On the Value Potential of Large Language Models in the Manufacturing Industry

Jochen Wulf (), Shaun West (), Matthew Anderson (), Petra Müller-Csernetzky () and Jürg Meierhofer ()
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Jochen Wulf: Zurich University of Applied Sciences (ZHAW)
Shaun West: Lucerne University of Applied Sciences and Arts
Matthew Anderson: Blekinge Institute of Technology
Petra Müller-Csernetzky: Lucerne University of Applied Sciences and Arts
Jürg Meierhofer: Zurich University of Applied Sciences (ZHAW)

A chapter in Smart Services Summit, 2025, pp 135-147 from Springer

Abstract: Abstract This study explores the integration of Large Language Models (LLMs) into the manufacturing sector, focusing on their potential to enhance efficiency, decision-making, and product quality. While existing literature emphasizes the conceptual benefits of LLMs, there is limited empirical evidence supporting these claims. The study uses a case study to examine five affordances of LLMs, including automating information processing and improving data quality, as well as four constraints, such as risks to job stability and data security. Key findings suggest that LLMs offer substantial opportunities for streamlining operations and reducing manual labor, yet challenges such as explainability and secure data management remain. The study contributes to both theory and practice by advancing the understanding of LLM integration in manufacturing through an affordance theory framework. This framework helps assess how LLMs influence operational processes and workforce dynamics. However, the study acknowledges its limitations due to the reliance on early stage data and a single case study, urging further research into diverse industrial settings and long-term effects.

Keywords: Large language models; Manufacturing; Affordance Theory; Constraints (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prochp:978-3-031-86958-7_10

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DOI: 10.1007/978-3-031-86958-7_10

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