CHAPTER 4 DATA ANALYSIS AND FINDINGS

Transcription

1 CHAPTER 4 DATA ANALYSIS AND FINDINGS In this chapter we will discuss the data collection and the outcome of data analysis. For this data collected through questionnaire in survey is tabulated. Both descriptive and inferential statistical analysis is carried out to analyze the data and to test the hypotheses. 4.1 Quantitative Data analysis: Through structured questionnaire and survey, we are collecting numerical data which can be quantified to answer our research questions and to meet our research objectives. All such data is called quantitative data. This data will become meaningful if analyzed and interpreted. Various quantitative analysis techniques are available to achieve this. These techniques include simple techniques like tabulation, graphs to complex statistical techniques like finding relationship among variables and forming statistical models. Designing the database is an important step in terms of defining the data fields & data type. The database for this survey has been developed in MS-Excel. Suitable data checks and validation techniques have been used while getting input (raw data) into the database. For the statistical analysis purpose, the database is converted into SPSS (Statistical Package for Social Sciences) format. Proper definition of variables, data type descriptions, value label descriptions are performed on the SPSS databases. Data validation & cleaning process make the database more usable for the analysis purpose. Data validation is done through computer which includes logical checks, consistency checks and correction of data entry mistakes etc. We have used descriptive statistics to interpret the data and to get meaningful information out of it. Inferential statistics is used to test hypotheses and validating the theoretical framework. 69

2 4.2 Descriptive Analysis Descriptive Analysis of data collected through survey is done and the interpretation is given below. The data has been analyzed with reference to questionnaire given to respondents (Refer Appendix) Role and responsibilities of respondents In this section we have presented the analysis of respondents based upon job title. While designing a questionnaire and during survey a care has been taken that respondents shall be from the functional area related to topic of research and shall have sufficient decision making authority. Table 4.1 illustrates this for responses received. All respondents are from related field and are in a position which allows them decision making. Job title Frequency Percent Manager (and above)-quality/manufacturing /Process Excellence Engineer/Executive-Quality/Manufacturing/Process Excellence Head-Operations/Business Excellence/Process Excellence Process Excellence Consultant Master Black Belt (MBB) Black Belt (BB) Green Belt (GB) Table 4.1 Job title One important change we observed during survey. Previously Process Excellence function was handled by manager responsible for quality, like quality control manager, quality assurance head. For ISO 9001 deployment each organization is having MR or management representative, similarly in survey we have observed that many organizations are having post like Process Excellence Manager, Business Excellence Manager Etc. This indicates that Indian manufacturing organizations have started giving importance to Process Excellence implementation. 70

3 Manager(and above)- Quality/ Manufacturing/Process Excellence Head- oeprations/business Excellence /Process Excellence Engineer/Executive-Quality/Manufacturing/Process Excellence Responses JOB TITLE CEO/ Managing Director Green Belt(GB) Response Master Black Belt(MBB) Process Excellence Consultant Owner Black Belt(BB) 0.00% 20.00% 40.00% 60.00% Figure 4.1 Job title Experience of respondents Experience of respondents play vital role in decision making. More experienced respondents give accurate information. The experience shall be from related field. Table 4.2 illustrates the pattern of experience of respondents. 72.9% respondents are having experience of more than 10 years. Frequency Percent Less than 5 years years More than 10 years Total Table 4.2 Years of experience of respondents 71

4 Percent More than 10 years YEARS 5-10 years Less than 5 years Percent 0.00% 20.00% 40.00% 60.00% 80.00% Figure 4.2 Years of experience of respondents Location of respondent s organization (State): The intention of this question is to see the pattern of PEM implementation with respect to geographical location. This is illustrated in table 4.3. State Frequency Percent Andhra Pradesh Dadra & Nagar Haveli 1.6 Delhi Ghaziabad 1.6 Goa 1.6 Gujarat Haryana Jharkhand 1.6 Karnataka M.S 1.6 Madhya Pradesh Maharashtra Odisha Punjab Punjab & M.P

5 Rajasthan Tamil Nadu Telangana U.P Uttarakhand 1.6 West Bengal Total Table 4.3 Geographical location of organization The findings shown in the table is represented in the bar diagram (figure. 3) indicating major manufacturing organizations are in western India i.e. from Maharashtra (44.1%) followed by Southern India i.e. Tamil Nadu (10.7%) and Karnataka (10.2%) followed by Northern India i.e. Uttar Pradesh (9.6%) and so on. These states have high industrialization hence the more responses for survey received from these states. These states also are having good concentration of higher educational institutions like engineering colleges, management institutions etc. Higher industrialization in particular area increases competition among industries. This leads to deployment of various initiatives for process improvement to improve competitiveness. Even one can observed concentration of process excellence consultants or consulting firms is more in states where industrialization is more. This helps industries to implement process excellence methodology very easily in their process. 73

6 Percent West Bengal Uttarakhand U.P Telangana Tamil Nadu Rajasthan Punjab & MP Punjab STATES Odisha Maharashtra Madhya Pradesh M.S. Percent Karnataka Jharkand Haryana Gujrat Goa Ghaziabad Delhi Dadra & Nagar Haveli Andhra Pradesh 0.00% 10.00% 20.00% 30.00% 40.00% 50.00% Figure 4.3 Geographical location of organization 74

7 4.2.4 Manufacturing sector of respondent s organization The tabulated data as shown in table 4.4 is reflecting the sectors which are in various manufacturing field. The graph represents Electrical and Electronics scaling high with 30.5% followed by Automobiles (15.8%) and ITES/Automation (8.5%). These are process oriented sectors which follows assembly line concept. Frequency Percent Electrical/Electronics Pharmaceutical Chemical Heavy Engineering Automobile Food and Beverage Textile and Apparel Healthcare Aerospace Agro/Agriculture ITES/Automation Banking 1.6 General Engg Consulting/Research Oil& Gas Consumer durables/appliances Other services Metal/Steel Others Retail Telecom Total Table 4.4 Manufacturing sector 75

