Requirements for a ML- and platform-based simulation service

Today, with the rapid development of new technologies, many industries have adopted them to enhance their performance. Among them, additive manufacturing is known as a rapid manufacturing process that can produce products in a single step, reducing time to market. Since 2019, COVID-19 has caused significant negative impacts on the global supply chain (SC), including shortages of medical goods. Therefore, an agile and flexible medical SC is required. While machine learning (ML) methods are known for using big data to gain valuable insights through forecasting, simulation enables unlimited if-then scenarios to make informed decisions in optimising SC operations. The combination methods between ML and simulation in solving SC issues has not been investigated at a sufficient level. This paper, therefore, aims to explore the advantages of coupling ML with simulation techniques in the SC field by conducting a systematic literature review. Through an expert survey, requirements for a ML and platform-based simulation service will be investigated from a technical point of view to develop a suitable use case in the future.