Resource Utilization Prediction in Decision-Intensive Business Processes. Sperl, S., Havur, G., Steyskal, S., Cabanillas, C., Polleres, A., & Haselböck, A. In Ceravolo, P., van Keulen, M., & Stoffel, K., editors, Proceedings of the 7th International Symposium on Data-driven Process Discovery and Analysis (SIMPDA 2017), volume 2016, of CEUR Workshop Proceedings, pages 128–141, Neuchâtel, Switzerland, December, 2017.
Resource Utilization Prediction in Decision-Intensive Business Processes [pdf]Paper  abstract   bibtex   
An appropriate resource utilization is crucial for organizations in order to avoid, among other things, unnecessary costs (e.g. when resources are under-utilized) and too long execution times (e.g. due to excessive workloads, i.e. resource over-utilization). However, traditional process control and risk measurement approaches do not address resource utilization in processes. We studied an often-encountered industry case for providing large-scale technical infrastructure which requires rigorous testing for the systems deployed and identified the need of projecting resource utilization as a means for measuring the risk of resource underand over-utilization. Consequently, this paper presents a novel predictive model for resource utilization in decision-intensive processes, present in many domains. In particular, we predict the utilization of resources for a desired period of time given a decision-intensive business process that may include nested loops, and historical data (i.e. order and duration of past activity executions, resource profiles and their experience etc.). We have applied our method using a real business process with multiple instances and presented the outcome.

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