8 Percent Telecom Retail Others Metal/Steel Other services Consumer durables/appliance Oil& Gas Consulting/Research SECTOR General Engg. Banking ITES/Automation Agro/Agriculture Aerospace Healthcare Textile and Apparel Food and Beverage Automobile Heavy Engineering Chemical Pharmaceutical Electrical/Electronics 0.00% 5.00% 10.00% 15.00% 20.00% 25.00% 30.00% 35.00% Percent Figure 4.4 Manufacturing sector 76

9 4.2.5 No. of employees working in respondent s organization Number of employees working in an organization is one of the indicators of size of the organization. Table 4.5 reflects the distribution of respondents in four employee size group. 46.3% respondents are working for organizations employing more than 1000 persons followed by group employing employees (24.3%), employing (14.1%) and employing (15.3%). This may be due to larger organizations are having more function specific staff and dedicated person for PEM implementation. Frequency Percent Above Total Table 4.5 No. of employees working in respondent s organization PERCENT Above NO. OF EMPLOYEES % 10.00% 20.00% 30.00% 40.00% 50.00% Percent Figure 4.5 No. of employees working in respondent s organization 77

10 4.2.6 Annual Turnover of respondent s organization Table 4.6 gives the distribution of respondents, annual turnover wise. 29.4% respondents are from crores turnover group, followed by 27.7% from , then 24.3% respondents are from crores turnover group, and 18.6% from above turnover group. Frequency Percent Crores Crores Crores Above Crores Total Table 4.6 Annual turnover of organization PERCENT ANNUAL TURNOVER Above Crores Crores Crores Crores 0.00% 10.00% 20.00% 30.00% 40.00% Percent Figure 4.6 Annual turnover of organization Process excellence Programs implemented in respondent s organization As per table 4.7 for Process Excellence Program implemented at respondent s organization shows that lean is most popular, which is implemented by 60.5% respondents followed by Six Sigma (52%) and TOC (15.8%). Lean is more popular among automobile manufacturers. As lean is originated in Toyota, which is an automobile manufacturer, there may be natural tendency why auto manufacturers prefer lean. Those who have mentioned others are mainly who implemented partial Lean methodology by using 1 or 2 lean tools like Total Productive Maintenance, 5S etc. 78

11 Most of the organization prefers implementing more than 1 process excellence methodology simultaneously. Frequency Percent Lean Six Sigma Theory of Constraints TPM TQM ISO S Other Table 4.7 PEM implemented in organization Other PERCENT PROCESS EXCELLENCE PROGRAM 5S ISO TQM TPM Theory of Constraints Six Sigma Lean PERCENT 0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% Figure 4.7 PEM implemented in organization Further Detail Interpretation By doing further interpretation (Table 4.8) to see which process excellence methodology or combination of process excellence methodology respondents prefer. From Figure.4.8 we analyze that Lean Six Sigma is the most preferred combination of process excellence methodology by Indian 79

12 Manufacturing Organizations. 32% respondents implemented Lean Six Sigma this is followed by Lean with 19% respondents then follows Six Sigma. Others include those who implemented process excellence methodology other than Lean Six Sigma & Theory of Constraints or other than these 3 process excellence methodologies. This also includes those who are using only few of the tools of lean. Combination of all 3 process excellence methodologies (Lean, Six Sigma, and Theory of Constraints) is used by 7% organization. Lean and Theory of Constraint combination is used by 2% organization, Six Sigma and TOC is used by 2% organization. Only Theory of Constraint is used by 4% organization. This also indicates that Theory of Constraint is less preferred by Indian Manufacturing Organizations. Frequency Percent Lean Six Sigma TOC 7 4 Lean & Six Sigma Lean & TOC 4 2 Six Sigma & TOC 4 2 Lean, Six Sigma & TOC 13 7 Others None Total Table 4.8 PEM implementation 80

13 Percent PROCESS EXCELLENCE PROGRAM None Others Lean, Six Sigma & TOC Six Sigma & TOC Lean & TOC Lean & Six Sigma TOC Six Sigma Lean Percent Figure 4.8 PEM implementation Organizations Planning to Implement PEM Table 4.9 indicates distribution of respondent s organization planning to implement PEM in future. This indicates preference of organizations for PEM. Lean manufacturing is preferred by 45.8% organization, followed by Six sigma (37.9%) and Theory of Constraints (18.1%). 13% organizations prefer PEM other than Lean, Six Sigma and TOC, 24.3% mentioned their choice as none. Frequency Percent Six Sigma Lean Manufacturing Theory of Constraints None Other Table 4.9 PEM Respondent planning to implement 81

14 Percent PROCESS EXCELLENCE PROGRAM Other None Theory of Constraints Lean Manufacturing Six Sigma 0.00% 10.00% 20.00% 30.00% 40.00% 50.00% Percent Figure 4.9 PEM Respondent planning to implement Figure 4.9 indicates the graph representation of the organization planning to implement process excellence program. 45.8% of the respondents are planning to implement Lean Manufacturing, 37.9% respondents expressed interest in implementing Six Sigma Highest priority area to address in the future Respondents were asked to indicate priority area they want to address in future through PEM implementation. Respondents indicated Improving Processes (76.8%) is the top priority area. Frequency Percent Improving Processes Ensuring customer focus in organization Improving Sales Improving quality of product Reducing inventory Reducing cost Upgrade Technology Reducing Risk Table 4.10 Highest priority areas for future 82

15 Percent Reducing risk HIGHEST PRIORITY AREA FOR FUTURE Upgrading Technology Reducing cost Reducing Inventory Improving quality of product Improving Sales Ensuring customer focus in organization Percent Improving Process Figure 4.10 Highest priority areas for future Frequency Percent Cause & Effect Diagram Check sheets Pareto Analysis Control Charts Bar Chart Failure Mode & Effect Analysis (FMEA) Five Why s Flow Chart Histogram Project Evaluation and Review Techniques (PERT chart) Poka Yoke Prioritization Matrix Process Capability Process Mapping Project Charter Run Chart SIPOC Tree Diagram

