Abhik Choudhury, IBM Corporation, USA

Fuzzy logic provides a framework for dealing with uncertainty and imprecision, making it particularly useful in natural language processing (NLP) applications. A critical subset of fuzzy logic is fuzzy search, which enhances search capabilities by allowing approximate matches rather than requiring exact ones. This paper explores the integration of fuzzy search techniques within the context of wholesale pharma distribution, a field that demands high accuracy in data retrieval due to its impact on public health and safety. We investigate two distinct case studies where each demonstrates specific fuzzy search techniques tailored to address unique challenges in data retrieval. Through a Python code implementation, we illustrate how these techniques can be practically applied to improve the accuracy and efficiency of searches within large datasets common in wholesale pharma distribution environments. Our findings underscore the potential of fuzzy logic as a transformative tool for enhancing information retrieval systems. By providing practical insights and technical guidance, this research aims to empower stakeholders in the pharmaceutical industry to leverage fuzzy search techniques effectively, ultimately contributing to better data management practices and improved decision-making processes.

Fuzzy logic, Fuzzy search, NLP, Levenshtein Distance, TF-IDF Vectorization, Cosine Similarity, Wholesale Drug Distribution, Chemical Composition Search, String Matching Algorithms, Data Preprocessing, Information Retrieval, Robust Search Techniques