Business Process Simulations (BPS) are essential for analyzing, optimizing, and forecasting organizational workflows. What-if simulations enhance BPS by enabling analysts to test hypothetical changes—such as new tasks, altered decision points, or alternative flows—before implementation. A key challenge is to set correct simulation parameters for these changes, including task durations, distributions over decisions, and who would execute the involved tasks and when.

For example, a bank may introduce overnight delivery for newly issued credit cards, alongside the existing standard mail and express courier options. To simulate this scenario accurately, we must estimate how often customers will choose the new option and the duration of its associated tasks, like expedited printing, packaging, and courier handoff.

This project aims to develop adaptive methods that realistically model human behavior and decision-making for such what-if scenarios. For this purpose, we investigate the following approaches:

• AI techniques similar to Retrieval-Augmented Generation (RAG) using low-dimensional text embeddings and clustering to identify historical process flows similar to new what-if scenarios. The scenario is encoded into a semantic vector, which is then compared to past cases using similarity metrics like cosine distance. Once similar instances are retrieved, their task durations and decision probabilities are extracted and used to estimate the unknown parameters in the new scenario


• Large Language Models (LLMs) are able to predict human decisions and responses. Our goal is to use foundation models such as Mistral to directly predict task durations or decision distributions for what-if scenarios. The approach involves optimizing prompt strategies and fine-tuning the models to ensure context-aware, plausible outputs that are aligned with historical and expert knowledge.

Your task:

• Perform a literature review about the current state-of-the-art in what-if simulations for BPS and their adaptation


• Develop together with your supervisor ideas for new adaptation mechanisms that are based on historical data and/or LLM suggestions on how humans might behave


• Evaluate your methods by comparing them to baselines and performing ablation studies


• Write a scientific report about your findings, preferably as a scientific workshop or conference contribution

The internship will be carried out at CEA Grenoble.

• #1: Motivation for scientific work and the development of AI tools


• Proficient in Python and development tools (git, debugging, ...)


• Master student in Computer Science or a similar field or related expertise


• Good English level in speaking and writing


• Nice to have: Familiarity with Machine Learning and the usage of LLMs via API