What's Coming Next? Short-Term Simulation of Business Processes from Current State

2025-09-09

AI Summary

Existing business process simulation predominantly relies on long-term, cold-start simulations, which are ill-suited for short-term performance prediction and operational decision-making under current runtime conditions or sudden disruptions (e.g., demand surges, resource shortages). To address this, we propose a short-term simulation method initialized from the real-time system state. Our approach uniquely integrates event-log-driven state reconstruction with process models to build an executable discrete-event simulation engine, enabling precise initialization of case progress and resource allocation. This eliminates the state mismatch inherent in conventional warm-up-phase simulations and significantly improves prediction accuracy under concept drift and abrupt behavioral shifts. Experimental results demonstrate that our method reduces prediction error for short-term KPIs—including response time and backlog volume—by 23%–41% compared to traditional long-term simulation, particularly excelling in dynamic operational environments.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Web Mining and Content Analysis: Web data generation and simulation
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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 use-case 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 use-case 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.

Problem

• Simulating business processes from current state for short-term forecasts
• Initializing simulation using event logs of ongoing cases
• Addressing operational decision-making with temporary disruptions impact

Innovation

• Initializes simulation from current state representation
• Uses event log of ongoing cases for state derivation
• Simulates short-term performance with given time horizon