Leveraging Recurrent Neural Networks (RNN) for Workforce Planning and Optimization

This paper looks at the use of Recurrent Neural Networks; particularly LSTM and GRU in workforce management. The task of Workforce planning is an important process of confronting employee need and requirement along with the use of minimum workforce and cost. It is important here to appreciate that the conventional techniques of forecasting like the linear regression model do not capture the intricate dynamics of labor demand in relation to time. On the other hand, RNNs are effective for time-series data and can therefore forecast the demand of the workforce in the future based on the data obtained. In this research, it describes how to train and build an RNN model and then combine it with optimization algorithms for generating the best staffing schedules. Using real-world forc data, the model was compared and shown to make more accurate labour forecasts and increase utilisation by 13% over traditional methods. Also it minimized the cases of understaffing and overstaffing and also cut down on employee overtime. Hence, there is confidence that RNN-based systems are capable of improving operational flexibility, productivity through increased employee satisfaction, and contain cost benefits. The further work will be directed to the inclusion of more external conditions and investigation of the reinforcement learning for the improvement of the results. According to this study, application of RNNs is a sign of a shift to a new level of managing workforce through intelligent demand forecast and improving scheduling.