Industry is facing a new, additional and perhaps even bigger challenge – energy. When cheap energy is no longer available around the clock – as it was just a few years ago – it is clear that manufacturing companies will have to work around the availability and price of energy in the future. That’s a pretty simple concept to grasp – but is it simple to implement, too? “That depends on how transparent and coherent the data from plants, products and production processes is,” says Sebastian Seitz, CEO of Eplan and Cideon, the software companies of the Friedhelm Loh Group. As he explains, measuring energy flows isn’t enough. If you want to optimise processes, you need a high level of transparency and have to put data into the right context. That applies to energy flows as much as it does to production processes and all the related data. “For instance, before the energy consumption data for machines and plants can provide any useful insight as a parameter, it first needs to be put into the context of load profiles. Fully digital plant data provides a crucial building block for that,” points out Seitz. To work out precisely where adjustments need to be made, all production processes need to be fully transparent and linked to data from energy monitoring.

The only way to achieve that is with smart production – even in Haiger. After all, the idea that enclosures are a simple, mass-produced product is not quite accurate. Some 8,000 of them leave the plant every day. The system platform comprises more than 100 standard options. Once you factor in special designs that customers order with customised cut-outs, for example, the number of options becomes virtually infinite. The factory generates up to 18 terabytes of data each day. This information comes from various sources, all of which need to be interlinked in several data spaces to ensure the transparency, flexibi - ity and efficiency of sma t production.

CONNECTING DATA SPACES

It all starts with the products. Every workpiece must have a high-quality and complete digital twin. The information for this comes from several types of software, such as CAD, PDM/PLM and ERP or configur - tors. To ensure all this data can be used as a digital product twin, it needs a shared data space. The experts at Cideon work on the connections and interfaces. It is a similar scenario for plants. “The processes in panel building, switchgear manufacturing and mechanical engineering can already be digitalised and automated to a high standard. The skills shortage is driving this development forward,” says Seitz. Eplan and Rittal support processes with coordinated hardware and software, and supply the data space basics for an automation ecosystem. The result is that a digital twin is created in engineering right from the getgo, continues to grow in its shared data space throughout the whole process, and subsequently comes into its own as a digital automation twin in the factory.

ONCITE DPS BRINGS DATA TOGETHER

Additional sources of data from all levels of the systems – from the overarching ERP to the basis of machine control (PLC) – are also fed into the data space for production processes. “In Haiger, the ONCITE DPS digital production system pieces together this diverse and varied data along with process information almost in real time,” explains Bernd Kremer, COO Digital Industrial Solutions at German Edge Cloud. The result is transparency for all processes. Information from the data spaces for products, production plants and production is displayed on dashboards in the workshops to show precisely where each production step is currently running, the quality levels being achieved and where interventions are required. “It is a high-performance tool for immediate manual optimisation. More importantly, it is the basis for increasingly wide-reaching automated responses to sudden changes in manufacturing conditions. Right at the top of the staircase leading to smart production is the fully self-regulating ‘lights-out factory’.”

EXPANDING DATA SPACES

What about energy? “The open architecture of ONCITE DPS based on microservices has helped us integrate data from energy monitoring into the system, even though that was a completely new requirement. Correlating that with data from the other data spaces doesn’t just give us transparency over energy flows, but also helps us interpret them in the context of production,” says Kremer. Where do expensive load peaks occur? Does the production process allow us to run machinery on a phased basis? How do changes to the settings and temperatures of the paintshop impact consumption? What is the energy consumption and, therefore, carbon footprint of each workpiece? Connecting together data spaces can provide answers. “As you work towards becoming a smart factory, that is the basis for introducing energy consumption as another parameter and being able to respond to energy availability forecasts with flexibilit ,” points out the production and IIoT specialist. “High levels of digitalisation in as many factories as possible – including energy management – will help grid operators manage their distribution networks as smart grids in the future.” And what is needed to achieve that? Even more overarching data spaces.