Digitalizing the supply chain on the road to deal with global crises

Abstract: Recent years have seen more and more companies digitize their logistics systems to varying degrees. Data sharing standards for the supply chain have appeared in different fields, such as ONE Record for air cargo transport, papiNet for the paper and forest industry. One of the existing challenges is that it is difficult to carry out horizontal integration between companies from different supply chains because they each use different data exchange standards. Hence it is challenging to achieve wider-scale sustainability in this case. DigiGoods (a Vinnova funded project) focused on bringing improvements through digitization and data sharing through participants in the logistics value chain. It proposes a data model to build its data exchange standard between supply chain partners for sharing data and synchronizing progress. This thesis explores how to integrate this standard with other existing standards, and on this basis, explores how to use machine learning to optimize forecasts in the supply chain. This thesis lays the foundation for the integration of standards in the supply chain, explores the application of machine learning in the supply chain, and applies machine learning algorithms in multiple stages to improve the accuracy of forecasts in the supply chain, thereby responding to the global crisis. The performance of eight machine learning models is tested and compared to find the optimal algorithm and parameters for each dataset. A prototype is implemented to combine the advantages of the eight models and demonstrate that multi-stage machine learning could improve the prediction results in the context of DigiGoods.