AI & Large Language Models for Enterprise Decision Intelligence in ERP, Supply Chain & Manufacturing Ecosystems

Abstract

The application of AI and large language models is fast becoming a revolutionary tool for decision intelligence in ERP (enterprise resource planning), supply chain, and manufacturing ecosystems. In this paper, there will be an examination of the technical background, market dynamics, and empirical evidence on the use of LLMs in these interconnected fields. Through a systematic literature review combined with a quantitative analysis of market data, this paper uses findings from reputable scholarly databases such as Scopus, Web of Science, and arXiv, as well as from credible market reports in the industry. This results in unmistakable market traction, as the size of the enterprise AI market has been projected to exceed 114 billion dollars in 2026, and even beyond to reach 270 billion dollars in 2031. There is empirical evidence that the proper use of LLMs improves demand forecasting, planning latency, and coordination in the supply chain. The use of hybrid models that integrate language models together with digital twins shows some promising potential in relation to decision support in manufacturing, where more resources are used. Still, there are some drawbacks in the sphere discussed, including the absence of theoretical background, insufficient verification in various industries, as well as governance and security concerns related to data. Specifically, the authors focus on the gap between the optimistic prospects concerning commercial markets and the empirical background of these technologies. In conclusion, the authors point out that despite the fact that large language models can really transform the industry of enterprise decision intelligence, further independent studies are required.