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Predictive Analytics for Business Processes in Service Management

Predictive Analytics for Business Processes in Service Management

Yurdaer N. Doganata, Geetika T. Lakshmanan, Merve Unuvar
Copyright: © 2017 |Pages: 38
ISBN13: 9781522517597|ISBN10: 1522517596|EISBN13: 9781522517603
DOI: 10.4018/978-1-5225-1759-7.ch114
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MLA

Doganata, Yurdaer N., et al. "Predictive Analytics for Business Processes in Service Management." Artificial Intelligence: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2017, pp. 2759-2796. https://doi.org/10.4018/978-1-5225-1759-7.ch114

APA

Doganata, Y. N., Lakshmanan, G. T., & Unuvar, M. (2017). Predictive Analytics for Business Processes in Service Management. In I. Management Association (Ed.), Artificial Intelligence: Concepts, Methodologies, Tools, and Applications (pp. 2759-2796). IGI Global. https://doi.org/10.4018/978-1-5225-1759-7.ch114

Chicago

Doganata, Yurdaer N., Geetika T. Lakshmanan, and Merve Unuvar. "Predictive Analytics for Business Processes in Service Management." In Artificial Intelligence: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 2759-2796. Hershey, PA: IGI Global, 2017. https://doi.org/10.4018/978-1-5225-1759-7.ch114

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Abstract

Underlying business processes in service management are people intensive and collaborative by nature. We are observing an emerging trend in the service management applications, moving away from rigid process orchestration to leveraging collaboration. Such solutions allow staffers to define their own customized, ad-hoc step flow consisting of the sequence of the activities necessary to handle a service component. These ad-hoc steps introduce uncertainty to the successful completion of a service request. When there is uncertainty, predictive guidance about future outcomes could provide value to the workers handling a time-sensitive service delivery component. Predicting the future outcomes using machine-learning techniques requires effective representation of the process execution traces. This is challenging when process model includes parallel execution flows or repeated executions of some activities. In this chapter, we describe algorithms for training machine learning models when the execution paths include parallel flows and when some activities are repeatedly executed.

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