Natural Language Processing (NLP) for Supply Chains

Since many operations generate large amounts of unstructured text data from customers, suppliers, regulators, and internal processes, NLP allows computer programs to process and reason about human language. This capability has been increasingly sought after for supply chain management. While quantitative analysis and NLP approaches have been employed, text is no longer considered a decision signal, but rather a contextual adjunct to most supply chain management applications. This gap is known as the Language–Decision Disconnect, which indicates that there is no mechanism by which language can influence decision-making. Decision-Centric Supply Chain NLP is described in this chapter as a domain-specific approach to the integration of domain-specific models into forecasting, supplier risk monitoring, and customer service processes. It includes temporal alignment, interpretation, and governance. By adding product reviews and news from around the world, the analysis predicts a 23% increase in forecast accuracy, a 67% increase in the speed of risk assessment, and a 31% increase in service quality. These observations illustrate operational scalability, robustness, enterprise readiness, cross-domain stability, and quantifiable decision benefits under realistic operational constraints within different multilingual supplier and customer environments.