Analyzing the Impact of UPI on Supply Chain Performance: A Natural Language Processing Approach with Generative Pre-trained Transformers

The increasing adoption of the Unified Payments Interface (UPI) has prompted an extensive investigation into its impact on supply chain performance. This discovery is driven by the growing global acceptance of UPI and its implications for business dynamics. As UPI gains traction, there are plans to reshape many aspects of supply chain management (SCM), delivering greater transparency, agility, and competitiveness. The impetus is further fueled by the meager amount of research that focuses on the ramifications of UPI for the Indian SCM context. This study aims to fill this gap and elucidate how UPI affects traceability, inventory management, competitiveness, and cash flow in SCM. To achieve these goals, a complex combination of conversion learning with pre-trained transformers (GPT) and natural language processing (NLP) is used. This method facilitates a nuanced analysis of textual data collected from different MSMEs. By quantifying the cosine similarity between statements having more than 80% Similarity through the attention mechanism used for triangulation found that minimal impact on inventory and traceability but significant error reduction, cash conversion cycle, paperwork in the transaction, and fake currency and increased transparency that led to reduced theft, but businesses are concerned about fee imposition, lower rural penetration and feel of spending businesses.