16 Voice of Customer ANOVA (Analysis of Variance) Design of Experiment (DoE) Measurement System Analysis Quality Function Deployment Brain Storming S Gemba Continuous Flow Jidoka Just-in time Kaizen Kanban Nominal Group Technique Root Cause Analysis Single Minute Exchange of Die (SMED) Total Productive Maintenance (TPM) Value Stream Mapping (VSM) Table 4.11 Tools used for process excellence in respondent s organization 84

17 PERCENT TOOLS USED FOR PROCESS EXCELLENCE MANAGEMENT Value Stream Mapping(VSM) Total Productive Maintenance(TPM) Single Minute Exchange of Die Root Cause Analysis Nominal Group Technique Kanban Kaizen Just-in time Jidoka Continuous Flow Gemba 5S Brain Storming Quality Function Deployment Measurement System Analysis Design of Experiment(DoE) ANOVA(Analysis of Variance) Voice of Customer Tree Diagram SIPOC Run Chart Project Charter Process Mapping Process Capability Priortization Matrix Poka Yoke Project Evaluation and Review Techniques Histogram Flow Chart Five Why's Failure Mode & Effect Analysis(FMEA) Bar Charts Control Charts Pareto Analysis Check Sheets Cause and Effect Diagram 0.00% 20.00% 40.00% 60.00% 80.00% % PERCE Figure 4.11 Tools used for process excellence in respondent s organization Figure 4.11 illustrates the tools that are used for Process Excellence program, 5S tool is most favorably implemented in the organization with (80.80%), followed by Root Cause Analysis (69.50%), Kaizen (67.80%), Poka Yoke (60.50%) and Brain Storming (60.50%) and followed by other tools. 85

18 Each process excellence methodology is having its own tool set. Six sigma is having tools like Root cause analysis, Quality Function Deployment, Cause and effect diagram, Failure mode effect analysis etc. Similarly Lean is having tools like Value Stream mapping, Just in Time, 5S, Kanban, Kaizen, Total Productive Maintenance etc. Though comparatively very few tools are there in theory of constraints still it offers specific tools for special applications like critical chain for project management. Tool selection by an organization implementing Process Excellence Methodology, decide what performance level organization will achieve after PEM implementation. Specific application requires specific tools. Tool selection requires expertise and mastery over techniques of process excellence methodology. Correct tool selection leads to successful completion of project. In six sigma we often see that DMAIC requires specific tools which may not be that much effective for DFSS. DFSS requires specific tools like Failure Mode Effect Analysis, Quality Function Deployment. The output of survey throw some light on the way employees are inclined towards selection of tools. Tools which are easy to learn and implement like root cause analysis, brain storming are more preferred by project teams. Complicated tools which require highly specialized skills like FMEA, PERT, QFD are less popular. Some tools like Nominal Group technique, though easy to deploy, are not popular as its usage in India is comparatively low and it s new to many organizations. Organizations shall conduct training for their employees on usage of tools. Correct tool usage will lead to correct solution and finally successful completion of project. 86

19 Q16. Overall how successful would you rate Process Excellence Program in your organization Frequency Percent Highly unsuccessful Unsuccessful Neither successful nor unsuccessful Successful Highly successful Total Missing System Total Table 4.12 Overall success of PEM implementation Q16. Overall how successful would you rate Process Excellence Program in your organization Frequency Highly unsuccessful Unsuccessful Neither successful Successful Highly successful Total System Q16. Overall how successful would you rate Process Excellence Program in your organization Frequency Missing Figure 4.12 Overall success of PEM implementation We have asked respondents to express what they feel overall how much successful is PEM implementation. The respondent s response is as given in table no We can see 59.9% respondents feel that it is successful and 7.3% feel it is highly successful. So we can say that overall success rate of PEM implementation is 67.2%. 87

20 4.3 NORMALITY CHECKS: USING NORMAL CURVE DISTRIBUTION 1. If the curve is bell shaped, the data distribution in that variable is said to be Normal. 2. If the curve is like left bend is expanded towards left side, then it is said to be left skewed. i.e. most of the respondents have indicated low rating for that attribute 3. If the curve is like right bend is expanded towards right side, then it is said to be right skewed. i.e. most of the respondents have indicated high rating for that attribute 4. If the curve is look like flatting, then it is said to be non-normal. This variable should not be used for any statistical analysis. 4.4 Overall interpretation: Most of the rating scale variables follow normal distribution. Some rating scale variable follow normal with slight right skewed values, but can be used for analysis. In general, we cannot expect everything should be normal. Only thing we should see is it should not be completely left skewed or completed right skewed. For all the statistical analysis, the basic assumption is that the data selected for that analysis, follow normal distribution. If any variable is highly left skewed or highly right skewed, either we need to adjust the data or omit the variable, before we do statistical analysis. Q15 Training people questions: The data is not normal, as we have collected the data from different nature of companies and of different size. So, this type of situation will arise. Only thing is we cannot use for any advanced statistical analysis. This is a general question and is not used in any analysis. Q13. PEM Success factors-data received for all sub questions (25 nos.)follow normal distribution. And Q14. Post PEM Performance. Data received for all sub questions (21 nos.) follow normal distribution Outlier test: Test conducted to check whether any outlier is present. Outliers were not observed in data to be analyzed. Details given in Appendix III Skewness and Kurtosis. 88

21 4.5 Data Reliability Test: Cronbach Alpha Test Data reliability and validity checks were performed using Cronbach Alpha Test. The purpose of this test is to understand the data reliability. For this test, important key variables and the attribute variables have been considered. The scales were reversed in the negative statements. Measure of internal consistency (reliability) is called as Cronbach s alpha. Normally 0 to 1 is the Cronbach s alpha reliability coefficient s range. Greater the internal consistency of the item, closer the Cronbach s alpha coefficient to 1.0. Interpretation Norm: Cronbach s alpha α 0.9 consistency Internal Excellent 0.7 α < 0.9 Good 0.6 α < 0.7 Acceptable 0.5 α < 0.6 Poor α < 0.5 Unacceptable Higher value of alpha is partially dependent upon the number of items in the scale and this has diminishing returns. Cronbach s alpha of 0.75 is probably a reasonable goal. As it was planned to perform different statistical analyses, the data reliability and validity checks were performed using Cronbach Alpha Test. The results are presented here. 89

