Using NLP to Enhance Supply Chain Management Systems
Farhan Aslam *
Department of Information Technology, University of the Cumberlands, KY, USA.
Jay Calghan
Department of Information Technology, University of the Cumberlands, KY, USA.
*Author to whom correspondence should be addressed.
Abstract
This article explores the transformative potential of Natural Language Processing (NLP) in enhancing Supply Chain Management (SCM) software. With the digital age ushering in vast amounts of unstructured data, especially customer feedback, there is a pressing need for advanced analytical tools. NLP, a subset of artificial intelligence, offers techniques such as sentiment analysis, topic modeling, and text classification to interpret this data. By integrating these techniques, businesses can gain unparalleled insights into their supply chain operations, leading to improved operational efficiency, stakeholder satisfaction, and proactive issue management. The article reviews studies across various industries, from food delivery to railways, underscoring the versatility and efficacy of NLP in diverse contexts. The findings highlight NLP's role as a game-changer in SCM, promising a more data-driven, efficient, and customer-centric supply chain landscape.
Keywords: Natural language processing, supply chain management, sentiment analysis, topic modeling, text classification, digital transformation, customer feedback, operational efficiency, stakeholder satisfaction