LLM-Assisted Optimization of Waiting Time in Business Processes: A Prompting Method

Abstract

AbstractWaiting is a waste in business processes, adversely affecting performance metrics such as cycle time or on-time delivery. Process mining techniques allow business users to analyze waiting times and their causes based on data extracted from enterprise systems. However, process mining techniques, per se, do not assist users in identifying redesign options to optimize business processes, e.g. to reduce waiting time. Recent studies suggest that Large Language Models (LLMs) may aid business users in various process analysis tasks, particularly in conjunction with process mining techniques. This paper studies how to use LLMs to assist business users in analyzing and redesigning business processes to optimize waiting time. The study compares two methods to prompt an LLM to recommend redesign options to reduce waiting times: (1) a baseline (“zero-shot”) method involving a minimalistic prompt; and (2) an enhanced method where the prompt includes descriptions of redesign patterns that may lead to redesigned processes with lower waiting times. To compare these methods, we conduct a user evaluation that combines semi-structured interviews with a survey involving process analysts. The analysts compare the recommended redesigns in terms of desirable properties of recommendation systems, such as relevance, usefulness, and diversity of the recommended redesigns. The results suggest that the enhanced prompting method yields more relevant and actionable redesign options. In contrast, the baseline produces high-level recommendations more suited for managerial decision-making.