Moving in the Right Circles
Reading time: approx. 15 min.Moving in the Right CirclesClosed-loop manufacturing paves the way for an efficient flow of data: from the customer to production, through development and quality assurance. Limitless possibilities are now becoming part of the norm.
CHAPTER 1 – Consistently Digital
CHAPTER 2 – Live Broadcasts for Customers
CHAPTER 3 – Productivity in the Fast Lane
CHAPTER 4 – Battling Knowledge Silos
CHAPTER 5 – A Future Topic of the Automotive Industry
CHAPTER 6 – Real-Time Correction in the Pressing Plant
CHAPTER 7 – The Machine Interpreters
CHAPTER 8 – A Mammoth Training Task
CHAPTER 9 – Ready for Closed-Loops?
CHAPTER 2 – Live Broadcasts for Customers
CHAPTER 3 – Productivity in the Fast Lane
CHAPTER 4 – Battling Knowledge Silos
CHAPTER 5 – A Future Topic of the Automotive Industry
CHAPTER 6 – Real-Time Correction in the Pressing Plant
CHAPTER 7 – The Machine Interpreters
CHAPTER 8 – A Mammoth Training Task
CHAPTER 9 – Ready for Closed-Loops?
CHAPTER 1 – Consistently Digital
CHAPTER 1Consistently DigitalThe Porsche factory in Leipzig manufactures customized vehicles on a series production line. Around 580 brand-new Macan and Panamera models roll off the assembly line every day, yet not a single car is like the others. Each one has been individually configured by the customer and precisely scheduled for production by the order system: Within seconds of receiving the order, the system checks the production capacity, books a slot on the production line in Leipzig, plans deliveries for vehicle-specific materials and calculates a precise pick-up date. This real-time information enables full data integration, or in other words a perfect production ecosystem. Specialists call it closed-loop manufacturing.
“The digital factory landscape is a fully synchronized system that functions without any extra tables, lists or emails,” explains Fabian Troll, Head of Vehicle Control at Porsche Leipzig. The 35-year-old industrial engineer heads a team of 12 employees responsible for managing the factory’s workflow. This encompasses overseeing incoming orders, planning weekly unit quantities, monitoring production and handing vehicles over to the sales team or directly to customers. “The logistics team also receives a forecast well in advance so that the flow of parts can be organized with the suppliers,” says Troll. Troll accentuates that customers can still make changes to their orders up until to two weeks before the start of production, despite the precise nature of the scheduling process. Operational changes that cause delays can also be reported immediately, mitigating risks through data driven insights.
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Thanks to the closed data chain at Porsche Leipzig, all systems and employees from the body construction team through to the final inspection team know exactly what they need to do and consider for each vehicle. The information for the next stages of production are available at each production station, exactly, when it is needed. The factory’s system is used to document every process that is completed on each vehicle, meaning that the Porsche Leipzig team can see, at any point, what route the vehicle has taken through the factory. And therefore verify compliance with the applicable quality standards. The system includes energy monitoring functions for each individual machine: Using “digital twins,” preventive maintenance instructions are created in advance based on the real-time energy consumption of each machine. “This approach helps us to continuously optimize our workflows and reduce downtime,” explains Troll.
CHAPTER 2 – Live Broadcasts for Customers
CHAPTER 2Live Broadcasts for CustomersAlthough these data loops may sound inconsequential, they are in fact the result of years of meticulous development that has ultimately blown up old software-related boundaries in various disciplines due to data standardization. As a result, accessing information is now also much simpler. The system has been perpetually enhanced for over of ten years – and is still being optimized today.
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To move away from a silo mentality, information is available to all coworkers via the Porsche Information System (PISA), which functions as a company-internal Wikipedia. Aside from efficiency, PISA champions transparency as all departments share information. “PISA helps us make decisions faster,” says Troll. The foundation for this smart system was already being put into practice when the Leipzig factory opened in 2002. The decision was to organize the factory-specific IT landscape so that all data generated by the factory was accessible was made back then. Since then, our IT department has ensured that all software systems can communicate and verify information in a meaningful way. “We created this data integration ourselves, which makes us a pioneer in the industry,”says Fabian Troll.
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CHAPTER 3 – Productivity in the Fast Lane
CHAPTER 3Productivity in the Fast LaneThe Leipzig factory is a good example of how closed-loop manufacturing unites the world of sales, development and production by combining systems and interface configurations. It is a new manufacturing culture based on the Industry 4.0 concept – and one that offers great benefits. Real-time integrated IT reduces the number of development stages, saves time and reduces production costs. In addition, knowledge becomes more readily available, optimizing overall service, which ultimately benefits the customer.