22 4.6 Variable consideration: CASE 1: Data reliability -Cronbach alpha test Cronbach alpha test - all the key variables and rating scale variables (excluding text questions). The following variables are considered for Case 1 - Cronbach alpha test. In this case, both categorical (nominal) and scale variables are considered. Table 4.13 Variable Consideration Serial Variable Variable details Type Number 1 Job title Q3 Job title Nominal 2 Years_exp Q4 Years of experience Nominal 3 Sector Q6 Sector Nominal 4 Q7Nr_staff Q7 No. of employees working in your organization Nominal 5 Q8Turnover_yr Q8 Annual turnover of your organization Nominal 6 Q9Lean Q9 Process Excellence Program implemented at your Nominal organization-lean 7 Q9sixsigma Q9 Process Excellence Program implemented at your organization-six sigma Nominal 8 Q9Theory_cons Q9 Process Excellence Program implemented at your organization-theory of constraints Nominal 9 Q9_TPM Q9 Process Excellence Program implemented at your Nominal organization-tpm 10 Q9_TQM Q9 Process Excellence Program implemented at your Nominal organization-tqm 11 Q9_ISO Q9 Process Excellence Program implemented at your Nominal organization-iso 12 Q9_5S Q9 Process Excellence Program implemented at your Nominal organization-5s 13 Q9Others Q9 Process Excellence Program implemented at your Nominal organization -Others 14 Q9None Q9 Process Excellence Program implemented at your Nominal organization- None 15 Q10Sixsig Q10 PEP-Planning to implement-six Sigma Nominal 16 Q10Leanmfr Q10 PEP-Planning to implement-lean manufacturing Nominal 17 Q10Theorycons Q10 PEP-Planning to implement-theory of constraints Nominal 18 Q10None Q10 PEP-Planning to implement-none Nominal 19 Q10Others Q10 PEP-Planning to implement-others Nominal 90

23 20 Q11PA_IMPPRO Q11Highest priority areas to address in the future ahead?- Improving processes 21 Q11PA_ECFO Q11Highest priority areas to address in the future ahead?- Ensuring customer focus in organization 22 Q11PA_IMPSAL Q11Highest priority areas to address in the future ahead?- Improving sales 23 Q11PA_IMPQLT Q11 Highest priority areas to address in the future ahead?- Improving quality of product 24 Q11PA_REDINV Q11 Highest priority areas to address in the future ahead?- Reducing inventory 25 Q11PA_REDCST Q11 Highest priority areas to address in the future ahead?- Reducing cost Nominal Nominal Nominal Nominal Nominal Nominal 26 Q11PA_UPGTEC Q11 Highest priority areas to address in the future ahead?- Nominal Upgrade technology 27 Q11PA_REDRISK Q11 Highest priority areas to address in the future ahead?- Reducing Risk Nominal 28 Q12Tool_CED Q12.Tools Used for PEM-1. Cause & Effect Diagram Nominal 29 Q12Tool_CKS Q12.Tools Used for PEM-2. Check sheets Nominal 30 Q12Tool_PRA Q12.Tools Used for PEM-3. Pareto Analysis Nominal 31 Q12Tool_CNC Q12.Tools Used for PEM-4. Control Charts Nominal 32 Q12Tool_BRC Q12.Tools Used for PEM-5. Bar Chart Nominal 33 Q12Tool_FMEA Q12.Tools Used for PEM-6. Failure Mode & Effect Analysis Nominal (FMEA) 34 Q12Tool_FWH Q12.Tools Used for PEM-7. Five Why s Nominal 35 Q12Tool_FLC Q12.Tools Used for PEM-8. Flow Chart Nominal 36 Q12Tool_HIS Q12.Tools Used for PEM-9. Histogram Nominal 37 Q12Tool_PERT Q12.Tools Used for PEM-10. Project Evaluation and Review Nominal Techniques (PERT chart) 38 Q12Tool_PKY Q12.Tools Used for PEM-11. Poka Yoke Nominal 39 Q12Tool_PRM Q12.Tools Used for PEM-12. Prioritization Matrix Nominal 40 Q12Tool_PRC Q12.Tools Used for PEM-13. Process Capability Nominal 41 Q12Tool_PCM Q12.Tools Used for PEM-14. Process Mapping Nominal 42 Q12Tool_PJC Q12.Tools Used for PEM-15. Project Charter Nominal 43 Q12Tool_RNC Q12.Tools Used for PEM-16. Run Chart Nominal 44 Q12Tool_SIP Q12.Tools Used for PEM-17. SIPOC Nominal 45 Q12Tool_TRD Q12.Tools Used for PEM-18. Tree Diagram Nominal 46 Q12Tool_VOC Q12.Tools Used for PEM-19. Voice of Customer Nominal 47 Q12Tool_ANO Q12.Tools Used for PEM-20. ANOVA (Analysis of Variance) Nominal 48 Q12Tool_DOE Q12.Tools Used for PEM-21. Design of Experiment (DoE) Nominal 49 Q12Tool_MSA Q12.Tools Used for PEM-22. Measurement System Analysis Nominal (MSA) 50 Q12Tool_QFD Q12.Tools Used for PEM-23. Quality Function Deployment Nominal 51 Q12Tool_BRS Q12.Tools Used for PEM-24. Brain Storming Nominal 52 Q12Tool_5S Q12.Tools Used for PEM-25. 5S Nominal 91

