By: Wolfgang Schloegl and Christian Heck
We’re surrounded by digital manufacturing software, and we use extremely complex products every day. But we don’t think about the effort it takes to design and manufacture these products.
Take cars, for example. We use cars throughout our daily life, and we don’t really think about how much of a technical marvel they actually are.
Large companies with many departments and suppliers manufacture cars, and they do so by using numerous software programs and databases. The very term ‘product lifecycle management’ includes the software needed to design complex products such as cars, as well as to plan their manufacturing, while being able to simulate the products in every phase of their lifecycles.
The most pressing problem with creating a fully functional car is with the amount of complexity involved. Complexity steadily increases with product variety, market demands and new technology, such as electric cars, or how the Internet of Things interacts with your car.
You also must consider the demands you place on your personal connected devices and which devices already have an Internet connection, which could add to the car’s complexity if they connect to your car to play music, provide navigation instructions or gather information on your car’s performance.
Fulfilling these demands is a huge task if you want to successfully build a car. If you try to fulfill those demands using traditional organizations and today’s digital manufacturing technology, you will fail. Those organizations and software will be overwhelmed because they can’t handle the challenges and intricacies of this complexity headed your way.
How are companies supposed to deal with all of the complexity that comes with today’s digital manufacturing software? If you look at all of the departments in companies completing work for the products you buy, you see that even though organizations may be on the same tasks, few know how to use deeper data integration and digitalization to complete that work much more efficiently.
Ideally, everything should be commonly engineered and thoroughly tested before actual production begins. This engineering and testing can be completed today, but it’s mostly done by people in different departments who use their own tools, often with weak interfaces. Some organizations are taking a partial digital manufacturing approach, but crucial domains, especially electrics, automation and mechatronics validation, are still separate from everything else in these organizations.
None of this will help you as you move deeper into the digital manufacturing age.
Digital manufacturing software and the smart factory
To understand why this process is still so complicated, and the value a new level of digital manufacturing software could bring to smart factories, we need to look at the beginnings of digital manufacturing technology.
The use of digital manufacturing software became more widespread around the year 2000. As the digital factory was first coming to be, companies were looking for ways to safeguard their factory planning and production planning, and they wanted to predict how profitable their products would be.
These digital factories, which began using digital technology to create a digital representation of entire physical value chains, began taking off because the software and hardware to handle such a digital factory were, for the first time, mature enough. They could support the increased demand coming from a new level of product and production complexity.
These digital factory software systems are widely used today and generally follow the approach of dividing the planning tasks as “products,” “processes,” “resources” and “plants,” and these structures are stored and managed in PLM systems. On top of this, data models in sophisticated software tools are developed for detailed process, cell, line and factory planning, as well as engineering and simulation.
In recent years, simulation systems’ functionalities have also enhanced to move closer to virtual commissioning. This technology allows a convergence of digital factory models with the real factory; actual factory control software drives the digital models and safeguards the engineering results.
The systems’ development is slowly moving in the right direction, but the progression of digital manufacturing software isn’t happening fast enough. These systems aren’t enough to help companies meet all of the demands they currently face or will face in the future, and companies will need to have agile systems in place to respond not just market fluctuations, but also to customer demands.
This concludes part one of our series on why manufacturers need smart factories to build increasingly complex products. In part two, we discuss the changes you need in your digital manufacturing software so you can have an efficient smart factory.
About the authors
Dr. Wolfgang Schloegl leads the Digital Engineering department in the Siemens Digital Factory division and is amongst others responsible for the software Automation Designer. He also works on providing integrated solutions of Siemens PLM Software with Siemens automation systems. He has been with Siemens since 2003 and previously worked in planning and digital manufacturing for automotive production. Wolfgang was formerly with Daimler in Sindelfingen, Germany, where he worked on knowledge management for production planning, and later, in project management for assembly planning. He studied production engineering at the University of Erlangen-Nuremberg in Germany, where he earned his doctorate in the area of simulation for discrete production.
Christian Heck is a Production Systems Engineering Solution Manager for Siemens PLM Software, a business unit of Siemens Digital Factory Division. He has more than 14 years of experience in automation engineering and commissioning, as well as developing cloud-based industry applications. Heck gained much of his experience working for Siemens Oil and Gas Offshore in Norway, where he worked as System Responsible for several major oil and gas projects. His previous experience with Siemens includes leading a Service Line for optimizing production plant performance based on data analytics applications. Heck holds a Bachelor of Science degree in automation engineering from the University of Cooperative Education in Karlsruhe, Germany.