Abstract
Systems for enterprise resource planning (ERP) are necessary to handle many aspects of human capital management (HCM) in companies. Among them, talent retention, efficient resource allocation, and overall organizational success all depend on the ability to forecast employee performance evaluations with accuracy. Traditional methods of performance reviews are not able to provide timely feedback, track performance in real-time. For example, to guarantee that the proper employees are assigned to the convenient task at the suitable moment, train and qualify them, and build evaluation systems to follow up their performance and an attempt to maintain the potential talents of workers. In this paper, we initiated to enable ERP application users to use the machine learning model to predict and enhance workers’ performance evaluation using real-time data directly from the ERP system. In particalr, we designed and enforced a prototype to define and apply Random Forest algorithm on Oracle EBS data. Based on measurements of accuracy the balanced dataset, the Random Forest algorithm enhanced theemployee performance evaluations with accuracy 98% rate.
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Without the assistance of Pythonista Egypt Commity, this work would not have been feasible. We are very grateful to the commity in providing us with the needed academic time.
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Youssri, A. et al. (2024). The Future ERP Systems: Improve Employee Performance Evaluation Using Machine Learning. In: Hassanien, A.E., Darwish, A., F. Tolba, M., Snasel, V. (eds) Proceedings of the 10th International Conference on Advanced Intelligent Systems and Informatics 2024. AISI 2024. Lecture Notes on Data Engineering and Communications Technologies, vol 220. Springer, Cham. https://doi.org/10.1007/978-3-031-71619-5_2
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