What protects intermodal supply chains against susceptibility to disruptions?

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To be efficient and economical, supply chains are usually very complex and frequently intermodal. Particularly cross-border deliveries demand a well-organized and transparent supply chain management. In principle this holds true: The longer the route and the more often the mode of transportation is changed, the greater the risk of disruptions. Increased digital supply chain visibility creates greater resilience in the transport of goods.

In the digital age, global offers, not only of consumer goods, are easily accessible. Everything your heart desires is usually just a mouse click away. For many people it is conceivable which routes the ordering of goods takes. But the depth of the task is not always fully comprehensible, even for some of those involved in a transport.

The logistical feat of just-in-time deliveries, i.e. delivery to the end customer within a tightly specified time window, is a very practical solution for customers who like to accept their parcels personally and want to avoid possible return shipping. However, the enormous challenges this poses for the logistics provider regarding the so-called last mile are often difficult to coordinate. This risk is of course even higher for the transport processes that bring the goods to the last point of transshipment. Because the potential for disruptions naturally increases on longer routes and with a more frequent cargo handling between alternative transportation modes.

If we consider Germany’s import figures, for example, with one of its most important trading partners, the People’s Republic of China, goods worth 116.3 billion Euros were imported in 2020. The distance that these end products or semi-finished materials must travel to reach the German consumers, or the factories, is huge and in almost all cases can only be realized through intermodal supply chains. In many cases, the goods are loaded onto a truck from the production plant’s outbound warehouse and transported to the next port. There, they are reloaded and continue their journey on a container ship. After their arrival in Germany, there is another change of transport modality to barge or, not infrequently, to rail. The container is thus transported by freight train to the nearest station, where it is loaded onto a truck again to reach an intermediate storage facility. To limit costs for storage time the products need to be delivered to the end customers as quickly as possible. Thus, the goods continue their journey on the aforementioned last mile by small trucks – in some cities, the final meters are even managed by cargo bicycles to become more sustainable.

Disruptive factors prevent smooth supply chain management

Due to the frequent change of transport modalities, intermodal supply chains are particularly susceptible to external disruptions. The delays caused by heavy traffic, even congestion at borders and transshipment terminals, as well as accidents along the way, have a direct impact on the Estimated Time of Arrival (ETA). If communication between all parties in the supply chain is not working properly, these delays cannot be conveyed. Missed connections cause further waiting periods, as organizing new transportation options can take additional time. Weather conditions are another source of disruption. Although they are easy to predict, their occurrence can cause serious interruptions in the transport chain and thus to the schedule.

Intermodal end-to-end monitoring

Digital solutions are needed to proactively counteract these unforeseen and sudden interruptions with more information. Most transportation movements are already recorded and transmitted digitally. So why is holistic end-to-end monitoring of the transport chain across all modalities so difficult? Incompatible information presents the biggest obstacle, as supply chain participants sometimes use different applications or operate at various levels of digitization. A smooth exchange of collected data sets becomes unfeasible. This results in so-called data silos that need to be integrated to generate added value from all tangible information.

By evaluating transport data and publicly available information on traffic and weather conditions, routes can be optimized and adjusted in the event of sudden disruptions. Normally, this evaluation is very time-consuming because analysts must first identify all possible disruptive factors. Furthermore, a free flow of communication between the supply chain participants is required. This enables rapid and steering intervention to reduce delays to a minimum because fact-based decisions rely heavily on the quality of the information available.

Stable intermodal supply chains due to transparent processes

The Potsdam-based start-up Synfioo offers an efficient solution: Using Artificial Intelligence (AI), it analyzes historical and current data on disruptions from over 70 external sources. Thus, public information on weather conditions, ferry times, traffic jams and waiting times, including at border crossings, as well as current positions of ship, air and train traffic are processed. Machine Learning (ML) can be used to derive reliable and precise ETA predictions. Especially in complex intermodal supply chains, this facilitates the organization of modality changes and increases effectiveness, as the use of AI and ML saves time enormously.

With the Track-&-Trace-application provided by Synfioo, all supply chain participants also always have access to current location data of the transport in real time. This not only enables communication processes, but also a coordinated and short-term response time to quickly modify transport routes in case of disruptions. The technology to support intermodal supply chain management exists. If it is used, it minimizes the random factor in multimodal supply chains. Logistical processes run more smoothly then as a result.

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