ABSTRACT
It is possible to find a wealth of useful information in the literature about the when, where, and how of demand-driven supply chain operations management strategies (DDSCOMSs). The supply chain planning and management processes of manufacturing company managers often involve the usage of many DDSCOMSs. Understanding when, how, and why to mix distinct DDSCOMSs is crucial for their work. The phases that comprise the suggested approach are as follow as preprocessing, training the model, and feature extraction. Preprocessing makes use of text information acquisition and preprocessing. A technique for reducing variables, principal component analysis (PCA) is what we utilized for feature extraction. It takes a collection of vector data and uses them to create a new set of vector observations. Training the model became more precise with the help of LLM. The proposed approach yielded an accuracy percentage of 95.72%.
