Productivity Forecasting of Employee Performance Using Machine Learning & Adaptive Neuro-Fuzzy Inference System

Subject Areas : Artificial Intelligence

Abdalrhman M. Bormah ^{1 *} , Osman Taylan ²

1 - Department of Industrial Engineering, King Abdul Aziz University, Jeddah, Saudi Arabia
2 - Department of Industrial Engineering, King Abdul Aziz University, Jeddah, Saudi Arabia

Received: 2023-06-06 Accepted : 2025-04-19 Published : 2025-06-20

Keywords: Employees’ Productivity, Artificial Neural Networks (ANNs), Adaptive Neuro-Fuzzy Inference System (ANFIS), Regression Tree, Support Vector Machine, Gaussian Processes Regression, Employees’ Performance,

Abstract :

Employee productivity has been considered a crucial factor that threatens/diminishes company revenues and growth. Investigating the productivity of a garment company's workforce has been occupying this research's focus. Moreover, the garments industry is one of the most labor-intensive industries, and studying the actual productivity of employees is an important source for decision-making.  Additionally, the productivity of the manpower is associated with influencers such as workload, incentives, overtime, and the capacity of the manpower versus task requirements. Based on theories and experiments, it has been found that employees’ productivity could be affected by those mentioned factors and other variables such as the convenience of the surrounding environments, and workplace layout. Considering the power of artificial intelligence (AI) and machine learning (ML) techniques, starting by examining a set of regression algorithms, linear regression (LR), Regression Trees (RT), Support Vector Machines (SVM), Gaussian Processes Regression (GPR), and Ensemble of Trees Regression (ET) methods are used to predict the employees’ productivity. Also, artificial neural networks (ANNs) are employed with a couple of training algorithms which are Levenberg-Marquardt (LM) & Bayesian Regularization (BR). The last application is the adaptive neuro-fuzzy inference system (ANFIS) via Hybrid and Backpropagation optimization. All the above models are studied to configure the impact of six independent predictors on productivity. In conclusion, medium regression trees give the RMSE of 0.10926 for training, and R-squared value of 0.69, Exponential Gaussian processes regression 0.10627 for RMSE for the training and 0.6 for R-squared respectively. The ANNs of Bayesian Regularization produced a value of 0.120476814 for RMSE and 0.72248 for R-Squared of the highest coefficient of determination

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