AI Powered Forecasting for Workforce Management

Abstract

In this paper, we present our approach to using time series forecasting for managing resource allocation for one of the Social Media UPI. With the rapid increase in online payments, the business is facing challenges in resource allocation across three vendors to monitor its payment requests categorized under seven hierarchical processes labelled as Towers. Each of these Towers is further divided into various sub-processes called Products and then Alert Queues. Since the workforce allocation depends upon the incoming volume requests for these processes, it is vital to have a proper volume forecasting system to plan for the optimal resource allocation in advance. We forecasted volumes for all Products and Alert Queues across all the Towers and categorized them under three different risk buckets depending upon the Mean Absolute Percentage Error (MAPE) observed on test data. Using the forecasted volumes and the risk category the Product falls under, capacity planning for future dates is made easier, and the client was able to reduce its resources by 26%.