A Study of the Factors Influencing the Insufficient Workforce proportion in the Manufacturing Industry.

• Main Authors: Shu-Fang Lee, 李淑芳
• Other Authors: Ying-Fang Huang
• Format: Others
• Language: zh-TW
• Published: 2011
• Online Access: http://ndltd.ncl.edu.tw/handle/83736490041546966980

碩士 === 國立高雄應用科技大學 === 工業工程與管理系 === 99 === This study utilizes data from 2004-2010, taken from the Directorate-General of Budgeting, Accounting and Statistics. Data included employee statistics from both private and public enterprises in the product and service industries, and covered the provinces of Taiwan, Taipei city and Kaohsiung city. This study utilized methods such as descriptive statistics, correlation analysis , grey relational generating, grey relational analysis, one-way analysis of variance, and Kruskal-Wallia one-way analysis of variance ranks to categorize the data into six main factors: “Hires the employee state”, “employee turnover state”, “labor indicator”, “salary indicator”, “danger indicator” and “economic indicator”. This study then analyzed and explored the influential factors regarding the insufficient workforce proportion in the manufacturing industry and its division. The results of the correlation analysis and grey relational analysis show that “labor indicator” is the most influential factor in regards to the insufficient workforce proportion in each category of the manufacturing industry. Two analysis methods revealed that a total of six categories and 12 manufacturing division show a linear relationship with the insufficient workforce proportion, namely: “08-10 division”, “19-20 division”, “non-metallic mineral products manufacturing”, “26-28 division”, “furniture manufacturing” and “other manufacturing”. Factors that were found to be influential in the grey relational analysis, but not in correlation analysis methods, may indicate a non-linear relationship with the insufficient workforce proportion. The two above mentioned analysis methods also indicated that “previous year disabling injury frequency” and “economic growth rate” were both influential factors in the insufficient workforce proportion of manufacturing industry. The “economic growth rate” is affected by the overall environment and cannot be controlled easily, but “previous year disabling injury frequency” can be decreased through the government guidance work safety idea and the manufacturing industry improvement working conditions security, which would help to decrease the workforce proportion. To further understand the relationship between “labor indicator”, “salary indicator”, “danger indicator” and “economic growth indicator” and the insufficient workforce proportion in the manufacturing industry, this study utilized a one-way analysis of variance and a Kruskal-Wallis test to examine the seven related factors. The results indicated that “overtime wage”, “economic growth rate”, “average work hours” and “overtime hours” all with the insufficient workforce proportion have significant correlation.