However, the transition from the old world of drawing boards to the data streams of the Industry 4.0 is a long path, as Siemens manager René Wolf knows only too well. As Senior Vice President of Manufacturing Engineering Software at Siemens Digital Industries, the 50-year-old physicist is responsible for developing and delivering integrated closed-loop manufacturing systems for international customers. Siemens is the leader in this segment, as rated by analysts such as Gartner. “Whether it was a new car, sneakers or a yogurt pot, traditionally every product was designed on a drawing board, then developed using CAD. Next, the materials were tested and then the production process was set up and tested. But nobody knew exactly what happens to the user with the product, whether they liked it or how the product behaved,” says Wolf. He points out that a product can now be designed digitally, and then simulated, tested and put into operation virtually, based on just the initial idea. The customer can also be involved right from the start. The goal is for even highly complex, smart products to be customized, developed and manufactured in line with unique customer requirements with very little effort.
The virtualization center run by Bausch+Ströbel, a manufacturer of customized packaging systems in the pharmaceuticals industry, is an industry best practice: Where at the site in southern Germany, every detail of a machine is developed and constructed digitally - before being built. Using 3D glasses, engineers and customers work together to virtually optimize the manufacturing process until all of the relevant requirements have been fulfilled. All the improvements are incorporated into the design, and the data is used in other parts of the production chain. This is another example of closed-loop manufacturing. A few years ago, the Bausch+Ströbel carpentry shop produced full-size wooden models of machine designs so that the company could test mechanical properties, ergonomics and workflows before production. The new approach improves efficiency and reduces production time.
CHAPTER 4 – Battling Knowledge Silos
CHAPTER 4Battling Knowledge SilosAccording to Wolf, filling information and data gaps in the value chain is one of the greatest challenges when developing a digital production control system. “We are always surprised by how dependent many companies are on individual senior professionals”. Closed-loop manufacturing can break down knowledge silos that are inevitably develop to a bidirectional flow of information. With the help of end-to-end systems, production data from all company sites can be accessed centrally and compared to engineering data. Within a very short time it is possible to verify whether and at what site, how quickly and at what price a product can be manufactured. As a result, the production capacity of multiple sites can be aligned within a company.
There are plenty of other ways how this approach can be used. For example, a vehicle can send error messages and other data to the manufacturer or service center, which provides additional feedback about the product. What can be improved if the error occurs in other vehicles? Is there potential for improvement? “The data generated by product lifecycle management can be used immediately to modify the production line,” says Wolf. In parallel, the data from the production line can be fed back to the engineering team. This interaction can significantly shorten development cycles as there is a closed loop between development, production and the service.
Siemens has developed its own closed-loop systems for products and production at its electronics factory in Amberg, Germany. The factory manufactures programmable machines that control systems used in the automotive industry and even in ski lifts. Dashboards display all performance, product and quality data in real time, allowing the company to quickly respond to changing circumstances. The next big step will see the integration of artificial intelligence into the production cycle.
CHAPTER 5 – A Future Topic of the Automotive Industry
CHAPTER 5A Future Topic of the Automotive IndustryFor Markus Junginger, Partner at MHP, closed-loop manufacturing is a topic that will continue to engage the automotive industry and other industries for many years to come. In the future, information loops throughout the value chain can massively reduce waste and reactive power while also reducing costs. “We are always surprised that the route between product development and production is still a one-way street,” says Junginger, who is also an economist and expert in behavioral economics.
Junginger points out that experienced production employees have a great deal of knowledge; they know the machines inside out and understand parameters such as the ideal temperature of an oven or the optimum torque for tightening a screw. And yet the development engineer does not have access to this knowledge. According to Junginger “They work in silos as if they were separated by the Great Wall of China. This separation means that the product development process begins from scratch every time, rather than building on existing knowledge.” In comparison, closed data cycles share information as they rely on the expertise and experience of everyone involved. “Like a thermostat that responds to every tiny temperature fluctuation in the surrounding environment, a company must learn from experience to prevent misguided developments,” explains Junginger. This is exactly why an OEM would want to know how its vehicles are functioning at any given point in time. Digitalization within the automotive industry allows this flow of information and makes it possible to explore new ways of thinking. “The very essence of digitalization is our ability to create closed loops across the entire value chain, from creation to disposal,” says Junginger. “There are no excuses for a silo mentality anymore.” As Junginger points out, the technological solutions for preventing this from happening already exist.
However, the steps and phases that a manufacturer must traverse to close the digital value chain are all very different as each company’s unique manufacturing processes use different technology. Some companies still use old systems that are very analog and are barely capable of Internet connectivity; others have already started to digitalize and must now fit all these pieces into digital networks like a puzzle. Depending on their qualifications, staff must also get used and trained to utilize new technologies.
CHAPTER 6 – Real-Time Correction in the Pressing Plant
CHAPTER 6Real-Time Correction in the Pressing PlantInterference-free data flows and autonomous systems are a research specialism of the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation in Karlsruhe, Germany. Dr. Olaf Sauer, the Vice President of the institute, is responsible for automation and supports large projects run by engine and automobile manufacturers such as Daimler and BMW, who wish to make better use of data pools.
