Analysis the Relationship between the Quality, Productivity and Safety in Manufacturing Setup

In the manufacturing industry specifically, quality, productivity, & safety have long been considered three key measures of success. They are seldom stressed together, though. It is because the goals of quality management and productivity management are seen as competing that these three concepts are rarely stressed all at once (Omachonu 1994). According to recent studies, there should be a positive correlation between quality, productivity, & safety. However this theory relies more on inference and deduction than on tried and true models in the real world. The correlation between quality, productivity, & safety has been the subject of few mathematical models. Yet, these current models suffer from serious flaws. As a result of these flaws, current models are useless in practice. In this post, we look at the logic behind the connection between efficiency, effectiveness, & protection. It is vital to choose a common base through which to relate quality, productivity, & safety as their definitions vary. Two industries, Maruti & Shree sai techno, will be used to test the models' viability in the manufacturing industry. The definition of quality depends on perception of a person in a given situation, which can be cost oriented, user oriented, supplier oriented etc. Quality is defined as fitness for purpose (Juran, 1994). If the component perform in the safe manner in the required situation, it is called Quality product (Mahajan, 2010). The collection of activities through which we meet the quality needs of society is called quality function. In a manufacturing concern, the means through which the company meets the needs of customers is the quality function. Hence in general terms, quality function is the collection of activities which involves all the departments, vendors, links in distribution chain & consumers. This research will look at how well the Maharashtra manufacturing industry performs in terms of quality, safety, and productivity, and a model will to investigate the relationship between the characteristics.

Methodology is the systematic, theoretical analysis of the methods applied to a field of study. This systematic research focuses on objectives & data collection. It comprises the theoretical analysis of the body of methods and principles associated with a branch of knowledge. Typically, it encompasses concepts such as paradigm and theoretical model, phases. The term Research is related to seek out the information and knowledge on a particular topic or subject. In other words, research is an art of systematic investigation.

Quantitative data can be collected in a uniform fashion with the use of a questionnaire, making it easier to analyze the acquired information. An initial survey is developed by taking into account respondents' levels of education and professional experience. The questions and their accompanying wording must be easily understood by the respondents.

Both primary and secondary sources are used in this investigation. It aided in completely encompassing the scope of the research. The search issue is targeted by collecting primary data. The data comes from interviews with professionals & academics in the Maharashtra area's many manufacturing sectors. The research focused on both medium- and large-sized businesses. Publications like academic journals, government reports, trade magazines, websites, and others are mined for secondary data. The secondary information provides specifics about the various industries.

A structured questionnaire is prepared using literature survey and in-depth interview with concerned people. An in depth- interview helps in enriching the information and makes the qualitative analysis more focused. It helps in making the questionnaire more specific and accurate for a large stratified survey. This study utilized a survey approach with a closed questionnaire. It was distributed in the industry professionals at Maharashtra region. The industries were randomly chosen in each zone. To ensure proper representation of manufacturing industries, we have chosen industries of different sizes, different types of production, different annual turnover and different year of establishments at various zones of Maharashtra state. The fieldwork was conducted in four phases, as seen in Table 1. Initial phase includes interviews of local manufacturing industries in Maharashtra State of India. This information collected from interviews helped in ascertaining the relevance of questions in the pilot questionnaire. It also helped in assessing the role of influential factors within the manufacturing industry. Second phase involved open-ended, moderately directed interviews and direct field observations in local manufacturing industries in Maharashtra State of India. These methods provided data for conducting a detailed analysis of the dynamics of various industries. These methods also brought out Maharashtra State of India. The details of the respondents is shown below- A questionnaire based on Likert scale was circulated .Questionnaire is being used to get a person's perspective on a Likert's scale which measures from 1 to 5 with meaning ranging from "not important" to "most important". To make the questions of questionnaire easy to understand by various respondents, all of them were educated so that they can select the right option .All the terms associated with the questions were explained deeply to them. This helped them to understand importance of question. Also the feedback was taken to ensure that they understood question incorrect perspective.

After the analysis of pilot questionnaire, the different variables of critical success factors and performance measures are scrutinized to be included in the final questionnaire. The critical success factors commonly known as input factors reduced from 62 to 43 variables and performance measures which are commonly known as output factors are made from 38 to 47 variables .The overlapping variables are also identified and merged into a single variable.

In the third phase the analysis of the data collected from respondents is done. Based on the suggestions from various respondents some factors are either added or deleted or modified. All the suggestions from the respondents are incorporated in the final questionnaire. The last phase of the fieldwork comprises of a survey consisting of distribution of final questionnaire to the various respondents across the Maharashtra state. A total of 450 questionnaires were circulated to the various Medium and Large manufacturing industries. Out of 450 only 250 questionnaires were returned with valid responses .The questionnaires were filled by the respondents having sound knowledge of their respective work. First of all initial screening of the questionnaire was done to check whether all the questions have been properly tick marked or not age of the industry, experience of respondents, their qualification and designation. After sorting all the data are systematically recorded in MS Excel sheet.

After the distribution of final questionnaire, a continuous follow up procedure was followed to get the responses. It resulted in receiving a total of 312 responses out of which only 250 were valid responses. It resulted in a response rate of 64% which is quite encouraging. Some other authors have also used questionnaire as a tool for collecting the data. Figure present below show the classification of respondents of questionnaires.

The data analysis is done with the help of statistical tools such as mean (central tendency measure) and standard deviation (measure of variability). The table given below shows the minimum and maximum score of input and output factors with variance. Also 100% of the questions revealed a mean score of more than 4.0, indicating a high importance to various aspects of performance improvement. Furthermore, the standard deviation values were relatively low (for majority of questions, standard deviation value was less than 30% of the mean).

