This research aims to enhance plant productivity by integrating two critical aspects: Overall Equipment Effectiveness (OEE) loss prediction and occupational ergonomics. OEE is a key performance indicator that measures the effectiveness of manufacturing processes by accounting for availability; performance; and quality losses. By predicting and addressing OEE losses; the study seeks to optimize production efficiency and reduce downtime.
In conjunction with OEE; the research emphasizes the significance of occupational ergonomics; which focuses on designing workspaces and tasks to minimize physical strain on workers. Ergonomic considerations play a vital role in preventing musculoskeletal injuries; fatigue; and discomfort; ultimately improving workforce well-being and performance.
The study utilizes data-driven approaches and machine learning techniques to analyze historical OEE data and ergonomic factors. By correlating these two domains; the research aims to identify potential relationships and insights that could contribute to informed decision-making and process improvement strategies.
The findings of this research have practical implications for industrial managers; engineers; and safety professionals. By optimizing OEE and ergonomics; plant productivity can be significantly enhanced; leading to increased production output; reduced operational costs; and a safer working environment. Moreover; the integration of data analytics and ergonomic considerations may pave the way for more efficient and sustainable manufacturing practices in various industries.