Building a Natural Language Processing Model to Extract Order Information from Customer Orders for Interpretative Order Management

Abstract

Due to the increased complexity of supply chains and the various challenges that these supply chains are facing, it is important for supply chains to automate and optimize their supply chain management processes to respond to these challenges and maintain their competitive advantages. Order management plays an integral role in supply chain management, and one of the ways where the order management process can be streamlined is to adopt a no-touch approach. In this paper, we describe a natural language processing (NLP)-based engine prototype to extract and interpret order information from customer natural language orders, which will facilitate no-touch order processing. This engine prototype can then be integrated into an overall no-touch order management engine that can be used to demonstrate a reliable Advanced Available-to-Promise (AATP) process at the critical sites in a supply chain testbed.