A Likert scale questionnaire is used to define issues that affect and lead to supply chain deficiency. The data is shown to be normally distributed with a significance of each issue
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exceeding 0.05 (See Appendix B). A t-test is used to determine the significance of the differences between the mean of the sample and a specified value (Colman and Pulford, 2006). Therefore it was used to measure at what level the respondents considered each issue as one leading to existing deficiencies. The t-test demonstrates whether all the local samples belong to the same population. One sample t-test has been conducted three times to compare the mean of each issue and produced test value of 3.5, 3.0 and 2.0. The outcome of each test is indicated in Appendix B. The first test value was issued to indicate a level above the (Neutral) and up to the (Agree) level on Likert’s scale. Elements in this range can be considered as leading issues for supply chain deficiency while other elements that had values entirely below 0 and a significance value less than 0.05 are considered as non-leading issues. The second test value was used to indicate the (Neutral) level on Likert’s scale. Elements that match this level can hardly be classified by respondents as leading or non-leading to supply chain deficiency. A third t-test was conducted using 2.0 as a test value. The test value represents the (Disagree) level in the Likert scale.
A t-test was conducted with a value 3.5 which represents the respondents’ agreement on the questioned issue. The analysis indicates that diversity of procurement and high production costs had significance levels that did not exceed 0.05. Based on that, the respondents agree to consider these issues as interfering issues that cause supply chain deficiency in the textile industry. The outcome shows other issues that did not interfere to cause supply chain deficiency. These issues are represented in supply chain design and integration, IT infrastructure and non-justified IT investment.
Another t-test was conducted with a test value of 3.0 which indicates the neutral level indicated by the respondents. The outcome of this test represents issues that have not been classified by respondents as leading or non-leading issues. The t-test outcome indicates a number of issues, with significance levels that do not exceed 0.05 and have not been classified by respondents as leading or non-leading issues to supply chain deficiency. These issues are represented in issues such as limited market share, inaccurate demand forecasting and long delivery time.
The third t-test with a value of 2.0, representing the disagreement of respondents, was conducted. It is proved, with a significance level less than 0.001, that inventory cost has not been considered by respondents as a leading issue to supply chain deficiency.
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4.11.2.1 Mass production versus mass customisation strategy
My survey was conducted to examine different strategies of supply chains. Mass production, mass customisation or mixed approaches were included. In order to estimate the difference in difficulties faced due to mass customisation or mass production adoption, an independent sample t-test was conducted. The outcome of the test is indicated in Appendix B. The group statistics indicate that on average many of the deficiencies faced such as diversity of procurement, long delivery, and inaccurate demand forecasting are higher in mass customisation firms than mass production firms. A limited IT infrastructure has a higher average in a mass production firm than in mass customisation firms. The outcome of the test indicated that equal variances are assumed since the result of a Levene’s test for equality of variances is non-significant (significance results exceed 0.05 levels). The significance of a t-test for means equality indicates that there is no difference between mass production and mass customisation firms in faced deficiencies except for diversity of procurement and instability of material prices which had a significance of 0.015 value. While the average means of the procurement issue is 2.83 for mass production firms, it reaches more than 4.25 for mass customisation firms. This illustrates that means are significantly different in mass customisation firms than in mass production firms. Due to demand uncertainty in mass customised products, procurement problems are more common and important in producing tailored products rather than standard ones.
4.11.2.2 Public firms versus private firms
As mentioned before, public and private firms are included in the selected sample. In order to define problems that face each sector, a t-test is used to conduct a mean comparison of each issue from both sectors. The outcome of the independent sample t-test is included in Appendix B. Levene's Test for Equality of Variances indicated non-significance results, which means that the assumption of equal variance between public and private firms is valid. The analysis of the outcome does not indicate significant mean differences between the public and private sectors for any of the issues. Although the average mean of issues, such as poor supply chain design, un-integrated supply chain platform, inventory cost, production cost and un-justified IT investments, are much higher in public firms than in private firms, theses differences cannot be considered a significant level of mean differences which exceeds 0.05.
125 4.11.2.3 Confidence Interval
T-tests conducted for this research have adopted a 95% level of confidence. Confidence interval generates a lower and upper limit for the mean. The interval estimate gives an indication of how much uncertainty there is in the outcome. (Snedecor and Cochran, 1989) Concerning the significance level, an acceptable level in the study is 0.05 which means that the researcher is willing to accept that 5 times out of 100 in the sample have occurred by chance. The 95% level of confidence is commonly used in academic research.
4.11.2.4 Hypotheses testing
The outcome of t-tests can be concluded to accept or reject the set of hypotheses outlined in Tables 22 and 24. Table 25 demonstrates the results of hypotheses testing.