Filling knowledge gaps – this is the goal of digitalized logistics. To get there, more data than ever before has to be collected. The challenge: drawing the right conclusions.
In the past, logistics on a global scale functioned as follows: A distributor accumulates goods in their warehouse. If the warehouse is full, a shipping company that can transport the goods must be found. Once the ship has left port, a period of anxious waiting begins. Will the cargo arrive undamaged? Will the crew mutiny and take control of the ship? Will the ship arrive at its destination or sink in a storm?
Whatever the end result was, the main problem with logistics was always the same: great uncertainty about what was happening while the goods were in transport, and what might happen before they arrive at the destination. The problem: Out of sight, but not out of mind – more by necessity than by intention.
That’s how logistics worked in the past, and this fundamental problem didn’t change for several thousand years: Up until a few decades ago, freight companies sent out trucks and ships and waited for the news that they had arrived safely. And today? That’s all in the past, and everything is different now. Networked data carriers are making it easier to get answers. From optimizing container loads to monitoring the condition of the goods in transport to the delivery time, suppliers and freight forwarders have never known as much as they do today.
Supply chain: More and more extensive, more and more difficult
And yet, the age of digitalization in logistics has only just begun. The mobility of tomorrow has the potential to optimize supply chains and make them more efficient. There are some obstacles to overcome before this is achieved, however. Roughly speaking, the core problem of every supply chain, namely getting the right parts to the right place at the right time, becomes more critical the longer the chain is.
A look at automobile manufacturer BMW gives an idea of the scale of the planning and organization necessary. 1,800 suppliers in 4,500 locations move a volume of 84 million cubic meters each day. And speaking of the shippers mentioned earlier: BMW dispatches 7,000 sea freight containers every day.
Such an extensive and diversified supply chain must still be flexible enough to handle unforeseeable events and changes. For an automobile manufacturer, this might be the ability to get any required part to any place in the world within 24 hours in emergency situations. This also means having the greatest flexibility possible for orders. And it also means that the various international documentation requirements must be complied with at all times. To get into a position where the above is possible, many companies are setting up numerous data points, each providing information on the status of a specific project.
How are suppliers and forwarders networked with each other, and how are they connected to the shipper? What risks are there in the supply chain – and how can they be managed? For example, the catastrophe caused by the 2011 Japanese tsunami shut down a manufacturer of color pigments, causing a global supply shortage. The reason: the company’s plant was heavily damaged during the natural disaster, and was only 25 miles away from the Fukushima nuclear power plant. The result: automobile manufacturers across the world urgently needed to find a substitute for the paint manufactured by this company.
Making the data talk
The trend in the automotive industry is now moving towards continuous and uninterrupted analysis of each link in the supply chain using interconnected data sources. Yet data collection alone does not guarantee success. The real challenge is drawing the right conclusions from the available data. This also means that the volume of goods transported might go down in the future, such as when maintenance, service, and repair work done within a company is possible worldwide using augmented reality and virtual reality technology. In the long run, this could enable the logistics industry to make automation and artificial intelligence play a much greater role than today.
Autonomous ships in 2030?
But the mobility of the future will also force freight forwarders and transport companies to make big changes. For example, the market-research company Frost & Sullivan considers autonomous ships by 2030 to be an entirely realistic scenario. This comes at a time when other links in the logistics chain – from cranes and robots in production halls to self-driving vehicles and drones for assembly lines – have long since become automated.
The awarding of contracts for freight transportation will also change. For example, Uber, the bane of taxi drivers, has been working on platforms which assign freight to trucks that are the most readily available at the necessary time. The logistics platform Freightos has a similar approach, offering transportation services with transparent freight rates and prices on the internet.
The mechanism behind this is always the same in this age of digitalization: Everyone knows where their goods are at all times and can react flexibly to peak periods. The uncertainty about the status and arrival date of goods that was practically inescapable in the past is now just a thing of the past. Uncertainty nowadays more likely results from the question of whether or not the most fragile links in the supply chain can be found – and improved by data analysis.