Data Trust in Logistics: Price Prediction for Collaboration

The logistics industry is facing the challenge of handling the increasing number of parcel shipments, which requires more drivers or alternative approaches. One possible solution is to improve collaboration between logistics providers through the use of a data trust. This paper proposes the implementation of a price prediction model to automate the comparison of potential partners and thus facilitate collaboration. The concepts of data trust, access control and policies, as well as the fundamentals of artificial intelligence in price formation and prediction are explained. The proposed model aims to simplify the pricing process and incorporate it into a global model. The study highlights the importance of data privacy and security, regulatory compliance, the use of access control for enforce access policies and machine learning methods in data trust environments. The findings highlight the potential benefits of AI-based pricing models in the logistics industry and the need for further research and development in this area.