Take for example, the forming quality in a pressing plant. The project involves the body parts being automatically measured in three dimensions once the sections have been formed by a steel roller in the pressing plant. The smallest deviations are then reported directly to the machines, so that they can correct them independently. This approach generates a closed data loop in the pressing line with one huge advantage. The next pressed part can be produced at a higher quality level and it no longer takes hours for a fault to be corrected. “The factory of the future must be capable to manufacture without deficiencies as well as manufacture customized products in ever new variants at the same times as enabling fast delivery times and saving resources,” says Sauer. According to Sauer, there are several standalone technologies available for this purpose; the challenge now is to bring them all together in a way that suits different industries. The most important hurdle to overcome is the initial step of networking all the systems within a company. As technology often stems from different generations with differing standards, it means that retrofitting old systems can be difficult. “Establishing any kind of connectivity between an ensemble like this is developmental mastery in practice,” says Sauer.
CHAPTER 7 – The Machine Interpreters
CHAPTER 7A Mammoth Training TaskHowever, standardized plugs and sockets are by no means enough to ensure a smooth flow of data. After all, every system has its own way of communicating, so to function machines and systems must learn to understand each other. “It’s no use if one machine spits out a measurement that the other does not understand,” explains Sauer. “Our role is to be a kind of interpreter for the machines.” Factories are already starting to rely on service-oriented standards from manufacturers and platform-independent machine languages, such as the OPC Unified Architecture, to create order among the chaos of programming languages. However, the interfaces are a long way from being unified on an international scale.
“While a laptop and a printer within a WIFI network can now understand each other independently and download printer drivers automatically, unfortunately it doesn’t work like that with Industry 4.0 yet unfortunatly,” says Sauer. “This is one of our goals for the next few years.” However according to Sauer, one of the biggest challenges for data exchange with Industry 4.0 are human beings. “Until now, factories have functioned as a result of the foreman holding his ear against a machine and knowing exactly what it’s doing. And the late shift does everything differently compared to the early shift. The expert knowledge was important for the company – and for the employees to keep their jobs.” But with more and more sensors and the exponentially increasing computing power of IT systems, old limitations are being removed. This trend means that knowledge is no longer stashed away by an individual foreman – becoming available to all. In turn, employees must adapt to changing work processes: Continuous professional development is the new norm in the world of work.
CHAPTER 8 – A Mammoth Training Task
CHAPTER 8A Mammoth Training TaskClosed data loops are set to bring about another change in the future. When maximum precision and error-free production by machines is possible, there is growing economic temptation to outsource processes from within the company to third parties. Companies such as Protolabs in the UK are already producing prototypes and made-to-measure small series. All Protolabs needs from its customers is a digital drawing of the required product, which can be submitted online. “It’s only natural that people are afraid of being replaced,” says Sauer. He believes that companies must focus on properly training their employees to cope with the ever increasing amount of data. Training of this nature is complex. As there are only a limited amount of people with the cross-sectional skills needed to be able to perform a role between mechanical engineering and IT. “It’s a major challenge for universities,” says Sauer, but he remains optimistic stating that “We won’t run out of work any time soon.”
CHAPTER 9 – Ready for Closed-Loops?
CHAPTER 9 Ready for Closed-Loops?To make closed-loop manufacturing a reality, several technological enablers are required, which are dependent on the current state of the respective factory’s production process: Machines and systems, as well as workpieces and products, must be equipped with digital technologies and integrated into digital infrastructures. Software and systems engineering, interface technologies and cloud computing can be used to improve networking, along with big data paired with meaningful data analysis. Virtual, real-time simulation of factory processes also has a role to play. The advantages for customers are significant. Thanks to closed-loop manufacturing, customers benefit from a noticeable saving in time and costs - due to faster development and production processes that use fewer resources. More transparent information flows are also an advantage, as it provides the opportunity to manufacture more customized products – just like Porsche in Leipzig with its customized vehicles. Where cars are manufactured to a precise schedule determined by when the order is received. In addition, closed data loops enable practical experience with new products to be integrated into further product improvements much faster than before.
As such, the vision of achieving complete data integration and intelligent control of the entire production process is inevitably linked to with profound changes within the company that is manufacturing a product. What sounds like a dream of the future for some is already a reality for others.
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Text:
Sven Heitkamp
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Moving in the Right Circles
Consistently Digital
“The digital factory landscape is a fully synchronized system that functions without any extra tables, lists or emails,”
Live Broadcasts for Customers
“We created this data integration ourselves, which makes us a pioneer in the industry,”
Productivity in the Fast Lane
Battling Knowledge Silos
What can be improved if the error occurs in other vehicles? Is there potential for improvement?
A Future Topic of the Automotive Industry
Real-Time Correction in the Pressing Plant
A Mammoth Training Task
A Mammoth Training Task
Ready for Closed-Loops?
As such, the vision of achieving complete data integration and intelligent control of the entire production process is inevitably linked to with profound changes within the company that is manufacturing a product. What sounds like a dream of the future for some is already a reality for others.
More information:
