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Improving the planning and performance of transportation chains with sea port data – insights into the “LAVIS” mFund project

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Sea port data for determining the predicted cargo availability

This blog article describes the current status of the project “LAVIS – Intelligent Data Analysis for Forecasting Cargo Availability in Sea Ports”. The Institute of Shipping Economics and Logistics (ISL) and AKQUINET, an IT company, are currently carrying out the feasibility study, with a time frame of less than a year. The project is receiving funding from the mFund research initiative by Germany’s Federal Ministry of Transport and Digital Infrastructure.

In its first step, the LAVIS project aims to evaluate whether cargo availability in container terminals can be determined more accurately, to improve efficiency and speed during loading and optimize downstream transportation chains. To achieve this, the project is examining which approaches for determining the predicted cargo availability are possible and which data is needed to do so.


Ship’s estimated time of arrival

Let us consider an example scenario involving container imports. The nine-day duration of the sea transport for the journey from Halifax (Canada) to Bremerhaven is known, as is the ship’s estimated time of arrival (ETA). The central questions for planning the subsequent legs: When exactly can the further shipping to the hinterland take place? And which parameters affect this point in time?

One already identified parameter is the process of unloading the ship. Since container ships continue to increase in size, the unloading process for a given ship can already take several days, depending on the number of containers involved. Other parameters also influence the earliest possible onward transport of the containers at the sea port:

  • ETA of the ship as the starting point
  • Unloading time of the container
  • Capacity utilization at the terminal
  • Commercial handling
  • Customs processes

Our approach in the LAVIS project is to determine the estimated time of availability (ETAv) for each container as the main measurand. It indicates the time at which the container will actually become available. This information will enable the truck, rail, or barge schedulers to plan more precisely before the container actually becomes available, reducing storage times at the terminal. If the ETAv were known, it would become apparent whenever a container threatened to miss a rail departure slot, for example. The rail scheduler could then work proactively, reschedule containers, and ensure that the train capacity was used more efficiently.

The LAVIS research project addresses the following key questions:

  • Which information regarding the predicted cargo availability is needed by the players in hinterland transport?
  • Which business models are conceivable for data providers?
  • Which factors influence the time of cargo availability at the sea port?
  • Which procedures can be used to forecast cargo availability?
  • Can a data-based application to determine the predicted cargo availability be realized and if so, what prerequisites must be met (feasibility)?

Presently, a number of different surveys are being carried out within the project framework – of companies, service providers, and public authorities involved in the supply chain in and around ports – to explore general interest and feasibility. Our interim conclusion: the industry believes that the ETAv approach is sensible and sees potential to improve the processes. In the next steps, we will examine the identified data gaps and potential solutions for dealing with the absent data (

Modern information systems in sea ports can already give hinterland shipping participants status notifications for their relevant cargo in near-real time, which supports the planning of hinterland traffic. If the ETAv were introduced as a planning parameter, the planning and performance of transportation chains could be improved further. In the truck planning area, for example, the subsequent legs could be shaped more proactively, reducing storage durations and lead times at the terminal. In the case of bulk goods transporters, such as trains and barges, capacity planning could be improved as outlined above.

By Patrick Specht, Research associate ISL – Institute of Shipping Economies and Norbert Klettner, managing director akquinet port consulting


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