24 53 Q12Tool_GEM Q12.Tools Used for PEM-26. Gemba Nominal 54 Q12Tool_CNF Q12.Tools Used for PEM-27. Continuous Flow Nominal 55 Q12Tool_JID Q12.Tools Used for PEM-28. Jidoka Nominal 56 Q12Tool_JIT Q12.Tools Used for PEM-29. Just-in time Nominal 57 Q12Tool_KAZ Q12.Tools Used for PEM-30. Kaizen Nominal 58 Q12Tool_KAN Q12.Tools Used for PEM-31. Kanban Nominal 59 Q12Tool_NOM Q12.Tools Used for PEM-32. Nominal Group Technique Nominal 60 Q12Tool_RCA Q12.Tools Used for PEM-33. Root Cause Analysis Nominal 61 Q12Tool_SMED Q12.Tools Used for PEM-34. Single Minute Exchange of Die Nominal (SMED) 62 Q12Tool_TPM Q12.Tools Used for PEM-35. Total Productive Maintenance Nominal (TPM) 63 Q12Tool_VSM Q12.Tools Used for PEM-36. Value Stream Mapping (VSM) Nominal 64 Q13_SF_LDR Q13.PEM Success Factors Rating-Leadership Scale: 1 65 Q13_SF_MIC Q13.PEM Success Factors Rating-Management Involvement & commitment Scale: 1 66 Q13_SF_ORI Q13.PEM Success Factors Rating-Organization Infrastructure Scale: 1 67 Q13_SF_CSF Q13.PEM Success Factors Rating-Customer focus Scale: 1 68 Q13_SF_EMT Q13.PEM Success Factors Rating-Employee Training Scale: 1 69 Q13_SF_UMTT Q13.PEM Success Factors Rating-Understanding methodology, tools and techniques Scale: 1 70 Q13_SF_UCR Q13.PEM Success Factors Rating-Understanding customer requirement Scale: 1 71 Q13_SF_LBS Q13.PEM Success Factors Rating-Linkage to business Scale: 1 strategy 72 Q13_SF_CI Q13.PEM Success Factors Rating-Customer involvement Scale: 1 73 Q13_SF_SI Q13.PEM Success Factors Rating-Supplier involvement Scale: 1 74 Q13_SF_RLS Q13.PEM Success Factors Rating-Relationships with suppliers. Scale: 1 75 Q13_SF_ST Q13.PEM Success Factors Rating-Supplier Training Scale: 1 76 Q13_SF_WEC Q13.PEM Success Factors Rating-Work Environment & Culture Scale: 1 77 Q13_SF_TBTS Q13.PEM Success Factors Rating-Team building and Team spirit Scale: 1 78 Q13_SF_ER Q13.PEM Success Factors Rating-Employee recognition Scale: 1 92

25 79 Q13_SF_IW 80 Q13_SF_CC 81 Q13_SF_PMS Q13.PEM Success Factors Rating-Involvement of workers Q13.PEM Success Factors Rating-Cultural change Q13.PEM Success Factors Rating-Project management skills 82 Q13_SF_LHR Q13.PEM Success Factors Rating-Linkage to human resources 83 Q13_SF_NCE Q13.PEM Success Factors Rating-Number of Certified employees 84 Q13_SF_CBD Q13.PEM Success Factors Rating-Co-ordination between departments (such as marketing, manufacturing, and purchasing) 85 Q13_SF_IMQ Q13.PEM Success Factors Rating-Involvement of Manufacturing and quality people in the product development process. 86 Q13_SF_DFM Q13.PEM Success Factors Rating-Design for Scale: 1 Scale: 1 Scale: 1 Scale: 1 Scale: 1 Scale: 1 Scale: 1 Scale: 1 manufacturability. 87 Q13_SF_PM Q13.PEM Success Factors Rating-Process management Scale: 1 88 Q13_SF_PD Q13.PEM Success Factors Rating-Process design Scale: 1 89 Q13_SF_PRM Q13.PEM Success Factors Rating-Preventive maintenance Scale: 1 90 Q14_Post_PEM_IQP Q14.Post PEM Performance Rating-Improvement in Quality of Products 91 Q14_Post_PEM_RPV Q14.Post PEM Performance Rating-Reduction in Process Variability 92 Q14_Post_PEM_IDP Q14.Post PEM Performance Rating-Improvement in delivery of Product 93 Q14_Post_PEM_RSR Q14.Post PEM Performance Rating-Reduction in Scrap and Rework 94 Q14_Post_PEM_RPC Q14.Post PEM Performance Rating-Reduction in Process Cycle Time 95 Q14_Post_PEM_ICS Q14.Post PEM Performance Rating-Improvement in Customer satisfaction Scale: 1 Scale: 1 Scale: 1 Scale: 1 Scale: 1 Scale: 1 96 Q14_Post_PEM_IMS Q14.Post PEM Performance Rating-Improvement in Sales Scale: 1 97 Q14_Post_PEM_IMK Q14.Post PEM Performance Rating-Increase in Market share Scale: 1 98 Q14_Post_PEM_DPUC Q14.Post PEM Performance Rating-Decrease in the per unit cost of manufacturing Scale: 1 93

26 99 Q14_Post_PEM_IOT Q14.Post PEM Performance Rating-Increase in organization s turnover 100 Q14_Post_PEM_IOP Q14.Post PEM Performance Rating-Increase in organization s profits 101 Q14_Post_PEM_IRV Q14.Post PEM Performance Rating-Increase in Return on investment 102 Q14_Post_PEM_IRRE Q14.Post PEM Performance Rating-Improvement in retention rate of employees 103 Q14_Post_PEM_IPEP Q14.Post PEM Performance Rating-Improvement in per employee productivity 104 Q14_Post_PEM_IETH Q14.Post PEM Performance Rating-Increase in employee training hours 105 Q14_Post_PEM_IEM Q14.Post PEM Performance Rating-Improvement in Employee moral 106 Q14_Post_PEM_IEML Q14.Post PEM Performance Rating-Increase in employee motivation level 107 Q14_Post_PEM_IESW Q14.Post PEM Performance Rating-Increase in employee s satisfaction with their work profile 108 Q14_Post_PEM_IESS Q14.Post PEM Performance Rating-Increase in employee satisfaction with support and facilities offered at workplace 109 Q14_Post_PEM_IEIP Q14.Post PEM Performance Rating-Increase in employee involvement in problem solving process 110 Q14_Post_PEM_RED Q14.Post PEM Performance Rating-Reduction in the equipment downtime 111 Q16_PEP_success Q16. Overall how successful would you rate Process Excellence Program in your organization 112 Q17_PEP_ER Q17.Obstacles/barriers to implementation of Process Excellence program in your organization-employee resistance 113 Q17_PEP_PWC Q17.Obstacles/barriers to implementation of Process Excellence program in your organization-present work culture 114 Q17_PEP_MMR Q17.Obstacles/barriers to implementation of Process Excellence program in your organization-middle management resistance 115 Q17_PEP_BC Q17.Obstacles/barriers to implementation of Process Excellence program in your organization-budget constraint Scale: 1 Scale: 1 Scale: 1 Scale: 1 Scale: 1 Scale: 1 Scale: 1 Scale: 1 Scale: 1 Scale: 1 Scale: 1 Scale: 1 Scale: 1 Nominal Nominal Nominal Nominal 116 Q17_PEP_SR Q17.Obstacles/barriers to implementation of Process Nominal Excellence program in your organization-supervisory resistance 117 Q17_PEP_LIKH Q17.Obstacles/barriers to implementation of Process Nominal Excellence program in your organization-lack of implementation know-how 118 Q18_AWARD_IMCRAM Q18.Awarded-IMC Ramkrishna Bajaj National Quality Award Nominal 119 Q18_AWD_CIIEXIM Q18.Awarded-CII-Exim Business Excellence Award Nominal 94

