Predicting Future Inbound Logistics Processes Using Machine Learning

Abstract

Manufacturing industry is highly affected by trends of globalization and increasing dynamics of product life-cycles which results in global supply chain networks. For inbound logistics, a high variance of parts from different suppliers and locations needs to be delivered to the assembly line. Planning these inbound logistics processes depends on frequently changing information of product development, assembly line planning and purchasing. Currently, a high amount of time is spent for gathering information during planning and existing knowledge from previous planning processes is scarcely used for future planning. Therefore, this paper presents an approach for predictive inbound logistics planning. Using machine learning, generic knowledge of logistics processes can be extracted and used to predict future scenarios.