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A framework for combined bayesian analysis and optimization for services delivery

机译：联合贝叶斯分析和优化服务交付框架

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摘要

One of the key challenges facing the professional services delivery business is the issue of optimally balancing competing demands from multiple, concurrent engagements on a limited supply of skill resources. In this paper, we present a framework for combining causal Bayesian analysis and optimization to address this challenge. Our framework integrates the identification and modeling of the impact of various staffing factors on the delivery quality of individual engagements, and the optimization of the collective adjustments of these staffing factors, to maximize overall delivery quality for a pool of engagements. We describe a prototype system built using this framework and actual services delivery data from IBM's IT consulting business. System evaluation under realistic scenarios constructed using historical delivery records provides encouraging evidence that this framework can lead to significant delivery quality improvements. These initial results further open up exciting opportunities of additional future work in this area, including the integration of temporal relationships for causal learning and multi-period optimization to address more complex business scenarios.
机译：一个面向专业服务交付业务的主要挑战是最佳平衡的技能有限的资源供给从多个并发的交战竞争性需求的问题。在本文中，我们提出了一个结合因果贝叶斯分析和优化来解决这一挑战的框架。我们的框架整合了各种人员配置因素对个人参与的交付质量的影响，以及优化这些人员配置因素的集体调整，以最大限度地为一系列订购池的整体交付质量。我们描述了使用此框架和IBM IT咨询业务的实际服务提供数据构建的原型系统。使用历史交付记录构建的现实情景下的系统评估提供了令人鼓舞的证据，即该框架可能会导致显着的交付质量改进。这些初步结果进一步开辟了这一领域的额外未来工作的令人兴奋的机会，包括对因果学习和多时期优化的时间关系的整合，以解决更复杂的业务场景。

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