27 120 C18_RAJIVGAN Q18.Awarded-Rajiv Gandhi National Quality Award Nominal 121 Q18_NONE Q18.None Awarded Nominal Table 4.13 Variable Consideration Analysis Results: The SPSS results on the selected variables are as below: Case Processing Summary N % Cases Valid Excluded 0.0 Total Table 4.14 Case Processing Summary There are 177 respondents in total. All the respondents have answered for these 121 variables in the study. Reliability Statistics Cronbach's Alpha N of Items (variables selected) Table 4.15 Reliability Statistics Interpretation: The Cronbach alpha is high at hence it is concluded that the internal reliability of data is very good. This implied to help us to proceed with further required statistical analyses. CASE 2: Data reliability -Cronbach alpha test -only the rating scale variables The following variables are considered for Case 2 - Cronbach alpha test. There are 48 variables used for this test., which are of five point rating scale type. Table 4.16 Variable consideration; Serial Number Variable Variable details Type 1 Q13_SF_LDR Q13.PEM Success Factors Rating-Leadership 2 Q13_SF_MIC Q13.PEM Success Factors Rating-Management Involvement & commitment Scale type: 1 Scale type: 1 95

28 3 Q13_SF_ORI Q13.PEM Success Factors Rating-Organization Infrastructure 4 Q13_SF_CSF Q13.PEM Success Factors Rating-Customer focus 5 Q13_SF_EMT Q13.PEM Success Factors Rating-Employee Training 6 Q13_SF_UM TT 7 Q13_SF_UCR Q13.PEM Success Factors Rating-Understanding methodology, tools and techniques Q13.PEM Success Factors Rating-Understanding customer requirement 8 Q13_SF_LBS Q13.PEM Success Factors Rating-Linkage to business strategy 9 Q13_SF_CI Q13.PEM Success Factors Rating-Customer involvement 10 Q13_SF_SI Q13.PEM Success Factors Rating-Supplier involvement 11 Q13_SF_RLS Q13.PEM Success Factors Rating-Relationships with suppliers. 12 Q13_SF_ST Q13.PEM Success Factors Rating-Supplier Training 13 Q13_SF_WEC 14 Q13_SF_TBT S Q13.PEM Success Factors Rating-Work Environment & Culture Q13.PEM Success Factors Rating-Team building and Team spirit 15 Q13_SF_ER Q13.PEM Success Factors Rating-Employee recognition 16 Q13_SF_IW Q13.PEM Success Factors Rating-Involvement of workers 17 Q13_SF_CC Q13.PEM Success Factors Rating-Cultural change 18 Q13_SF_PMS Q13.PEM Success Factors Rating-Project management skills 19 Q13_SF_LHR Q13.PEM Success Factors Rating-Linkage to human resources 20 Q13_SF_NCE Q13.PEM Success Factors Rating-Number of Certified employees Scale type: 1 Scale type: 1 Scale type: 1 Scale type: 1 Scale type: 1 Scale type: 1 Scale type: 1 Scale type: 1 Scale type: 1 Scale type: 1 Scale type: 1 Scale type: 1 Scale type: 1 Scale type: 1 Scale type: 1 Scale type: 1 Scale type: 1 Scale type: 1 21 Q13_SF_CBD 22 Q13_SF_IMQ 23 Q13_SF_DFM Q13.PEM Success Factors Rating-Co-ordination between departments (such as marketing, manufacturing, and purchasing) Q13.PEM Success Factors Rating-Involvement of Manufacturing and quality people in the product development process. Q13.PEM Success Factors Rating-Design for manufacturability. Scale type: 1 Scale type: 1 Scale type: 1 96

