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Urban logistics - Part 5 of the article series: Success factors

The fifth and final part of our Urban Logistics series

Cities are chaotic places full of hustle and bustle, like a busy warehouse on an enormous scale. In recent years, data analysis techniques and automated technologies have transformed the face of the warehousing sector. What lessons can we learn from this example to help us improve the logistics networks in our cities?


In the past, warehouses were managed using a card index box and a huge stack of paperwork. “All day, you’d see people rushing around the warehouse, piles of paper in hand, sorting out orders by calling out instructions or following shipment and picking lists,” recounts Peter Golz, Senior Director for Intralogistics Software Development at Dematic. Then came the software revolution. Today, warehouse managers can access all the data they need in real time. They can see what goods are available and where; what tasks are currently pending; and follow every movement of their warehouse trucks.

“Thanks to digitization, I can run my warehouse operations in line with specific parameters,” says Maik Manthey, Senior Vice President for Digital Business at the KION Group. “Sometimes my priority will be throughput, sometimes time, sometimes cost, and sometimes it will be maximizing process reliability.” Using modern simulation and analysis software, we can optimize our operations in specific areas and deprioritize others in order to achieve our primary goal more efficiently. “We collect the data, evaluate it, and devise recommendations based on it,” he explains.

“Just because there are signs on the warehouse wall, doesn’t mean everything automatically runs like clockwork”

To what extent can these experiences be applied in urban areas? One fundamental difference between cities and warehouses is that warehouses operate according to a stricter set of rules. Cities, by comparison, are chaotic places where it’s virtually impossible to predict where people and traffic will go next. This may seem obvious, but, in fact, it’s not that simple, as Maik Manthey explains: “Just because there are signs on the warehouse wall, for instance saying don’t unload boards here, that doesn’t mean everything automatically runs like clockwork.” People still go into areas where they shouldn’t be, or park a truck or leave a pallet in the wrong place. For a warehouse to maximize its automation potential and reap the benefits of autonomous vehicles, the systems have to be able to cope with these unexpected disruptions. Otherwise, humans will always have to step in to help.

As Maik Manthey explains: “The challenge for the software is not so much training it to complete the task, but rather training it to overcome disruptions.” “This requires positive action: The trucks can’t just stop, they need to be able to resolve the problem.” That’s why autonomous warehouse trucks are fed data to enable them to evaluate different situations. State-of-the-art trucks such as the iGo neo from STILL can already recognize pallets, people, and signs, and react accordingly. And thanks to machine learning and artificial intelligence, autonomous transport systems can even be trained to anticipate challenges ahead of time and take the necessary action. These challenges are essentially the same as those facing autonomous vehicles on the streets of our cities, namely people, other vehicles, and obstacles.

Autonomous warehouse trucks can evaluate different situations independently. A truck like the iGo neo from STILL can recognize pallets, people or signs and act accordingly. In the city, however, things are much more chaotic than in the warehouse, the road users are hardly predictable.

Identifying Patterns Using Simulations

Peter Golz believes that “automated vehicles are part of our future.” The first examples currently in use in the warehousing sector still navigate using reflectors or predefined routes. But Peter Golz is confident “that will change,” not least because the computing power required for artificial intelligence is becoming more and more affordable. Moreover, the potential of AI goes far beyond autonomous trucks; it also offers many benefits when it comes to analyzing and forecasting warehouse activities. In future, it’s entirely possible that artificial intelligence will enable us to predict what products customers will order next week. “Simulations are a powerful tool for recognizing patterns,” explains Peter Golz. All of us make plenty of spontaneous purchases, but a significant proportion of our spending is entirely predictable. “For instance, there are foods that we always want to have in our cupboards or fridge,” says Maik Manthey. “For me, that’s milk. If I placed an order with a supplier for some other products, they could, theoretically, use an algorithm to predict that I would need milk as well, and send me that, too.”

The insights gained from this kind of predictive modeling are already having an impact on our urban spaces. “Warehouses are moving nearer to customers and becoming smaller,” explains Peter Golz. From a logistics perspective, bricks and mortar shops are a very attractive option because, by putting their shopping in their basket, the customer takes responsibility for one of the most time-consuming aspects of intralogistics, namely the picking process. Should customers prefer to use an online click & collect service and collect their shopping in person, the picking process can also be completed by an automated micro-fulfillment center such as those provided by Dematic .

