From the physical plant to its virtual twin
DIENERTEX has developed digital twin prototypes for energy-intensive processes — fiber-shredding and recycling lines, spinning, finishing and laminating — capable of accurately reproducing a plant’s real energy and production behavior. The key isn’t just the technology, it’s how it’s used: these models let teams simulate production, maintenance or investment decisions before touching a single machine, avoiding costly trials and unnecessary downtime.
ITE’s technical team puts it well: the models “reproduce energy-production behavior with an adequate degree of accuracy,” translating into better-informed, data-driven decisions. That’s not a small detail. With energy costs remaining one of the biggest pressures on industrial margins, being able to anticipate the impact of a decision before executing it changes the game.
A pattern repeating at global scale
What’s telling is that DIENERTEX isn’t an isolated case — it’s the local confirmation of a trend already showing up in major sector reports. Cisco’s State of Industrial AI Report 2026, based on more than 1,000 operational technology leaders across 19 countries, found that 61% of industrial organizations have already moved AI into live operations — not pilots — with energy optimization and sustainability ranking among the highest-traction use cases, alongside predictive maintenance and process automation. 83% of companies plan to increase their industrial AI investment, and 87% expect tangible results within the next two years.
Taken together, the local case and the global snapshot point in the same direction: industrial digitalization has stopped being a promise for the future and become a present-day management tool. And its raw material is always the same: well-structured energy, production and environmental data, able to feed both a diagnosis and a simulation.
Why this matters for industrial engineering
Beyond the immediate savings, DIENERTEX points to something with more runway: laying the groundwork for a sector-wide data environment that supports carbon footprint calculations, decarbonization plans, or the adoption of ecodesign criteria. In other words, the same data that cuts an energy bill today will be the documentation required by European sustainability regulation tomorrow. Whoever starts structuring that information now isn’t just saving money — they’re arriving prepared for what’s coming.
For those of us working every day at the intersection of engineering, industrial processes and sustainability, cases like this confirm a core conviction: there’s no energy transition without a data transition. The question every industrial company should be asking isn’t whether to digitalize, but how fast it can turn its operational data into decisions.
Does your organization have the data it needs to build its own digital twin, even at a small scale? We’d love to hear about your experience in the comments.
