An Empirical Study of Method Optimization and Productivity Improvement in Labor-Intensive Manufacturing Using Historical Data
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Paul, Karunamoye
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Ip, Ryan
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Auckland University of Technology
Abstract
This research presents an empirical investigation of production method optimization and productivity improvement in a labor-intensive manufacturing environment using historical production data in the garment industry. In conventional textile manufacturing systems, operational inefficiencies frequently arise from inaccurate estimation of the Standard Minute Value (SMV), suboptimal workflow configuration, excessive manpower deployment, and the absence of systematic analytical evaluation of production performance. These limitations contribute to reduced production output, elevated unit manufacturing cost, and significant financial loss. The present study demonstrates how the enhancement in productivity and profitability of engineering-level process optimization without major capital investment can be evaluated statistically, grounded in real industrial production data.
Historical production records were collected from Esquire Knit Composite Ltd., focusing on matched blanket manufacturing data from two consecutive production years. Comparative statistical and graphical analyses were conducted to evaluate key operational indicators, including SMV, manpower utilization, working hours, daily and total output, production efficiency, and financial performance under fixed Free on Board (FOB) pricing. Root-cause diagnosis of productivity loss in the initial production year was performed through detailed observation of Enterprise Resource Planning (ERP) production records and structured fishbone (cause-and-effect) analysis to identify deficiencies in production method, machine configuration, workflow organization, and technical coordination.
Based on the identified causes, coordinated engineering interventions were implemented, including enhanced pre-production planning, multidisciplinary technical and mechanical consultation, machine layout rearrangement, and refinement of sewing production methodology. Empirical results demonstrate that excessive SMV in the first production year significantly constrained labor productivity and generated substantial operational loss. Following systematic method optimization, SMV was reduced by 52.31%, accompanied by reductions in manpower requirement and working hours, while production output and operational efficiency increased markedly. These improvements transformed a loss-incurring manufacturing system into a profitable production outcome in 2024 without major technological investment.
To validate these findings beyond fixed average values, a Monte Carlo simulation was developed to model operation-level time variability, error-prone conditions, and rework probability under both production methods. Across 10,000 simulated production outcomes per method, the improved 2024 process consistently produced a more favorable distribution of cost and profit per piece than the conventional 2023 process, confirming that the observed productivity gains remain robust under realistic operational uncertainty rather than holding only under idealized average conditions. Statistical evidence further confirms that productivity recovery in labor-intensive manufacturing is primarily driven by production method refinement and SMV optimization rather than increased labor intensity or overtime extension.
Overall, the study contributes a technically grounded and empirically validated framework for productivity and profitability enhancement through historical data analysis, statistical evaluation, simulation-based validation, and engineering process optimization. The proposed methodology is particularly applicable to labor-intensive manufacturing industries seeking sustainable operational improvement under resource-constrained conditions.