(untitled) (International Journal of Engineering Materials and Manufacture, 2023)
The manufacturing industry is pushing forward with the smart machineries, linked networks, and a smart environment to enhance Big Data’s operations – gaining intelligence and actionable real-time insights for higher efficient and smart production. However, in many parts of the manufacturing industry, manual monitoring system is still the usual practice, especially the long-established plants that consist of legacy machines. The monitoring processes of the lines in Top Glove F31 such as recording the lines’ downtime duration, checking the source of the stoppage, transferring the data into spreadsheet, and analyzing the downtime of the lines are all being carried out by human operators. There is a high tendency for the workers to overlook the maintenance of the system because of this work practice, and it will contribute to higher unplanned downtime of the lines. Thus, this project proposes to design an automatic downtime monitoring system that consists of sensors, PLC’s programming, and IoT technologies that can decrease the dependency on workers and not prone to human mistakes to reduce the unplanned downtime of the lines and increase the efficiency of the system.