Automated System for forecasting and capacity management in BPO

Anuraag Anand; JB Simha; Shinu Abhi

doi:10.4108/ew.4460

Authors

Anuraag Anand REVA University
JB Simha REVA University
Shinu Abhi REVA University

DOI:

https://doi.org/10.4108/ew.4460

Keywords:

Data models, capacity modelling, extrapolation, knowledge models, prediction intervals, predictive validity, regression analysis, time series data

Abstract

In the virtual world, every decision made by executives today need forecasting. Sound forecasting of demand and variations are no longer an extravagance but a necessity, since Operations in the organizations have to deal with the seasonality, sudden changes in capacity management, cost-cutting strategies of the competition, and enormous dynamics of the economy.

This paper details the development of a Forecasting and Capacity Planning model to empower operations to consistently forecast incoming volume for scheduling/rostering. A combination of past process-specific data, algorithmic forecasting, Subject Matter Expert (SME) inputs, and modelling results in a forecast with a daily accuracy of up to 85% per month out and approximately 95%-98% per week. The tool leverages the generated forecast to envisage capacity and resource planning. This Capacity Planning tool gives the capacity requirement for the forecasted volume, scheduling, and staffing. The tool has been deployed across 150+ client area. POC (Proof of Concepts) was done across all domains to test the tool and as expected the tools is generating the forecast and schedule with the accuracy of 96.77%.

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