Automated System for forecasting and capacity management in BPO

Author:

Anand Anuraag,Simha JB,Abhi Shinu

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%.

Publisher

European Alliance for Innovation n.o.

Subject

Marketing,Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment

Reference19 articles.

1. H. B. Review, "how-to-choose-the-right-forecasting-technique," July 1971. [Online]. Available: https://hbr.org/1971/07/how-to-choose-the-right-forecasting-technique.

2. E. J. K. S. L. B. C. Jessica Lin, "A Symbolic Representation of Time Series, with Implications for Streaming Algorithms," 10.1145/882082.882086, 2003.

3. V. Kumar, "Predictive Analytics: A Review of Trends and Techniques," International Journal of Computer Applications 182(1):31-37, 2018.

4. N. A. T. A. C. Patrick Courtney, "Algorithmic modelling for performance evaluation," 1997. Paper-1312018.pdf

5. S. Ansari, "Pattern Recognition | Introduction," 23 august 2022. [Online]. Available: https://www.geeksforgeeks.org/pattern-recognition-introduction/.

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3