with in Gujarat State. Personal meet as well as the mail has been sent to the companies through a person occupying with highest position in specially production areas in Gujarat automotive industries.

The reason behind the selection of the production areas with in industries is the main indicators in implementation of the lean manufacturing is experience and well tested by the operational and production areas. Total 730 companies were selected for the survey. Out of all companies 134 valid surveys have been received which in turn shows the response rate of 18.35%, this number is quite adequate suggested by synodinos. Murillo-Luna et al. state that exploration of the results can be attempts only when response rates greater than 6% can be considered in studies that structural equation modeling.

Industries Studied: The main target of study is for the finding in Gujarat State Industries, specifically the automobile manufacturing sector. Gujarat’s automotive manufacturing sector initiates in the 1980s and has evolved into about 134 factories supplied by more than 3000 auto part companies. As per our scientific survey results, 2.2 percent of the organizations have reported them as OEM Manufacturer, 41.8 percent of the respondents have reported as TIER 1 Manufacturer and 56 percent of the respondents have reported as TIER 2 or Higher Manufacturers. 6.7 percent of the organizations are 0-5-year-old, 13.4 percent of the organizations are 6-10-year-old, 19.4 percent of the organizations are 11-15-year-old and 60.4 percent of the organizations are more than 15-year-old. 44.8 percent of the organizations are having 0-100 employees, 37.3 percent of the organizations are having 101-200 employees, 9.7 percent of the organizations are having 201-300 employees, 6 percent of the organizations are having 301-400 employees and 2.2 percent of the organizations are having more than 500 employees.

III. RELATIONSHIP ANALYSIS FOR CRITICAL SUCCESS FACTORS AND PERFORMANCE INDICATORS Test of Normality: The following variables were tested for Normality. The Result is shown in Table 3.1 Hypothesis taken as, H0: The Distribution is normally distributed.

H1: The Distribution is not normally distributed.

All tested variables are not found normally distributed at 5 per cent level of significance. Hence it would be appropriate to perform non-parametric test on these variables.

Table - 3.1 Tests of Normality

Kolmogorov-Smirnova Statistic df Sig. Executional .360 134 .000 Organizational .356 134 .000 Future .350 134 .000 People .354 134 .000 Process .349 134 .000 customer .352 134 .000 Finance .340 134 .000 a. Lilliefors Significance Correction

Shapiro-Wilk Statistic df Sig. .692 134 .000 .682 134 .000 .675 134 .000 .678 134 .000 .671 134 .000 .681 134 .000 .645 134 .000

Krushkal Wallis Test: The Non-parametric test- Krushkal Wallis test has been performed and the result is shown below

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Significant difference on basis of number of employees Ho: There is no significance difference in Critical Success Factors and Performance Indicator Factors between the different categories of number of employees.

Ha: There is a significance difference in Critical Success Factors and Performance Indicator Factors between the different categories of number of employees.

Table - 3.2 Test Statisticsa,b Organizational Future People 12.091 14.930 15.000 4 4 4 .017 .005 .005

Executional Chi-Square 13.822 df 4 Asymp. Sig. .008 a. Kruskal Wallis Test b. Grouping Variable: No_Employees

Process 14.628 4 .006

Customer 15.249 4 .004

Finance 14.741 4 .005

The Table 3.2 shows the mean rank of different categories with respect to Critical Success Factors and Performance Indicator Factors. In above table, the calculated Chi-Square, degree of freedom, and significant value is given. All seven factors are found significant at 5 % level of significance hence it can be concluded that there is a significance difference in Critical Success Factors and Performance Indicator Factors between the different categories of number of employees.

Significant difference on basis of age of organization Ho: There is no significance difference in Critical Success Factors and Performance Indicator Factors between the different categories of Age of the organization.

Ha: There is a significance difference in Critical Success Factors and Performance Indicator Factors between the different categories of Age of the organization.

