Discovery, simulation, and optimization of business processes with differentiated resources

Abstract

Business process simulation is a versatile technique to predict the impact of one or more changes on the performance of a process. Process simulation is often used to identify sets of changes that optimize one or more performance measures. Mainstream approaches to process simulation suffer from various limitations, some stemming from the fact that they treat resources as undifferentiated entities grouped into resource pools, and then assuming that all resources in a pool have the same performance and share the same availability calendars. Previous studies have acknowledged these assumptions, without quantifying their impact on simulation model accuracy. This article addresses this gap in the context of simulation models automatically discovered from event logs. Specifically, the contribution of the article is three-fold. First, the article proposes a simulation approach, wherein each resource is treated as an individual entity, with its own performance and availability calendar. Second, it proposes a method for discovering simulation models with differentiated performance and availability, starting from an event log of a business process. Third, it proposes a method to optimize the resource availability calendars in order to minimize resource cost while also minimizing cycle times. An empirical evaluation shows that simulation models with differentiated resources more closely replicate the distributions of cycle times and the work rhythm in a process than models with undifferentiated resources, and that iteratively optimizing resource allocations in conjunction with resource calendars leads to superior cost–time tradeoffs with respect to optimizing these allocations and calendars separately.