The tests of normality, reliability, adequacy and validity are the four major tests used to evaluate a measurement tool and subsequent measurements. The data should follow normal distribution. Normality test refers the bell shape nature of the curve. Reliability tells the accuracy and precision of a measurement procedure. Data adequacy confirms that obtained data is sufficient for analysis. Validity refers to the extent of measure. The tests of sound measurement are:

• Sample Adequacy

• Normality

• Validity

 Reliability

The size of sample is of prime importance. Larger sample size reduces sampling error. For sample sizes of 200 or more, however, these same effects may be negligible. Moreover, when group comparisons are made, such as in ANOVA, the differing sample sizes between groups are large enough, can even cancel out the detrimental effects. Thus this research has 250 responses (large sample size). a) In the book 'Elementary Statistics: A Modem Approach' written in 2003 by Altares et.al a Sloven's formula is given for sample size calculation. n=N/l+N*e2 Where: n=Sample Size (Minimum and Adequate for Analysis) N= Population Size@ (185 Medium, 1201 Large Registered Units)[ data taken from District Industrial Corporation, Maharashtra] e= Margin of Error denotes the allowed probability of committing an error in selecting a small representative of the population (0.05 for 95 % Confidence Level)

= 1386/ (1+1386*0.05*0.05)

= 310 (Samples) Based on the above listed considerations given for the calculation of minimum number of responses, the number of responses taken for study i.e. 250 is sufficient for further studies. It indicates that the sample size is adequate.

A lot of statistical tests are based on the assumption that the data is normally distributed. So first of all we should check the trueness of this assumption. The normality test is carried out for all input items and all output items. Normality test refers the bell shape nature of the curve. It is necessary that data follows normal distribution i.e. the data should lie within the inverted bell shaped curve. At a confidence level of 95%, a clearance of 2.5% on either side is taken. Skewness and Kurtosis refer to the shape of the distribution. These are used with interval and ratio level data. When the observed distribution is exactly normal, values of skewness and kurtosis are zero. For a good data, the values of skewness and kurtosis should be nearer to zero. The Kolmogorov - Smimov statistic and the Shapiro - Wilks Statistics with a significance level for testing Table 4: Normality for input variables Case Processing Summary Descriptive Tests of Normality *. This is a lower bound of the true significance. a. . Lilliefors Significance Correction

a. Lilliefors Significance Correction

Validity is used to check the quality of a test. The term validity refers to a measure of closeness what it claims to measure. The three most important type of validity tests for research are: on how well the researchers created the measurement items to cover the content domain of the variable being measured. Its determination is subjectively judged. If a factor has items that cover all possible aspects of the variable being measured, then it is said to have content validity. The philosophy mentioned by various researchers is being used here to frame the questionnaire. In our study an exhaustive literature review has been done for the selection of measurement items and knowledge has been collected by visiting the plants. Academics, researchers, and specialists have all contributed to a thorough analysis. All these ensure the content validity at the stage of questionnaire formation.

Criterion validity tells us how well one variable predicts an outcome based on information from other variables. In this research criterion-related validity of the instrument evaluated the correlation coefficients between six input and six output performance measurement factors. The correlation coefficients between all of the variables are positive & statistically significant. The Bivariate correlation between the input and output factors are shown below in Table 6.

**. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed).

If a factor measures a theoretical construct which is designed to measure then it has construct validity. Factor analysis is most commonly used to identify the items to be included in measuring instrument. Appropriateness of the data is also measured by examining the minimum no. of

no of variables. Flynn et al. (1994) stated that a sample size of 30 or more is statistically sufficient for the analysis. Construct validity is thus an assessment of the quality of an instrument or experimental design. It says 'Does it measure the construct it is supposed to measure'. If you do not have construct validity, you will likely draw incorrect conclusions from the experiment It measure if the items are belonging to same group or not. If the items do not belong to that group, eliminate that or check the appropriate group. This is done with the help of principal component factor analysis. The factor matrices showed that they are uni-factorial with Eigen values (Eigen values represent the amount of variance explained by the factor, or relative importance of each factor in accounting for variance associated with the set of variables being analyzed) greater than the accepted criterion. The component matrix, Eigenvalue and percentage of variance for all input and output values are shown below-

Reliability refers to the extent to which an experiment or measuring procedure yields a same value on repeated trials. (Carmines and Zeller, 1979).Generally the internal consistency method is used in the instrument development process involving the field studies. Internal consistency can be estimated using reliability coefficient Cronbach α. The computation of Cronbach's alpha is based on the number of items on the survey (k) and the ratio of the average inter-item covariance to the average item variance. The reliability analysis of a questionnaire determines its ability to yield consistent results. The Cronbach's alpha value ranges from 0 to 1. If the value lies between 0.6 to 1 then instrument is said to be reliable. (Wee and Quazi, 2005, Sarode and Bhavaskar, 2011). Nunnally (1978) suggested that if this value is greater than 0.7 then it suggests good internal consistency. The Cronbach's alpha value for all 43 input items is found to be 0.803 and for 47 output items is 0.744 respectively. The above values indicate that the questionnaire is reliable. The Cronbach's alpha value for individual input and output items are shown below-

Cronbach‘s Alpha standardized items Cronbach‘s Alpha No. of items

.803 .811 43

Reliability Statistics

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Research Scholar, Kalinga University, Raipur