Table - 3.3 Test Statisticsa,b Organizational Future People 5.240 8.044 8.309 3 3 3 .155 .045 .040

Executional Chi-Square 6.530 df 3 Asymp. Sig. .088 a. Kruskal Wallis Test b. Grouping Variable: Old Organization

Process 7.634 3 .044

Customer 7.875 3 .049

Finance 8.240 3 .041

The Table 3.3 shows the mean rank of different categories with respect to Critical Success Factors and Performance Indicator Factors. In above table, the calculated Chi-Square, degree of freedom, and significant value is given. All Performance Indicator factors are found significant at 5 % level of significance hence it can be concluded that there is a significance difference in Performance Indicator Factors between the different categories of age of the organization. Both Critical Success Factors are not found significant at 5 % level of significance hence for success factors it can be concluded that there is no significance difference in Performance Indicator Factors between the different categories of age of the organization.

Significant difference on basis of tier of organization Ho: There is no significance difference in Critical Success Factors and Performance Indicator Factors between the different categories of Tier of Organization.

Ha: There is a significance difference in Critical Success Factors and Performance Indicator Factors between the different categories of Tier of Organization.

Table - 3.4 Test Statisticsa,b Organizational Future People 4.733 4.698 4.147 2 2 2 .094 .095 .126

Executional Chi-Square 4.512 df 2 Asymp. Sig. .105 a. Kruskal Wallis Test b. Grouping Variable: Tier_Organization

Process 4.921 2 .085

Customer 4.204 2 .122

Finance 4.703 2 .095

The Table 3.4 shows the mean rank of different categories with respect to Critical Success Factors and Performance Indicator Factors. In above table, the calculated Chi-Square, degree of freedom, and significant value is given. All seven factors are not found significant at 5 % level of significance hence it can be concluded that there is no significance difference in Critical Success Factors and Performance Indicator Factors between the different categories of tier of the organization.

Significant difference on basis of product mix Ho: There is no significance difference in Critical Success Factors and Performance Indicator Factors between the different categories of Product Mix. Ha: There is a significance difference in Critical Success Factors and Performance Indicator Factors between the different categories of Product Mix.

Chi-Square

Executional 9.470

Table - 3.5 Test Statisticsa,b Organizational Future People 6.768 10.636 10.789

Process 9.702

customer 10.566

Finance 10.564

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df 3 3 Asymp. Sig. .024 .040 a. Kruskal Wallis Test b. Grouping Variable: Product Mix

3 .014

3 .013

3 .021

3 .014

3 .014

The Table 3.5 shows the mean rank of different categories with respect to Critical Success Factors and Performance Indicator Factors. In above table, the calculated Chi-Square, degree of freedom, and significant value is given. All seven factors are found significant at 5 % level of significance hence it can be concluded that there is a significance difference in Critical Success Factors and Performance Indicator Factors between the different categories of Product Mix. Significant difference on basis of operation type Ho: There is no significance difference in Critical Success Factors and Performance Indicator Factors between the different categories of Operation Type.

Ha: There is a significance difference in Critical Success Factors and Performance Indicator Factors between the different categories of Operation Type.

Table - 3.6 Test Statisticsa,b Organizational Future People 1.726 .952 .762 2 2 2 .422 .621 .683

Executional Chi-Square 1.685 df 2 Asymp. Sig. .431 a. Kruskal Wallis Test b. Grouping Variable: Operation Type

Process 1.362 2 .506

customer .934 2 .627

Finance 1.132 2 .568

The Table 3.6 shows the mean rank of different categories with respect to Critical Success Factors and Performance Indicator Factors. In above table, the calculated Chi-Square, degree of freedom, and significant value is given. All seven factors are not found significant at 5 % level of significance hence it can be concluded that there is no significance difference in Critical Success Factors and Performance Indicator Factors between the different categories of Product Mix. Multiple Regression Analysis: This technique was used to predict the dependent variables (Performance Indicator Factors) from independent variables (Critical Success Factors). The factor scores were used to run the analysis. Each model predicts the performance factors from the factor scores of success factors.

1) Model: 1 Critical Success factors and Financial (PI: Performance Indicator) Dependent Variable: Y = Financial (PI) Independent variables: X1 = Organizational Success Factor X2 = Executional Success Factor The Table 3.7 shows the Model summary, ANOVA table and Values of coefficients and its associated significant values.

The value of R square shows that 78.1 percent of the variance in dependent variable can be explained by jointly all independent variables.