29 24 Q13_SF_PM Q13.PEM Success Factors Rating-Process management Scale type: 1 25 Q13_SF_PD Q13.PEM Success Factors Rating-Process design Scale type: 1 26 Q13_SF_PRM Q13.PEM Success Factors Rating-Preventive maintenance Scale type: 1 27 Q14_Post_PE Q14.Post PEM Performance Rating-Improvement in Quality Scale type: M_IQP of Products 1 28 Q14_Post_PE Q14.Post PEM Performance Rating-Reduction in Process Scale type: M_RPV Variability 1 29 Q14_Post_PE Q14.Post PEM Performance Rating-Improvement in delivery Scale type: M_IDP of Product 1 30 Q14_Post_PE Q14.Post PEM Performance Rating-Reduction in Scrap and Scale type: M_RSR Rework 1 31 Q14_Post_PE Q14.Post PEM Performance Rating-Reduction in Process Scale type: M_RPC Cycle Time 1 32 Q14_Post_PE Q14.Post PEM Performance Rating-Improvement in Customer Scale type: M_ICS satisfaction 1 33 Q14_Post_PE Scale type: Q14.Post PEM Performance Rating-Improvement in Sales M_IMS 1 34 Q14_Post_PE Scale type: Q14.Post PEM Performance Rating-Increase in Market share M_IMK 1 35 Q14_Post_PE Q14.Post PEM Performance Rating-Decrease in the per unit Scale type: M_DPUC cost of manufacturing 1 36 Q14_Post_PE Q14.Post PEM Performance Rating-Increase in organization s Scale type: M_IOT turnover 1 37 Q14_Post_PE Q14.Post PEM Performance Rating-Increase in organization s Scale type: M_IOP profits 1 38 Q14_Post_PE Q14.Post PEM Performance Rating-Increase in Return on Scale type: M_IRV investment 1 39 Q14_Post_PE Q14.Post PEM Performance Rating-Improvement in retention Scale type: M_IRRE rate of employees 1 40 Q14_Post_PE Q14.Post PEM Performance Rating-Improvement in per Scale type: M_IPEP employee productivity 1 41 Q14_Post_PE Q14.Post PEM Performance Rating-Increase in employee Scale type: M_IETH training hours 1 42 Q14_Post_PE Q14.Post PEM Performance Rating-Improvement in Scale type: M_IEM Employee moral 1 43 Q14_Post_PE Q14.Post PEM Performance Rating-Increase in employee Scale type: M_IEML motivation level 1 44 Q14_Post_PE Q14.Post PEM Performance Rating-Increase in employee s Scale type: M_IESW satisfaction with their work profile 1 45 Q14_Post_PE Q14.Post PEM Performance Rating-Increase in employee Scale type: M_IESS satisfaction with support and facilities offered at workplace 1 46 Q14_Post_PE Q14.Post PEM Performance Rating-Increase in employee Scale type: M_IEIP involvement in problem solving process 1 47 Q14_Post_PE Q14.Post PEM Performance Rating-Reduction in the Scale type: 97

30 M_RED equipment downtime 1 Q16_PEP_su Q16. Overall how successful would you rate Process Scale type: 48 ccess Excellence Program in your organization 1 Table 4.16 Variable - Cronbach alpha Analysis Results: The SPSS results on the selected variables are as below: Case Processing Summary N % Cases Valid Excluded Total Table 4.17 Case Processing Summary There are 177 respondents in total. More than 90% of the respondents have given rating for these selected variables. For no responses cases, a zero value is assigned and performed the analysis. Reliability Statistics Cronbach's Alpha N of Items (variables selected) Table 4.18 Reliability Statistics Interpretation: The Cronbach alpha is very high at hence it is concluded that the internal reliability of data is excellent. Hence, we conclude that the internal data reliability is good and the data can be further used for advanced statistical analysis. 98

31 4.7 Factor Analysis FACTOR ANALYSIS: AMONG THE ATTRIBUTES OF PEM SUCCESS ASPECTS (Q13) Objective: There are multiple attributes under PEM success aspects. The ratings are collected for each attribute from the surveyed respondents. Factor Analysis is tried here to understand whether the set of these attributes form any grouping or not. If they form groupings, what is the nature of such groups? This is done to reduce the number of statements meaningfully into certain groups. Statistical Technique: Factor Analysis technique for data reduction. Factor Analysis is a data reduction & summarization technique. It reduces a complicated data set into a simplified format. Factors are linear combinations of variables. Approach: F1= W1X1+W2X3+.+WkXn Combinations are based on weighs developed by the analysis Weights are called a loadings 1. Formulate the problem 2. Construct the correlation matrix 3. Identify the method of factor extraction 4. Determine the number of factors 5. Rotate the factors 99

32 6. Interpret the factors 7. Use the final data set for further analysis Kaiser-Meyer Olkin (KMO), measure of sampling adequacy. If KMO >= 0.5, it is desirable. i.e. the number of records we have sufficient to run the factor analysis. In case KMO is < 0.5, either we can add more records or drop the factor analysis. Factor Extraction: Extraction: The process by which the factors are determined from a large set of variables. Factor Extraction helps us in knowing: How many components (factors) are needed to represent the variables? What do these components (factors) represent? The concern here is to determine the minimum number of factors that will account for maximum variance in the data. Principal Component Method of Extraction: Used when the goal is to do data reduction To find a linear combination of variables (a component) that accounts for as much variation Each component is a weighted linear combination of the variables Transforms a set of variables into a new set of variables (components) that are not correlated with each other (*) C i W i1 X 1 W i 2 X 2 W ip X p - The best combination= 1 st st factor Principal Component (high variance) =1 100

33 - 2 nd PC = Another linear combination of variables, which explains variance, not accounted by 1 st PC - Likewise, it continues - till all the variance is accounted - Each component becomes a factor Factor extraction method produces the following results: Communality matrix Component/ Factor list Eigen Values (Total variance explained by each component/factor) Extraction sum of square loadings for each component/factor Rotation sum of square loadings for each component/factor Scree Plot Eigen Value: Total Variance Explained Eigen value is the total variance explained by each component/ factor. i.e. amount of variance in the original variables accounted by each factor/component. The purpose of Factor Analysis is not just to arrive at components / factors. But to summarize the information presented in the original variables. To do this, smaller number of factors should be extracted, based on the eigen values (most used method). Eigen value is an index of the strength of the factor. By strength, it means the amount of variance it accounts for. It is the sum of the squared loadings. We set to get the number of factors which achieve the Eigen value > 1. approach. Varimax Rotation variables identify with different factors. It is commonly used 101

34 Interpreting a Component/ Factor Matrix: Examine the Factor Matrix of Loadings. Factor loadings - the correlations between the factor and the variables. The output is sorted in descending order of factor loading. Identify the variables that have large loading on the same factor. Variable considerations: (5 point scale, 1 being least important and 5 being most important, 26 variables) Q13.PEM Success Factors Rating-Leadership Q13.PEM Success Factors Rating-Management Involvement & commitment Q13.PEM Success Factors Rating-Organization Infrastructure Q13.PEM Success Factors Rating-Customer focus Q13.PEM Success Factors Rating-Employee Training Q13.PEM Success Factors Rating-Understanding methodology, tools and techniques Q13.PEM Success Factors Rating-Understanding customer requirement Q13.PEM Success Factors Rating-Linkage to business strategy Q13.PEM Success Factors Rating-Customer involvement Q13.PEM Success Factors Rating-Supplier involvement Q13.PEM Success Factors Rating-Relationships with suppliers. Q13.PEM Success Factors Rating-Supplier Training Q13.PEM Success Factors Rating-Work Environment & Culture Q13.PEM Success Factors Rating-Team building and Team spirit Q13.PEM Success Factors Rating-Employee recognition Q13.PEM Success Factors Rating-Involvement of workers Q13.PEM Success Factors Rating-Cultural change Q13.PEM Success Factors Rating-Project management skills Q13.PEM Success Factors Rating-Linkage to human resources Q13.PEM Success Factors Rating-Number of Certified employees 102

