Fuzzy Logic-Driven Natural Language Processing in Pharma Supply Chain Analytics

Fuzzy logic offers a method for handling uncertainty and imprecision, proving valuable in natural language processing (NLP). Fuzzy search, a key component, enhances search functionality by permitting approximate matches. This study examines the application of fuzzy search techniques in wholesale pharmaceutical distribution, where data retrieval accuracy is crucial for public health and safety. We present two case studies, each showcasing specific fuzzy search methods designed to overcome unique data retrieval challenges. A Python implementation demonstrates the practical application of these techniques to enhance search accuracy and efficiency in large pharmaceutical datasets. Our results highlight fuzzy logic's potential to revolutionize information retrieval systems. By offering practical insights and technical guidance, this research aims to enable pharmaceutical industry stakeholders to effectively implement fuzzy search techniques, leading to improved data management and decision-making processes.