Table - 3.7 Model Summaryb Model R R Square Adjusted R Square Std. Error of the Estimate 1 .884a .781 .778 .30316 a. Predictors: (Constant), Organizational, Executional b. Dependent Variable: Finance

The Table 3.8 shows the ANOVA table. The two tailed significant value for the F statistics is 0.000 hence null hypothesis can be rejected at 5 % level of significance. Hence it can be concluded that jointly all independent variables can have influence on dependent variables.

Table - 3.8 ANOVAb Model Sum of Squares df Mean Square Regression 43.018 2 21.509 1 Residual 12.039 131 .092 Total 55.058 133 a. Predictors: (Constant), Organizational, Executional b. Dependent Variable: Finance

F 234.040

Sig. .000a

The Table 3.9 shows the coefficient values, t statistics and its associated two tailed p value. It can be seen from the table that all variables are found significant at 5 percent level of significance.

Model 1

(Constant) Executional

Table - 3.9 Coefficientsa Unstandardized Coefficients Standardized Coefficients B Std. Error Beta .849 .209 .302 .141 .268

t

Sig.

4.064 2.142

.000 .034

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Organizational 1.329 a. Dependent Variable: Finance

.147

1.133

9.053

.000

The regression equation can be obtained as Financial (PI) = 0.849 + 0.302 (Executional Success Factor) + 1.32 (Organizational Success Factor) 2) Model: 2 Critical Success factors and Customer (PI) Dependent Variable: Y = Customer Performance Independent variables: X1 = Organizational Success Factor X2 = Executional Success Factor The Table 3.10 shows the Model summary, ANOVA table and Values of coefficients and its associated significant values.

The value of R square shows that 80.9 percent of the variance in dependent variable can be explained by jointly all independent variables.

Table - 3.10 Model Summaryb Model R R Square Adjusted R Square Std. Error of the Estimate 1 .899a .809 .806 .21863 a. Predictors: (Constant), Organizational, Executional b. Dependent Variable: customer

The Table 3.11shows the ANOVA table. The two tailed significant value for the F statistics is 0.000 hence null hypothesis can be rejected at 5 % level of significance. Hence it can be concluded that jointly all independent variables can have influence on dependent variables.

Table - 3.11 ANOVAb Model Sum of Squares df Mean Square Regression 26.447 2 13.224 1 Residual 6.262 131 .048 Total 32.709 133 a. Predictors: (Constant), Organizational, Executional b. Dependent Variable: customer

F 276.645

Sig. .000a

The Table 3.12 shows the coefficient values, t statistics and its associated two tailed p value. It can be seen from the table that organizational success factor is found significant at 5 percent level of significance but Executional factor is not found significant at 5 % level of significance.

Table - 3.12 Coefficientsa Unstandardized Coefficients Standardized Coefficients Model B Std. Error Beta (Constant) .683 .151 1 Executional .007 .102 .008 Organizational .820 .106 .907 a. Dependent Variable: customer

t

Sig.

4.537 .068 7.744

.000 .946 .000

Customer (PI) = 0.683 + 0.007 (Executional Success Factor) + 0.82 (Organizational Success Factor) 3) Model: 3 Critical Success factors and Process (PI) Dependent Variable: Y = Process (PI) Independent variables: X1 = Organizational Success Factor X2 = Executional Success Factor The Table 3.13 shows the Model summary, ANOVA table and Values of coefficients and its associated significant values.

The value of R square shows that 82.8 percent of the variance in dependent variable can be explained by jointly all independent variables.

Table - 3.13 Model Summaryb Model R R Square Adjusted R Square Std. Error of the Estimate 1 .910a .828 .826 .24033 a. Predictors: (Constant), Organizational, Executional b. Dependent Variable: Process

The Table 3.14 shows the ANOVA table. The two tailed significant value for the F statistics is 0.000 hence null hypothesis can be rejected at 5 % level of significance. Hence it can be concluded that jointly all independent variables can have influence on dependent variables.

1

Model Regression Residual Total

Table - 3.14 ANOVAb Sum of Squares df Mean Square 36.476 2 18.238 7.566 131 .058 44.042 133

F 315.761

Sig. .000a

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a. Predictors: (Constant), Organizational, Executional b. Dependent Variable: Process

The Table 3.15 shows the coefficient values, t statistics and its associated two tailed p value. It can be seen from the table that all variables are found significant at 5 percent level of significance.

