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ew 23(1):

Research Article

Automated System for forecasting and capacity management in BPO

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  • @ARTICLE{10.4108/ew.4460,
        author={Anuraag Anand and JB Simha and Shinu Abhi},
        title={Automated System for forecasting and capacity management in BPO},
        journal={EAI Endorsed Transactions on Energy Web},
        volume={10},
        number={1},
        publisher={EAI},
        journal_a={EW},
        year={2023},
        month={11},
        keywords={Data models, capacity modelling, extrapolation, knowledge models, prediction intervals, predictive validity, regression analysis, time series data},
        doi={10.4108/ew.4460}
    }
    
  • Anuraag Anand
    JB Simha
    Shinu Abhi
    Year: 2023
    Automated System for forecasting and capacity management in BPO
    EW
    EAI
    DOI: 10.4108/ew.4460
Anuraag Anand1,*, JB Simha1, Shinu Abhi1
  • 1: REVA University
*Contact email: anuraaganand26@gmail.com

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%.

Keywords
Data models, capacity modelling, extrapolation, knowledge models, prediction intervals, predictive validity, regression analysis, time series data
Received
2023-09-05
Accepted
2023-11-16
Published
2023-11-23
Publisher
EAI
http://dx.doi.org/10.4108/ew.4460

Copyright © 2023 Anand et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.

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