Abstract
Companies strive to be more efficient and constantly increase manufacturing productivity to stay competitive. The Overall Equipment Effectiveness (OEE) is a relevant performance measurement that companies use to monitor efficiency, quality, costs, and the capacity of their production lines. A case study in a pharmaceutical company was conducted to see if additional methods alongside the OEE could help improve the production planning, capacity utilization, and output of a packaging manufacturing line regarding production speed, demand size, and cost per item. Therefore, the study utilized theoretical concepts from the literature with empirical data to develop a simulation model for this specific manufacturing system. A time study and a discrete event simulation were used, and the solution showed acceptable and coherent to real numbers. In addition to bottleneck identification, the simulation enabled the estimation of an optimal number of operators and the gains achieved by implementing changes in the manufacturing processes. It was concluded that the simulation model could help to improve the production planning and, subsequently, the capacity utilization and output of the manufacturing line.
Original language | English |
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Title of host publication | 2023 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) |
Publisher | IEEE |
Publication date | 2023 |
Pages | 0198-0202 |
ISBN (Electronic) | 979-8-3503-2315-3 |
DOIs | |
Publication status | Published - 2023 |
Event | IEEE International Conference on Industrial Engineering and Engineering Management - Marina Bay Sands, Singapore, Singapore Duration: 18 Dec 2023 → 21 Dec 2023 |
Conference
Conference | IEEE International Conference on Industrial Engineering and Engineering Management |
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Location | Marina Bay Sands |
Country/Territory | Singapore |
City | Singapore |
Period | 18/12/2023 → 21/12/2023 |
Keywords
- Capacity utilization
- Discrete event simulation
- Overall equipment effectiveness (OEE)
- Pharmaceutical manufacturing
- Process improvement
- Production planning