Table - 3.15 Coefficientsa Unstandardized Coefficients Standardized Coefficients Model B Std. Error Beta (Constant) .751 .166 1 Executional .248 .112 .246 Organizational 1.195 .116 1.139 a. Dependent Variable: Process

t

Sig.

4.535 2.220 10.271

.000 .028 .000

Process (PI) = 0.751 + 0.248 (Executional Success Factor) + 1.19 (Organizational Success Factor) 4) Model: 4 Critical Success factors and People (PI) Dependent Variable: Y = People (PI) Independent variables: X1 = Organizational Success Factor X2 = Executional Success Factor The Table 3.16 shows the Model summary, ANOVA table and Values of coefficients and its associated significant values.

The value of R square shows that 78.9 percent of the variance in dependent variable can be explained by jointly all independent variables.

Table - 3.16 Model Summaryb Model R R Square Adjusted R Square Std. Error of the Estimate 1 .888a .789 .786 .25836 a. Predictors: (Constant), Organizational, Executional b. Dependent Variable: People

The Table 3.17 shows the ANOVA table. The two tailed significant value for the F statistics is 0.000 hence null hypothesis can be rejected at 5 % level of significance. Hence it can be concluded that jointly all independent variables can have influence on dependent variables.

Table - 3.17 ANOVAb Model Sum of Squares df Mean Square Regression 32.758 2 16.379 1 Residual 8.744 131 .067 Total 41.502 133 a. Predictors: (Constant), Organizational, Executional b. Dependent Variable: People

F 245.373

Sig. .000a

The Table 3.18 shows the coefficient values, t statistics and its associated two tailed p value. It can be seen from the table that organizational success factor is found significant at 5 percent level of significance but executional factor is not found significant at 5 % level of significance.

Table - 3.18 Coefficientsa Unstandardized Coefficients Standardized Coefficients Model B Std. Error Beta (Constant) .778 .178 1 Executional .115 .120 .117 Organizational .791 .125 .777 a. Dependent Variable: People

t

Sig.

4.374 .956 6.322

.000 .341 .000

People (PI) = 0.778 + 0.118 (Executional Success Factor) + 0.79 (Organizational Success Factor) 5) Model: 5 Critical Success factors and Future (PI) Dependent Variable: Y = Future (PI) Independent variables: X1 = Organizational Success Factor X2 = Executional Success Factor The Table 3.19 shows the Model summary, ANOVA table and Values of coefficients and its associated significant values.

The value of R square shows that 82.9 percent of the variance in dependent variable can be explained by jointly all independent variables.

Table - 3.19 Model Summaryb Model R R Square Adjusted R Square Std. Error of the Estimate 1 .910a .829 .826 .25611 a. Predictors: (Constant), Organizational, Executional b. Dependent Variable: Future

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The Table 3.20 shows the ANOVA table. The two tailed significant value for the F statistics is 0.000 hence null hypothesis can be rejected at 5 % level of significance. Hence it can be concluded that jointly all independent variables can have influence on dependent variables.

Table - 3.20 ANOVAb Model Sum of Squares df Mean Square Regression 41.580 2 20.790 1 Residual 8.593 131 .066 Total 50.172 133 a. Predictors: (Constant), Organizational, Executional b. Dependent Variable: Future

F 316.952

Sig. .000a

The Table 3.21 shows the coefficient values, t statistics and its associated two tailed p value. It can be seen from the table that organizational success factor is found significant at 5 percent level of significance but Executional factor is not found significant at 5 % level of significance.

Table - 3.21 Coefficientsa Unstandardized Coefficients Standardized Coefficients Model B Std. Error Beta (Constant) .823 .176 1 Executional .151 .119 .140 Organizational 1.166 .124 1.042 a. Dependent Variable: Future

t

Sig.

4.667 1.265 9.405

.000 .208 .000

Future (PI) = 0.778 + 0.118 (Executional Success Factor) + 0.79 (Organizational Success Factor) Chi-Square Test: Ho: There is no association between No of Employees and Awareness about the Lean Manufacturing Practices.

