Improving Operational Efficiency and Performance in Large-Scale Brewery With the Use of AI

Abstract

This report is the master’s thesis of two students at Chalmers University of Technology, carried out at a large-scale brewery. The purpose was to identify where AI could improve operational efficiency and performance within their S&OP department. This was investigated by analyzing where an AI would be effective, what type of AI that would be suitable, and how an implementation could be conducted. The investigation contained a literature study of the current market usages of AI and AI implementations, as well as an empirical study at the large-scale brewery where nine interviews, one focus group, and two shadowing sessions were conducted. The findings indicate that implementing an agentic AI in selected process categories would be beneficial, and the implementation of an LLM would cover the remaining processes. Utilization of the available integrated AI tool would therefore cover all process categories identified in the S&OP department. The thesis also provides a recommendation for how an AI-model can be implemented based on literature and empirical findings. The large-scale brewery has the potential to improve operational efficiency and performance by utilizing existing AI tools, provided that employees receive the necessary training to use them effectively.