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Jul 14, 2020 at 7:00 AMHHLA is moving forward with digitalization. On one hand, the new booking portal Modility is being introduced, and on the other hand, machine learning is being utilized to analyze the prediction of a container’s dwell time. This is intended to increase productivity.
(Hamburg) At the initiative of Hamburger Hafen und Logistik AG (HHLA), the logistics company is developing a new booking portal for combined transport (KV) in collaboration with eleven partners from the transport and forwarding industry. Modility is being established as a provider-neutral corporate spin-off to help shape the digital future of combined transport and promote the transition to climate-friendly KV transport. As a first step, an information website has now been launched at www.modility.com. The booking portal is expected to go live by the end of this year.
Modility aims to bring together the available transport capacities of KV operators with the transport needs of freight forwarders. The focus is on combined road/rail transport across Europe.
HHLA’s CEO Angela Titzrath sees Modility as a good example of developing cooperative growth strategies: “Together with customers and partners, we are developing a new digital hub that shapes the transport flows of the future. HHLA sees itself as the initiator of a portal from which many players in logistics can benefit from new digital potentials. Close collaboration ensures that the interests and ideas of stakeholders in the KV market are taken into account.”
Modility offers the opportunity for easy access to intermodal transport services while simultaneously generating new customer relationships. This further strengthens KV transport as a powerful and environmentally friendly transport system.
Quick and Easy Access to Climate-Friendly KV Transport
“With Modility, we want to enable quick and easy access to climate-friendly door-to-door transport,” says Lars Neumann, Director of Logistics, Strategy, and Business Development at Hamburger Hafen und Logistik AG. There is significant potential to shift transport from road to rail.
HHLA is developing a market solution for the market in collaboration with cooperation and development partners from various sectors of the transport and forwarding industry, including the European KV Association UIRR. Neumann states: “Our current focus is on the demand-oriented further development of the application. In dialogue with interested parties, potential users, and pilot customers, we want to ensure that Modility meets the diverse needs and meets market demands at go-live.”
Modility: Inform, Plan, and Book
The portal is unique in its type and development. The complexity of combined transport is represented in a provider-neutral portal with clear functionalities: Inform, Plan, Book, is the formula of Modility.
HHLA with Machine Learning
(Hamburg) Hamburger Hafen und Logistik AG (HHLA) has developed solutions for its Hamburg container terminals as one of the first ports worldwide that utilize machine learning (ML) to predict the dwell time of a container at the terminal. The first two projects have now been successfully integrated and applied in the IT landscape of the Altenwerder (CTA) and Burchardkai (CTB) container terminals.
Angela Titzrath, CEO of HHLA, emphasized in a greeting for the World Artificial Intelligence Conference (WAIC) taking place in Shanghai from July 9 to 11 the importance of ML for the company: “The ongoing digitalization is changing the logistics industry and thus our business in the port. Solutions for machine learning offer us many opportunities to increase the productivity and capacity rates of the terminals.” The HHLA CEO announced that further fields for the application of ML will be identified.
Increasing Productivity
At the CTA, the productivity of the automated block storage is increased through an ML-based prediction. The goal is to accurately predict the timing of a container’s pickup. If a steel box does not need to be unnecessarily re-stacked during its dwell time in storage, this leads to a significant optimization of processes. When storing containers, their pickup time is often still unknown. Therefore, in the future, the computer will calculate the likely container dwell time. It uses an algorithm based on historical data but continuously optimizes itself through state-of-the-art machine learning methods.
A similar solution is applied at the CTB, where both automated and conventional container storage are utilized. Here, too, ML supports terminal control by assigning optimized container slots. In addition to dwell time, the algorithm can also calculate the type of delivery. Machine learning can more accurately predict whether a container should be loaded onto a truck, train, or ship than previously indicated by the reported data.
A clearly positive effect is already evident for both terminals, as containers are stored according to their expected pickup and then need to be moved less frequently. The projects have been driven forward by teams from HHLA and the consulting subsidiary HPC Hamburg Port Consulting.
Photos: © HHLA