Ha: There is an association between No of Employees and Awareness about the Lean Manufacturing Practices.

Table - 3.22 No_Employees * awareness Crosstabulation awareness Yes No Count 23 37 0-100 Expected Count 39.4 20.6 Count 41 9 101-200 Expected Count 32.8 17.2 Count 13 0 No_Employees 201-300 Expected Count 8.5 4.5 Count 8 0 301-400 Expected Count 5.3 2.7 Count 3 0 More than 500 Expected Count 2.0 1.0 Count 88 46 Total Expected Count 88.0 46.0

Total 60 60.0 50 50.0 13 13.0 8 8.0 3 3.0 134 134.0

The Table 3.23 shows the value of Chi-square test. The calculated value of chi-square test at 4 degree of freedom is 38.350 and its two sided P value is 0.000. It can be concluded that at 5 % level of significance, the Hypothesis can be rejected. So it can be concluded that there is association between Number of employees in the Organization and Awareness about the Lean Manufacturing Practices.

Table - 3.23 Chi-Square Tests Value df Asymp. Sig. (2-sided) Pearson Chi-Square 38.350a 4 .000

The Table 3.24 shows the strength of association between these two variables. The value of Cramer’s V is 0.260 which shows the weak association between Number of employees and Awareness about the Lean Manufacturing Practices.

Table - 3.24 Symmetric Measures Value Phi .260 Nominal by Nominal Cramer's V .260 N of Valid Cases 134

Approx. Sig. .000 .000

Ho: There is no association between Age of the Organization and Awareness about the Lean Manufacturing Practices.

Ha: There is an association between Age of the Organization and Awareness about the Lean Manufacturing Practices.

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Table - 3.25 Old_Organization * awareness Crosstabulation awareness Yes No Count 6 3 0-5 years Expected Count 5.9 3.1 Count 10 8 6-10 years Expected Count 11.8 6.2 Old_Organization Count 19 7 11-15 years Expected Count 17.1 8.9 Count 53 28 More than 15 years Expected Count 53.2 27.8 Count 88 46 Total Expected Count 88.0 46.0

Total 9 9.0 18 18.0 26 26.0 81 81.0 134 134.0

The Table 3.26 shows the value of Chi-square test. The calculated value of chi-square test at 3 degree of freedom is 1.456 and its two sided P value is 0.693. It can be concluded that at 5 % level of significance, the Hypothesis can not be rejected. So it can be concluded that there is no association between Age of the Organization and Awareness about the Lean Manufacturing Practices.

Table - 3.26 Chi-Square Tests Value df Asymp. Sig. (2-sided) Pearson Chi-Square 1.456a 3 .693

Ho: There is no association between Tier of the Organization and Awareness about the Lean Manufacturing Practices.

Ha: There is an association between Tier of the Organization and Awareness about the Lean Manufacturing Practices.

Table - 3.27 Tier_Organization * awareness Crosstabulation awareness Yes No Count 2 1 OEM Expected Count 2.0 1.0 Count 46 10 Tier_Organization TIER 1 Expected Count 36.8 19.2 Count 40 35 TIER 2 or Higher Expected Count 49.3 25.7 Count 88 46 Total Expected Count 88.0 46.0

Total 3 3.0 56 56.0 75 75.0 134 134.0

The Table 3.28 shows the value of Chi-square test. The calculated value of Chi-square test at 2 degree of freedom is 11.805 and its two sided P value is 0.003. It can be concluded that at 5 % level of significance, the Hypothesis can be rejected. So it can be concluded that there is an association between Tier of the Organization and Awareness about the Lean Manufacturing Practices.

Table - 3.28 Chi-Square Tests Value df Asymp. Sig. (2-sided) Pearson Chi-Square 11.805a 2 .003

The Table 3.29 shows the strength of association between these two variables. The value of Cramer’s V is 0.297 which shows the weak association between Tier of the organization and Awareness about the Lean Manufacturing Practices.

Table - 3.29 Symmetric Measures Value Phi .297 Nominal by Nominal Cramer's V .297 N of Valid Cases 134

Approx. Sig. .003 .003

Ho: There is no association between Product Mix of the Organization and Awareness about the Lean Manufacturing Practices.

