The Effect Of Packing Machine Automation On Operator Productivity With Moderation Of Technical Competence And Workload Perception: Systematic Literature Review (SLR) With Bibliometrics - Mohammad Kartika; Madziatul Churiyah; Budi Eko Soetjipto (PT. Maju Malaqbi Makkarana, 2026)
This study aims to analyze the influence of packing machine automation on operator productivity in the facial tissue industry by considering the role of moderation, technical competence and operator workload perception. The method used is Systematic Literature Review (SLR) by reviewing scientific articles published in the 2020–2026 range from reputable databases such as Scopus. The selection process was carried out using the PRISMA approach through the identification, screening, eligibility, and inclusion stages, resulting in a number of articles relevant to the topics of industrial automation, labor productivity, technical competence, and workload. The results of the study show that the implementation of packing machine automation in general has a positive impact on increasing operator productivity through time efficiency, quality consistency, and reduction of manual errors. Nevertheless, the effectiveness of automation is highly dependent on the level of technical competence of the operator, especially in the operation, maintenance and troubleshooting of the machine. In addition, workload perception has also been shown to moderate the relationship, where automation can lower physical workloads but potentially increase mental workloads due to the demands of automated system supervision. Other findings suggest that an imbalance between automation levels and human resource readiness can hinder productivity optimization. Conceptually, this study confirms that the relationship between packing machine automation and operator productivity is not linear, but is influenced by individual and psychological factors. The practical implications of this study are the importance of technical competency-based training as well as adaptive workload management in supporting the successful implementation of automation in the manufacturing industry. This research contributes to the development of an integrative model that connects technology, people, and operational performance in the context of the tissue processing industry.