SIMOD: Automated discovery of business process simulation models

Abstract

Business process simulation is a technique that enables analysts to estimate the impact of changes to a business process with respect to time and cost-related performance measures. Specifically, business process simulation allows analysts to answer questions such as “what would be the cycle time of a process if 10% of the resources become unavailable for an extended period of time, or if we automate one of the activities in the process?” The starting point of business process simulation is a model capturing the possible sequences of activities of a process, the distribution of processing times of each activity, the resources available to perform each activity in a process, and other parameters that capture the workload and behavior of resources. Designing simulation models by hand is overly time-consuming and error-prone. To address this shortcoming, several methods have been proposed to automatically discover business process simulation models from event logs extracted from enterprise information systems. This paper introduces SIMOD, a Python package to automatically discover business process simulation models from event logs. SIMOD applies a range of statistical and process mining techniques to discover a process model from an event log and to enhance it with simulation parameters.