| dc.description.abstract | Purposes: This paper explores how laborers’ perception, attitude, and self-efficacy of intelligent manufacturing in enterprise management are influenced when employers in technology industries introduce Intelligent Manufacturing Automation (IMA) and use an effective enterprise strategy management, implement intelligent manufacturing through automation wireless sensor networks and ant colony optimization algorithms, and carry out related education and training. It also assesses the differences among the laborers after IMS introduction by comparing the evaluation results of the maturity of intelligent factories, which can help employers to effectively replace the machinery and equipment in the bottom-level production line of intelligent factories.
Method: As intelligent Manufacturing Automation (IMA) employs wireless sensor networks and ant colony optimization algorithm, this study adopts a cross-sectional research design and the self-compiled and modified “Scale of Laborer’s Perception, Attitude and Self-efficacy in Intelligence Manufacturing”, with a review of the reliability and effectiveness of the research tool. In total, 330 laborers were selected in April 2019 by means of intentional sampling from two fields (Field A and Field B) of a technology factory with different degrees of IMA implementation, including different levels of working environment management automation, design automation, and factory intelligence. The research results were then analyzed by means of descriptive statistics, independent sample T test, one-way ANOVA, Pearson product difference correlation, and linear regression analysis. The purpose is to investigate the different impacts of various independent variables and IMA related education and training on laborers’ perception, attitude, self-efficacy in intelligent manufacturing, and automation management. SPSS 21 was used as the analysis tool.
Results: Introducing ant colony optimization algorithm in IMA can save the energy consumption of nodes in the network and prolong the life cycle of the network. Among the two fields, laborers in Field B, which has a relatively higher level of IMA, exhibit better performance in the perception, attitude, and self-efficacy of intelligent manufacturing. The fact that they have received education and training courses on IMA is the major correlation factor for their perception, attitude, and self-efficacy. On the contrary, laborers in Field A (control unit field) have poorer performance in their perception, attitude, and self-efficacy in intelligent manufacturing. The demographic impact factors are mainly gender, seniority, work unit, intelligent automation, the times and effects of learning in intelligent production line courses, intelligent automation experience, automation support, and working environment. By controlling the demographic impact factors, the differences in the perception of the laborers in the control unit field of IMA related education and training courses still have a significant correlation with their perception, attitude, and self-efficacy in intelligent manufacturing, which runs contrary to the case with the laborers in Field B who have received a higher degree of IMA related education and trainings.
Conclusion: The higher the implementation degree is for the IMA education and training courses among the laborers, the better are the laborers’ perception, attitude, and self-efficacy in intelligent manufacturing and learning effect of the courses, and the impact factors are simpler. The research results of this study can be used as a reference for labor adjustment in various industries after introducing intelligent factories and for the curriculum design of intelligent automation courses. In terms of practical contributions, it enables employers to have better cognition of the implementation of introducing intelligent automation in technology industries and further provides business operators with procedures for establishing intelligent automation enterprise Industry 4.0 and QCDS management procedures, which can enhance their competitiveness. | en_US |