Ha: There is an association between Product Mix of the Organization and Awareness about the Lean Manufacturing Practices.

Table - 3.30 Product_Mix * awareness Crosstabulation

High Volume-More Variety Product_Mix

High Volume-less variety Low Volume-High variety

Count Expected Count Count Expected Count Count Expected Count

awareness Yes No 33 14 30.9 16.1 43 26 45.3 23.7 8 3 7.2 3.8

Total 47 47.0 69 69.0 11 11.0

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Low volume-Low Variety Total

Count Expected Count Count Expected Count

4 4.6 88 88.0

3 2.4 46 46.0

7 7.0 134 134.0

The Table 3.31 shows the value of Chi-square test. The calculated value of chi-square test at 3 degree of freedom is 1.24 and its two sided P value is 0.749. It can be concluded that at 5 % level of significance, the Hypothesis can not be rejected. So it can be concluded that there is no association between Product mix of the Organization and Awareness about the Lean Manufacturing Practices.

Table - 3.31 Chi-Square Tests Value df Asymp. Sig. (2-sided) Pearson Chi-Square 1.243a 3 .743

Ho: There is no association between Type of Operation of the Organization and Awareness about the Lean Manufacturing Practices.

Ha: There is an association between Type of Operation of the Organization and Awareness about the Lean Manufacturing Practices.

Table - 3.32 Operation_Type * awareness Crosstabulation awareness Yes No Count 62 32 make to order Expected Count 61.7 32.3 Count 14 9 Operation_Type Assemble to Order Expected Count 15.1 7.9 Count 12 5 Make to Stock Expected Count 11.2 5.8 Count 88 46 Total Expected Count 88.0 46.0

Total 94 94.0 23 23.0 17 17.0 134 134.0

The Table below 3.33 the value of Chi-square test. The calculated value of chi-square test at 2 degree of freedom is 0.421 and its two sided P value is 0.810. It can be concluded that at 5 % level of significance, the Hypothesis can not be rejected. So it can be concluded that there is no association between Operation Type of the Organization and Awareness about the Lean Manufacturing Practices.

Table - 3.33 Chi-Square Tests Value df Asymp. Sig. (2-sided) Pearson Chi-Square .421a 2 .810

IV. RESULT AND DISCUSSION Based on survey of 134 automotive parts manufacturing industries the data has been collected and are subjected to the normality test. Which infers that the collected data is not normal hence Krushkal’s Wallis test has been implemented to find the significant difference between success factor and performance indicator. Krushkal’s Wallis test has been used to find out the difference between both the parameter in below Table 3.34 tabulated criteria; Sr. No. 1 2 3 4 5

Table - 3.34 Summary of Krushkal’s Wallis test Criteria Observation Number of Employee Significant difference between CSF and PI Age of Organization Significant difference between CSF and PI Tier of Organization No significant difference between CSF and PI Product mix Significant difference between CSF and PI Operation type No significant difference between CSF and PI

Multiple regression analysis has been used to find out the relationship between success factor and performance indicator which is tabulated as in below Table 3.35; Sr. No. 1 2 3 4 5

Table - 3.35 Summary of Multiple regression analysis Factors Factors Regression % Finance 78.1 Customer 80.9 Critical Success factors Process 82.8 People 78.9 Future 82.9

Chi-square has been used to find out the association between awareness of lean manufacturing and selected criteria which is tabulated as in below Table 3.36;

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Sr. No. 1 2 3 4 5

Criteria Number of Employee Age of Organization Tier of Organization Product mix Operation type

Table - 3.36 Summary of Chi-square test Observation Weak association with awareness as Cramer number is 0.260 No association with awareness as p value is greater than 0.05 Weak association with awareness as Cramer number is 0.297 No association with awareness as p value is greater than 0.05 No association with awareness as p value is greater than 0.05

V. CONCLUSION Based on the above analysis following results have been found;  The process performance indicator is significant criteria of performance indicator which is influenced by critical success factor, while finance performance indicator is having least influence of critical success factor.  There is a weak association of awareness of lean manufacturing with number of employees and tier of organization, while rest of the criteria does not have any association with awareness of lean manufacturing.

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