Predicting Fluid Work Demand in Service Organizations Using AI Techniques

Prediction is about making claims on future events based on past information and the current state. Predicting workforce demand for the future can help service organizations adjust their resources and reach their goals of cost saving and enhanced efficiency. In this paper, a use case for a telecom service organization is presented and a framework for predicting workforce demand using neural networks is provided. The experiments were performed with real-world data, and the results were compared against other popular techniques such as linear regression and also moving average which served as a simulation of the technique historically applied manually in the organization. The results show that the accuracy of prediction is improved with the use of neural networks. The technique is being built into a tool that is being tested by the partner telecom organization.

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