In logistics, questions as “Where is my container?” and “When does my container arrive?” can often not be answered with sufficient precision, which restricts the ability of logistics service providers to be efficient. Since logistics is complex and often involves multiple transportation modes and carriers, improving efficiency and saving costs in the supply chain requires communication between the different parties and the usage of real-time data is critical. Currently, logistics service providers (LSPs) use real-time data to a very limited extent, mainly for tracking the progress of a specific part of a given shipment. This data is retrieved manually from a number of websites and sharing with other actors is not even considered. This leads to lack of end-to end visibility and delays in planning. This research proposes an architecture and a common data model for an integration platform that allows the automated collection of real time container tracking data enabling LSPs to plan more efficient. Currently, there is no common data model available that contains all the information required and enables LSPs to track their shipments real-time. The common data model is designed via a bottom-up approach using results of interviews, observations at different logistics service providers, analyses of open data on websites, and serves the information needs of the business processes involving such data. The model is also validated against industry standards. Based on the proposed architecture a prototype was built that is tested in real operating conditions with a fourth party logistics company.