Resource Utilization Prediction in Decision-Intensive Business Processes

Simon Sperl, Giray Havur, Simon Steyskal, Cristina Cabanillas Macias, Axel Polleres, Alois Haselböck

Publikation: Beitrag in Buch/KonferenzbandBeitrag in Konferenzband


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 under and 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.
Titel des Sammelwerks7th International Symposium on Data-driven Process Discovery and Analysis (SIMPDA 2017)
Herausgeber*innen Paolo Ceravolo, Maurice Van Keulen, Kilian Stoffel
ErscheinungsortNeuchâtel, Switzerland
VerlagCEUR Workshop Proceedings
Seiten128 - 141
PublikationsstatusVeröffentlicht - 2017

Österreichische Systematik der Wissenschaftszweige (ÖFOS)

  • 101
  • 101015 Operations Research
  • 102001 Artificial Intelligence
  • 502050 Wirtschaftsinformatik
  • 101018 Statistik