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Industrie 4.0

The smart secret of big data

Much of today’s logistics landscape would not be possible without big data. But big, on its own, is no longer enough. Data also needs to be smart.

23 Oct. 2015
RFID_Transport_Smart Data

"Big data" is a simple-sounding term for a number of highly complex processes, methods and technologies designed to convert vast volumes of data into useable knowledge. It starts with capturing and storing data and proceeds through various stages including analysis. Most importantly, it ends with presenting the analysis results in a readily intelligible and useable form. In other words, you have to understand the data before you can derive value from them and turn them into a competitive advantage.

What does this mean for logistics?

Digital integration of the entire supply chain is fundamental to achieving the fully automated value chain envisaged by Industry 4.0. According to Sascha Schmel , President of Germany’s VDMA Materials Handling and Intralogistics Association, intelligent, digitized systems are the way of the future. He anticipates that ongoing integration and automation will bring sweeping changes to all supply chain processes and associated fields of work.

The management consulting firm KPMG predicts that about 60 percent of all value chain stakeholders will eventually buy into big data. That is still a long way off, however, as a recent survey by PwC shows . Only 35 percent of the transportation and logistics-sector CEOs surveyed by PwC indicated that the IT areas of their organizations were ready to make the changes necessary to capitalize on transformative global trends. 43 percent of this CEO group expressed concern at the speed of technological change. But change is coming, and there is no stopping it.

End-to-end integration

Vehicles are a major source of smart big data. Traffic management systems, for instance, use crowdsourced data from sat nav systems and smartphones to manage traffic volumes. Among much else, these data enable digitally networked smart trucks to adjust their routes in real time in response to changes in the traffic situation and calculate the most efficient capacity utilization. And that’s just the beginning. In May of this year, Daimler Trucks revealed the world’s first self-driving truck to be licensed for use on public roads .

Intralogistics vehicles are following a similar trajectory. Gunter Van Deun is Product Manager for autonomous guided vehicle systems at the German intralogistics company Egemin. In his view , "logistics demands are increasing all the time, which is why intra-logistic automation solutions not only have to map the client’s material handling 100 per cent, but also have to be as flexible as possible. Only in this way can the systems remain future-proof." Egemin has developed an AGV production and testing facility where it puts its products through a range of customer-specific test scenarios prior to delivery. As a result, its products are optimally tailored to the intralogistics and materials handing requirements of its customers.

Goods are just as important as vehicles in terms of creating and using smart big data. For example, goods equipped with RFID chips can automatically capture and relay data on their current position and temperature and even carry their own delivery instructions. RFID scanners can then automatically and contactlessly track and record goods movements within the factory or warehouse and feed the information through to inventory management systems.

And when it’s time for dispatch, the IT system can scan the chip on each pallet and automatically trigger an error alert if a consignment is loaded into the wrong truck. This reduces the error rate in dispatch operations to virtually zero. Each truck has sensors, a GPS transponder and is connected to the operator’s IT system, so once it has left the warehouse, it continuously monitors the condition of the goods it is carrying and reports back on its current position.

The challenge of analysis

The ultimate aim is for smart, integrated supply chains to interconnect seamlessly with intelligent Industry 4.0-style production systems to create a self-organizing, dynamic value-adding network. This presents a major challenge for logistics providers seeking to supply Industry 4.0 companies: they need to be able to access and accurately analyze vast streams of data. And that’s no easy task, because more data means exponentially more sources of error. "When ten details from environmental sensors are linked to ten details relating to traffic volume this does not result in 20 new data records, but is instead multiplied to produce 100 new pieces of information, which can all be interpreted in a variety of new ways," explains Deutsche Bank Research analyst and digital economy expert Thomas Dapp . "This increases the complexity of the newly created data set and the requirements made of it enormously, with the inherent danger of trying to detect patterns in the underlying analysis that do not exist."

Increased vulnerability to technical failures

Digital integration and automation enable suppliers and distributors to operate on tighter schedules and with lower inventory levels. However, this also means that technical disruptions and responses to those disruptions can have far-reaching and unforeseeable knock-on effects throughout the entire supply chain. That’s because bottlenecks and disruptions require manual intervention, and the problem with manual intervention in highly complex, integrated systems is that it generally makes things worse.

A German research consortium is currently developing a big data platform called "ProveIT" to solve this problem. The platform will work by giving employees all the information they need to make risk-free interventions in highly networked logistics systems. It will do this by gathering all the relevant data from the entire production and supply network and combining it with sales forecasts for the products affected. Ultimately, it will pinpoint exactly where employees need to intervene in order to rapidly restore the compromised network to its proper operating state.

The researchers anticipate that they will know by the fall of 2016 whether the platform is up to the task of capturing and intelligently analyzing the inevitably huge volumes of data it will encounter.

But why wait until then? Find out all about smart data in logistics at CeMAT, which runs from 31 May to 3 June 2016.

Book your ticket now!

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