Automated System for forecasting and capacity management in BPO

Anuraag Anand, JB Simha, Shinu Abhi
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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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业务流程外包预测和能力管理自动化系统
在虚拟世界中,企业高管的每项决策都需要进行预测。对需求和变化进行合理预测不再是奢望,而是必须的,因为企业的运营部门必须应对季节性、产能管理的突然变化、竞争对手的成本削减战略以及经济的巨大动态变化。 本文详细介绍了预测和产能规划模型的开发过程,该模型可帮助运营部门持续预测进货量,以便进行调度/排期。将过去的特定流程数据、算法预测、主题专家 (SME) 输入和建模相结合,得出的每日预测准确率每月高达 85%,每周约为 95%-98%。该工具利用生成的预测来设想容量和资源规划。该产能规划工具可提供预测产量、调度和人员配置所需的产能。该工具已部署到 150 多个客户区域。在所有领域都进行了 POC(概念验证)以测试该工具,正如预期的那样,该工具生成预测和计划的准确率高达 96.77%。
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