Approach to a GPT-based Early Detection Tool to Evaluate Heterogeneous Data Sources and Identify Reconfiguration Needs of SMEs in the Production Sector

In the face of a rapidly evolving commercial ecosystem, small and medium-sized enterprises (SMEs) must ensure their process chains are capable of swift reconfiguration to mitigate disruptions from supply chain volatility, regulatory changes, and demand shifts. This study addresses the need for reconfiguration in the context of data-driven opportunities for early identification of reconfiguration needs in SMEs, with a focus on the production sector. We identify a variety of diverse and heterogeneous data sources that are critical for tracking these disruptions, such as public news, economic reports, market analyses, legal documents, and internal corporate records. To make use of these data streams, we propose an innovative early detection tool that employs advanced machine learning techniques and private, locally operated Generative Pretrained Transformers (GPTs). This system is intended to process and analyze the various data formats that SMEs encounter including HTML, PDF and plain text as well as quantitative data. By combining public and company internal data while addressing privacy concerns, the tool aims to provide SMEs with a comprehensive tracking of their commercial environments. The tool also addresses the challenges posed by the limited availability of large datasets within SMEs, which are typically required for effective use of machine learning techniques, allowing for effective operation with smaller datasets. As these technologies continue to advance, their impact across various industries, including legal, manufacturing, and supply chain management, is set to expand significantly. The proposed tool offers SMEs enhanced decision-making, compliance assurance, and the ability to proactively adapt to the unpredictable global business landscape.