The Impact of Large Language Models on Task Automation in Manufacturing Services

TL;DR

This paper investigates how large language models can improve task automation in manufacturing services by enhancing efficiency and customer support through text correction, summarization, and question answering, while addressing integration challenges.

Contribution

It introduces a practical framework combining LLMs with domain knowledge for manufacturing, demonstrating improved operational efficiency and customer service capabilities.

Findings

LLMs reliably correct errors and generate summaries.

Integration of RAG improves response relevance.

Efficiency gains are significant despite challenges.

Abstract

This paper explores the potential of large language models (LLMs) for task automation in the provision of technical services in the production machinery sector. By focusing on text correction, summarization, and question answering, the study demonstrates how LLMs can enhance operational efficiency and customer support quality. Through prototyping and the analysis of real-life customer data, LLMs are shown to reliably correct errors, generate concise summaries of complex communication, and provide accurate, context-aware responses to customer inquiries. The research also integrates Retrieval Augmented Generation (RAG) to combine LLM outputs with domain-specific knowledge, enhancing precision and relevance. While the findings highlight significant efficiency gains, challenges such as knowledge hallucination and integration with human workflows remain barriers to large-scale adoption. This study contributes to the theoretical understanding and practical application of LLMs in manufacturing, paving the way for further research into scalable, domain-specific implementations.