The development of data-driven logistic platforms for barge transportation network under incomplete data

Currently, the capabilities to capture, store and process logistics data, such as generated by the transport and handling of millions of maritime containers to distribute cargo worldwide, are available. A lot of these logistics events are already recorded and stored within some databases to keep track of operations. These data represent considerable value when analysed to diagnose bottlenecks and inefficiencies and guide better decisions in global supply chains. Since, amongst other things, the data is not readily available as information to the decision maker, this potential has not been reaped. In this paper, we focus on the question of how data can be transformed into meaningful information to the decision maker even when data is available to a limited extent. We explore the role of data-driven 4PL IT platforms, where users of the platform provide data that is incomplete and untimely, in producing valuable information for the stakeholders of their logistics ecosystem. We develop a mathematical model to obtain meaningful information from lower-quality data. We apply this in the context of container logistics of river vessels (barges) in a port environment. We introduce three sets of functions that capture movement, inventory, and productivity, to describe the logistics processes at hand and assess the state of a distribution network, often not recorded by the IT systems of operators in the distribution network. A Kalman filter approach is used to match movement and productivity information, to detect the state of the distribution network, and to predict its evolution in support of decision making about the allocation of containers to empty slots on barges.