Business process simulation is an approach to evaluate business process changes prior to implementation. Existing methods in this field primarily support tactical decision-making, where simulations start from an empty state and aim to estimate the long-term effects of process changes. A complementary usecase is operational decision-making, where the goal is to forecast short-term performance based on ongoing cases and to analyze the impact of temporary disruptions, such as demand spikes and shortfalls in available resources. An approach to tackle this usecase is to run a long-term simulation up to a point where the workload is similar to the current one (warm-up), and measure performance thereon. However, this approach does not consider the current state of ongoing cases and resources in the process. This paper studies an alternative approach that initializes the simulation from a representation of the current state derived from an event log of ongoing cases. The paper addresses two challenges in operationalizing this approach: (1) Given a simulation model, what information is needed so that a simulation run can start from the current state of cases and resources? (2) How can the current state of a process be derived from an event $\log$ ? The resulting short-term simulation approach is embodied in a simulation engine that takes as input a simulation model and a log of ongoing cases, and simulates cases for a given time horizon. An experimental evaluation shows that this approach yields more accurate short-term performance forecasts than long-term simulations with warm-up period, particularly in the presence of concept drift or bursty performance patterns.

Waiting times in a business process often arise when a case transitions from one activity to another. Accordingly, analyzing the causes of waiting times in activity transitions can help analysts identify opportunities for reducing the cycle time of a process. This paper proposes a process mining approach to decompose observed waiting times in each activity transition into multiple direct causes and to analyze the impact of each identified cause on the process cycle time efficiency. The approach is implemented as a software tool called Kronos that process analysts can use to upload event logs and obtain analysis results of waiting time causes. The proposed approach was empirically evaluated using synthetic event logs to verify its ability to discover different direct causes of waiting times. The applicability of the approach is demonstrated in a real-life process. Interviews with process mining experts confirm that Kronos is useful and easy to use for identifying improvement opportunities related to waiting times.

Abstract Prescriptive process monitoring methods recommend interventions during the execution of a process to maximize its success rate. Current research in this field focuses on algorithms to learn intervention policies that maximize the expected payoff of the interventions under certain statistical assumptions. In contrast, there has been limited attention on how to aid process stakeholders in understanding the outputs of these algorithms. In this research, we set to develop an interface to provide end users with relevant information to guide the decision on where and when to trigger interventions in a process. We draw upon an analysis of existing solutions and a review of the literature to elicit information items for a user interface for prescriptive process monitoring. Thereon, we develop a user interface concept and evaluate it with experts. The evaluation confirms the informational needs covered by the user interface concept. In addition, the evaluation shows that different end-user groups (operational users, tactical managers, and process analysts) can benefit from the information items included in the interface.

Abstract Waiting times in a business process often arise when a case transitions from one activity to another. Accordingly, analyzing the causes of waiting times of activity transitions can help analysts to identify opportunities for reducing the cycle time of a process. This paper proposes a process mining approach to decompose the waiting time observed in each activity transition into multiple direct causes and to analyze the impact of each identified cause on the cycle time efficiency of the process. An empirical evaluation shows that the proposed approach is able to discover different direct causes of waiting times. The applicability of the proposed approach is demonstrated in a real-life process.