Optimizing manpower utilization in the machining line of an automotive parts manufacturing company
Submitted: 2025-11-16
|Accepted: 2026-02-15
|Published: 2026-05-05
Copyright (c) 2026 Bennirey Advincula, Aaron, Rizalyn Falcon, Marianne Fruponga, Heidee Mamerto, Pierre Parungan, Jhelo Manaog, John Salonga, Alec Santos, Jemalyn Sapida
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
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Keywords:
optimization, manpower utilization, automotive parts, manufacturing, line balancing, time and motion study
Supporting agencies:
Abstract:
Optimizing manpower utilization is crucial for enhancing efficiency and productivity in a labor-intensive manufacturing setting. A machining line in an automotive parts manufacturing company demonstrated high dependency on labor, with minimal automation in place. Additionally, workload imbalances were observed, where some workers handled multiple machines while others managed fewer tasks. These inefficiencies resulted in delays, inconsistent cycle times, and lower productivity. With this, this study aims to focus on the optimization of manpower utilization in the machining line. Time and motion study and line balancing were performed to validate the cycle time for each workstation, examine workload distribution, and calculate the required number of workstations. The results revealed inefficiencies due to variable worker pacing, unoptimized travel, and lack of proper workload distribution that impede overall production flows and increase processing time variations. Based on the analysis, it was determined that the workstations and workers can be reduced from 3 to 2. A set of possible alternatives were drawn to find the most optimal man-to-machine allocation with 2 workers and 7 machines. The alternative with the most balanced workload distribution, minimized worker idle time, and enhanced manpower utilization was recommended for the machining line. The study highlights the importance of data-driven optimization and use of industrial engineering tools in achieving sustainable improvement.
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