This study selected two labor-intensive processes in harsh environments among domestic food production processes. It analyzed their improvement effectiveness using 3-dimensional (3D) simulation. The selected processes were the “frozen storage source transfer and dismantling process” (Case 1) and the “heavily loaded box transfer process” (Case 2). The layout, process sequence, man-hours, and output of each process were measured during a visit to a real food manufacturing factory. Based on the data measured, the 3D simulation model was visually analyzed to evaluate the operational processes. The number of workers, work rate, and throughput were also used as comparison and verification indicators before and after the improvement. The throughput of Case 1 and Case 2 increased by 44.8% and 69.7%, respectively, compared to the previous one, while the utilization rate showed high values despite the decrease, confirming that the actual selected process alone is a high-fatigue and high-risk process for workers. As a result of this study, it was determined that 3D simulation can provide a visual comparison to assess whether the actual process improvement has been accurately designed and implemented. Additionally, it was confirmed that preliminary verification of the process improvement is achievable.

This paper presents a log-transformed model-based performance analysis system for analyzing and improving manufacturing performance of the smart factory in the display business. Two years of data related to traditional manufacturing performance such as Cycle-time, WIP(Work-In-Process), and Throughput were investigated from the smart factory that producing the display for this research. We assessed manufacturing competitiveness based on how the operational level of automation affects improvements in manufacturing performances. We analyzed functional relationships between the indicators were derived using logtransformed regression analysis how the manufacturing performance indicators change according to the operational level of smart factory automation. As a result, we knew that the 170K production, which was planned capacity in the line design phase, achieved by running an automation level of only 59%. Based on this research, we suggest building an autopoietic optimize performance model to improving manufacturing competitiveness of smart manufacturing.

In an automated industry PLC plays a central role to control the automation system. Therefore, fault free operation of PLC controlled automation system is essential in order to maximize a firm’s productivity. A prior test of control system is a practical way to check fault operations, but it is a time consuming job and can not check all possible fault operation. A formal verification of PLC program could be a best way to check all possible fault situation. Tracing the history of the study on formal verification, we found three problems, the first is that a formal representation of PLC control system is incomplete, the second is a state explosion problem and the third is that the verification result is difficult to use for the correction of control program. In this paper, we propose a transformation method to reproduce the control system correctly in formal model and efficient procedure to verify and correct the control program using verification result. To demonstrate the proposed method, we provided a suitable case study of an automation system.