In the city, warehouses are moving nearer to customers – like the automated micro-fulfillment center provided by Dematic.

Making Optimal Use of Resources

Predictive technologies can also help reduce travel times and make journeys more efficient. According to Peter Golz: “The transport systems in every major city are on the brink of collapse and the only solution is to completely rethink our approach to transport.” This is an area where autonomous vehicles could make a real difference. In many sectors, there’s no reason why workers couldn’t vary their arrival time in the office based on the current traffic situation. “You could decide you need to be in the office by 10:00 at the latest and then book a driverless taxi to get you there,” suggests Peter Golz as one alternative. “Ideas like this would open up a new approach to individual choice.” What’s more, with traffic analysis software and the resulting data, such a system is theoretically possible.

Likewise, it’s worth looking at whether certain journeys within cities could be combined as part of a shared service in order to reduce the number of vehicles traveling empty. This approach would mirror the systems used in warehouses, where there are defined areas each responsible for specific tasks. “The primary aim of any warehouse is to make optimal use of its resources,” says Peter Golz. “You want to achieve the best outcome with minimal investment in hardware.” Ultimately, that’s exactly what comprehensive warehouse management systems, such as those offered by the KION Group, are designed to do. Urban areas need to set themselves similar goals if they want to overcome challenges such as traffic congestion and environmental damage.

Reducing Traffic Volumes

There’s no denying that when it comes to change, warehouses have one huge advantage over cities: They can be redesigned, even rebuilt, to meet new requirements. Cities, on the other hand, require much more long-term planning and you don’t have influence over all the players involved, not least the residents, some of whom may have different priorities. Urban areas are also disadvantaged when it comes to the network coverage. “GPS is not yet accurate enough for autonomous vehicle navigation,” explains Maik Manthey. Widespread Wi-Fi coverage is also not available in many cities.

That said, it is possible to use data from navigation systems. This is an area where IT specialists in the warehousing sector have lots of experience. “Forklift guidance systems manage traffic volumes using heat maps. A similar system could also be used in cities to ensure that not all cars or lorries are funneled down Main Street, for example.” Recently we have seen more and more cities opting to close roads to traffic or even create traffic-free districts. In 2020, the Rue de Rivoli, one of the main thoroughfares into the center of Paris, was closed to traffic with the exception of cyclists. Likewise, certain districts of Barcelona are closed to all vehicles apart from residents and delivery companies, who must follow a 10 km/h speed limit. And the centers of Houten in the Netherlands and Ferrara in Italy are both designated car-free zones.

If data from navigation systems were exchanged, guidance systems could manage the traffic volume at rush hour using heat maps.

“Ultimately, it’s all about networks”

Finally, let’s take a look at the issue of energy management. When using electric vehicles, you need to make sure they can be recharged at the right time. No other industry in the world uses as many electric vehicles as the warehousing sector, so the industry is well placed to advise in this area. As Maik Manthey explains, the key question is: “How do I make sure that, at the end of the day, the whole fleet comes back to base with enough power and I don’t have two trucks with empty batteries and two still with full power.” Charging breaks are just half the answer; there are many other factors to consider, not least which truck transports which package and when, how can the driving routes be optimized, and how is the workload best shared between the trucks. According to Maik Manthey “The more data you have and the more skilled you are at combining different data to refine and enrich your data output, the better the results.”

There are lots of levers to juggle, in both intralogistics and urban logistics. “Ultimately, it’s all about networks,” says Peter Golz from Dematic. “We see the warehouse as its own network, and together different warehouses build bigger networks.” It is these networks that you have to analyze, coordinate, and manage. “Engineers break complex problems down into smaller tasks and find a solution for each task,” continues Maik Manthey. “They then put everything back together.” Whether the complex problem is a large warehouse or a city, the approach is exactly the same.

Learn more in our latest podcast

In the last episode, we talk to our guests Maik Manthey (KION Group), Brigitte Strathmann (City of Osnabrück) and Andreas Löwe (Podcast "Irgendwas mit Logistik") about "what the city can learn from the warehouse": What concepts are needed for the last mile? Where is logistics headed? Where does technology help - and where perhaps not?


Read more articles from our series on Urban Logistics - What cities can learn from warehouses .


Markus Schmermund about solutions that intralogistics may have for the last mile.