Deep Neural Network Approach for Predicting the Productivity of Garment Employees

The garment industry is one of the most dominating industries in this era of industrial globalization. It is a highly labor-intensive industry that requires a large number of human resources to produce its goods and fill up the global demand for garment products. Because of the dependency on human labor, the production of a garment company comprehensively relies on the productivity of the employees who are working in different departments of the company. A common problem in this industry is that the actual productivity of the garment employees sometimes does not meet the targeted productivity that was set for them by the authorities to meet the production goals in due time. When the productivity gap occurs, the company faces a huge loss in production. This study aims to solve this problem by predicting the actual productivity of the employees. To achieve this aim, a Deep Neural Network (DNN) model has been proposed to predict the actual productivity of the employees. The experimental results of this study have shown that the proposed model yields a promising prediction performance with a minimal Mean Absolute Error (=0.086) which is less than the baseline performance error (=0.15). Such prediction performance can indisputably help the manufacturers to set an accurate target, minimize the production loss and maximize the profit.