Interpretable garment workers' productivity prediction in Bangladesh using machine learning algorithms and explainable AI

Abstract

Bangladesh's garment industry is widely recognized and plays a significant role in the current global market. The nation's per capita income and citizens' living standards have risen significantly with the noteworthy hard work performed by the employees in this industry. The garment sector is more efficient once the target production can be achieved without any difficulties. But a frequent issue that comprises within this industry is, often the actual garment producing productivity of the people working there do not reach the previously determined target-productivity. The business suffers a significant loss when the productivity gap appears in this process. This approach seeks to address this issue by prediction of the actual productivity of the workers. To attain this goal, a machine learning approach is suggested for the productivity prediction of the employees, after experimentation with five machine learning models. The proposed approach displays a reassuring level of prediction accuracy, with a minimalist MAE (Mean Absolute Error) of 0.072, which is less than the existing Deep Learning model with a MAE of 0.086. This indicates that, application of this process can play a vital role in setting an accurate target production which might lead to more profit and production in the sector. Also, this work contains an explainable AI technique named SHAP for interpreting the model in order to see further information within it.