Machine Learning Models for Analysing and Predicting Team Productivity in Garment Manufacturing
DOI:
https://doi.org/10.48047/vzt5rv45Keywords:
Keywords: Team Productivity, Garment Manufacturing, Machine Learning, Prediction Models, Workforce AnalysisAbstract
The garment manufacturing industry contributes to over 6% of global industrial employment and generates $1.5 trillion in annual revenue. However, inefficiencies in workforce productivity lead to a 20–30% loss in overall production efficiency, affecting profit margins and delivery timelines. Traditionally, team productivity is monitored manually, relying on supervisor assessments and historical averages, which are prone to inconsistencies, delays, and human bias. These manual tracking systems fail to adapt to real-time production changes, resulting in inaccurate efficiency evaluations. To address these challenges, we propose a machine learning-driven productivity prediction framework specifically designed for garment manufacturing, using the Garment dataset with "actual productivity" as the target variable. The methodology includes preprocessing techniques
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