The analysis of large amounts of data, commonly referred to as “big data”, is an important tool for optimization – also and especially in the logistics industry. In its 2021 Annual Third-Party Logistics Study, the US Council of Supply Chain Management Professionals (CSCMP) reported that 81 percent of the freight forwarders surveyed and 84 percent of third-party logistics providers gave data analysis top priority for the further development of their logistics chains.
However, there is still a significant gap between expectations and reality. According to the German Logistics Association (BVL), only about one third of logistics companies currently use big data analysis. And even in these cases, the technology is used more to optimize inventory – for warehouse management, for example – than to develop new solutions and business models.
There are also convincing examples of this approach. Packaging specialist Tetra Pak, for example, has worked with DHL to map a warehouse in Singapore as a digital twin – a virtual data model. With its help, big data analyses achieved extensive process optimizations. Now Tetra Pak can stow materials in the warehouse within 30 minutes of arrival as well as make them ready for shipment within 95 minutes. As a beneficial side effect, optimized warehouse management also reduces the need to move heavy loads – saving energy and manpower as well as time.
A classic field of activity for data-driven optimization is route planning. As early as 2013, American service provider UPS equipped its vehicles with sensors that register not only the current location but also parameters such as speed, braking, or reversing. These are incorporated into the real-time optimization of routes. This is not just for the sake of punctuality. If just one mile of driving can be saved per day, it leads to a reduction in fuel consumption of around 5.6 million liters per year across the entire company.
Noelia Díaz Bernal, Head of Big Data at DEKRA DIGITAL (also see interview), points out that complexity in transportation processes has increased massively due to unstable fuel prices, driver shortages, growing traffic, and increasing government regulations. “Smart data analysis can counteract these effects. It helps to simplify processes again, thereby reducing resource use and improving profitability.”
The global machine learning market is expected to grow tenfold by 2029
Machine learning is the technological basis for such intelligent data analysis. Fortune Business Insights estimates the annual volume of the global machine learning market at 15.4 billion US-dollars in 2021 and already forecasts growth to 21.2 billion US-dollars for the current year. According to this forecast, the market volume is expected to grow to 209.9 billion by 2029.
Intelligently evaluated data can also be used for quality assurance. For example, Austrian logistics company Quehenberger has implemented a comprehensive asset monitoring solution in collaboration with Californian startup Roambee. IoT sensors in the transport vehicles provide real-time information on the freight’s location, temperature, humidity, pressure, incidence of light, changes in position, and movement. With this cloud solution, Quehenberger offers its customers quality assurance and proactive problem management for perishable goods.
However, the disruptions to global supply chains caused by Corona and other factors have, to a certain extent, also shifted the objectives of many optimizations. Instead of just-in-time processes and efficiency improvements, the focus is now primarily on higher resilience and the expansion of safety reserves. In this context, however, the pendulum must not swing too far in the other direction, emphasizes Franz Staberhofer, Professor of Logistics Management at the Steyr University of Applied Sciences in Austria. He warns companies and their logistics service providers against squirreling away and stockpiling as much as possible.
“That works for the squirrel because it only stores nuts, but not for businesses that store hundreds or thousands of different parts.” Instead, Staberhofer recommends dealing with the volatility of deliveries in a forward-looking manner and using artificial intelligence to identify patterns from the supply chain to customer behavior, and thus look into the future with precision. This offers the best conditions for balancing warehousing and transport logistics even in times of global supply bottlenecks.
The art of big data has always been to aggregate and evaluate data in such a way that truly meaningful and useful insights can be derived from the analysis. “Only then can the identified trends enable strategic conclusions to be drawn for new, data-driven business models,” big data expert Noelia Díaz Bernal also emphasizes and adds that the DEKRA subsidiary DEKRA DIGITAL supports its customers in this challenge. “If data can be analyzed intelligently, it enables our customers to make decisions based on a profound and meaningful database. In that case, the ability to draw strategic conclusions has a major impact on profitability, future viability, and potential competitive advantages. That’s why, simply put, data-driven business models are the future.”