Data driven logistics-oriented value stream mapping 4.0: A guideline for practitioners

The use of data-oriented approaches like data mining or machine learning has an increasing potential for application in the planning and control of production and logistics systems. The growing amount of digital process information helps to expand the existing process understanding in order to determine weaknesses in the process landscape. Due to the extensive complexity within production and logistics systems, a comprehensive approach is required to ensure a systematic analysis. This article presents an extension of the value stream method based on the existing approaches that is intended to support operators of logistics systems in the company. This methodology collects all relevant process information and validates the data maturity. Hence, indications for the use of data-oriented approaches can be given and potential machine learning-based analysis scenarios can be derived.