35 Q13.PEM Success Factors Rating-Co-ordination between departments (such as marketing, manufacturing, and purchasing) Q13.PEM Success Factors Rating-Involvement of Manufacturing and quality people in the product development process. Q13.PEM Success Factors Rating-Design for manufacturability. Q13.PEM Success Factors Rating-Process management Q13.PEM Success Factors Rating-Process design Q13.PEM Success Factors Rating-Preventive maintenance Factor Analysis Outputs: KMO and Bartlett's Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy. Approx. Chi-Square Bartlett's Test of Df 325 Sphericity Sig..000 KMO and Bartlett's Test Interpretation: The KMO measure is greater than 0.5, hence there is a more than sufficient sample size to run the factor analysis. Total Variance Explained: Initial Eigen values Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings % of % of Cumulative % of Cumulative Component Total Variance Cumulative % Total Variance % Total Variance %

36 Total Variance Explained Interpretation: With eigen value > 1, we get 5 factors (from the 26 statements given as input variables). The total variance explained by these 5 factors is 63.23%. This is a good and accepted level of variance. 104

37 Rotated Component Matrix a Component Q13.PEM Success Factors Rating-Number of Certified employees Q13.PEM Success Factors Rating-Project management skills Q13.PEM Success Factors Rating-Understanding methodology, tools and techniques Q13.PEM Success Factors Rating-Employee Training Q13.PEM Success Factors Rating-Linkage to human resources Q13.PEM Success Factors Rating-Organization Infrastructure Q13.PEM Success Factors Rating-Team building and Team spirit Q13.PEM Success Factors Rating-Preventive maintenance Q13.PEM Success Factors Rating-Management Involvement & commitment Q13.PEM Success Factors Rating-Leadership Q13.PEM Success Factors Rating-Involvement of workers Q13.PEM Success Factors Rating-Cultural change Q13.PEM Success Factors Rating-Employee recognition Q13.PEM Success Factors Rating-Work Environment & Culture Q13.PEM Success Factors Rating-Design for manufacturability Q13.PEM Success Factors Rating-Involvement of Manufacturing and quality people in the product development process. Q13.PEM Success Factors Rating-Process design Q13.PEM Success Factors Rating-Co-ordination between departments (such as marketing, manufacturing, and purchasing) Q13.PEM Success Factors Rating-Process management Q13.PEM Success Factors Rating-Supplier involvement Q13.PEM Success Factors Rating-Relationships with suppliers Q13.PEM Success Factors Rating-Supplier Training Q13.PEM Success Factors Rating-Customer involvement Q13.PEM Success Factors Rating-Customer focus Q13.PEM Success Factors Rating-Understanding customer requirement Q13.PEM Success Factors Rating-Linkage to business strategy Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. a. Rotation converged in 16 iterations. Interpretation: The values appear in the above table are arrived from the factor analysis. The maximum value that go into each statement is marked in colour above. Using the same, we can give suitable name and use the factors for further analysis. For further analysis, these five factors can be named and used. 105

38 In this dissertation, only the factor analysis is performed to understand whether the input statements form meaningful factors. There is no further analysis done using the factor analysis results, as it is generally not the focus of this research work FACTOR ANALYSIS: AMONG THE ATTRIBUTES OF POST PEM PERFORMANCE RATING (Q14) Objective: There are multiple attributes under Post PEM Performance. The ratings are collected for each attribute from the surveyed respondents. Factor Analysis is tried here to understand whether the set of these attributes form any grouping or not. If they form groupings, it is also tried to understand the nature of such groups. This is done to reduce the number of statements meaningfully into certain groups. Statistical Technique: Factor Analysis technique for data reduction. Variable considerations: (5 point scale, 1 being No benefit, 5 being Excellent benefit, 21 variables) Q14.Post PEM Performance Rating-Improvement in Quality of Products Q14.Post PEM Performance Rating-Reduction in Process Variability Q14.Post PEM Performance Rating-Improvement in delivery of Product Q14.Post PEM Performance Rating-Reduction in Scrap and Rework Q14.Post PEM Performance Rating-Reduction in Process Cycle Time Q14.Post PEM Performance Rating-Improvement in Customer satisfaction Q14.Post PEM Performance Rating-Improvement in Sales Q14.Post PEM Performance Rating-Increase in Market share Q14.Post PEM Performance Rating-Decrease in the per unit cost of manufacturing Q14.Post PEM Performance Rating-Increase in organization s turnover Q14.Post PEM Performance Rating-Increase in organization s profits Q14.Post PEM Performance Rating-Increase in Return on investment 106

39 Q14.Post PEM Performance Rating-Improvement in retention rate of employees Q14.Post PEM Performance Rating-Improvement in per employee productivity Q14.Post PEM Performance Rating-Increase in employee training hours Q14.Post PEM Performance Rating-Improvement in Employee moral Q14.Post PEM Performance Rating-Increase in employee motivation level Q14.Post PEM Performance Rating-Increase in employee s satisfaction with their work profile Q14.Post PEM Performance Rating-Increase in employee satisfaction with support and facilities offered at workplace Q14.Post PEM Performance Rating-Increase in employee involvement in problem solving process Q14.Post PEM Performance Rating-Reduction in the equipment downtime Factor Analysis Outputs: KMO and Bartlett's Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy. Approx. Chi-Square Bartlett's Test of df 210 Sphericity Sig..000 Interpretation: The KMO measure is greater than 0.5, hence there is a more than sufficient sample size to run the factor analysis. Total Variance Explained: Initial Eigen values Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings % of Cumulative % of Cumulative % of Cumulative Component Total Variance % Total Variance % Total Variance %