The most transformative technology in manufacturing right now is not a robot, not a 3D printer, and not a large language model. It is the digital twin — a real-time virtual replica of a physical system that can simulate, predict, and optimise operations before they happen in the real world.
Digital twins are not new. The concept originated at NASA in the early 2000s and has been used in aerospace and defence for over a decade. What is new is their accessibility. The convergence of affordable IoT sensors, cloud computing capacity, and AI-driven simulation models has brought digital twin technology within reach of mid-market European manufacturers who, five years ago, could not have contemplated the investment.
I am watching this space closely through NexaTech Ventures because it sits at the intersection of several trends we back: AI applied to physical systems, deep domain expertise as competitive advantage, and European industrial strength translating into technology leadership.
What a Digital Twin Actually Does
A digital twin is not a static 3D model of a factory floor. It is a living, continuously updated simulation that mirrors the physical system it represents in real time. Sensors on physical equipment — temperature, vibration, pressure, energy consumption — feed data into the digital model, which uses AI to identify patterns, predict failures, and test operational changes in simulation before they are implemented physically.
The practical applications are substantial. Predictive maintenance is the most mature: a digital twin that learns the normal operating patterns of a machine can detect the early signatures of component degradation weeks before failure occurs. For a manufacturer whose production line generates fifty thousand pounds of revenue per hour, the difference between an unplanned shutdown and a planned maintenance window is the difference between a catastrophic loss and a minor schedule adjustment.
Process optimisation is the next frontier. A digital twin can simulate hundreds of production configurations — different machine speeds, material feed rates, energy inputs — and identify the optimal setup for a specific production run. The energy savings alone are typically in the range of ten to fifteen percent. The quality improvements — fewer defects, less waste — compound the economic case.
Why Europe Is Leading This Quietly
Europe’s manufacturing base is the quiet giant of its economy. Germany, Italy, France, and increasingly Ireland and the Nordics have manufacturing sectors that combine engineering precision with a willingness to invest in process improvement. This culture of continuous improvement is the natural breeding ground for digital twin adoption.
The European companies building digital twin platforms are not Silicon Valley transplants trying to sell generic software to factories. They are engineering-led teams with deep domain expertise in specific manufacturing verticals — automotive, pharmaceuticals, food production, energy equipment — who understand the physical systems their software is modelling.
This domain expertise is the competitive moat. A digital twin for a pharmaceutical manufacturing line must understand the specific physics and chemistry of the processes it is modelling, the regulatory requirements that govern production quality, and the idiosyncrasies of the equipment installed in European pharmaceutical plants. A generic platform built in California cannot replicate this without years of domain immersion.
The Investment Case
The digital twin market is projected to grow substantially over the next five years, driven by both cost reduction imperatives and regulatory pressure for energy efficiency and emissions reporting. European manufacturers facing the CSRD reporting requirements need granular, real-time data on energy consumption and emissions per production unit — exactly the data that digital twins generate.
At NexaTech Ventures, the digital twin companies that interest us most share three characteristics. First, they have deep vertical expertise — they understand the specific manufacturing processes they are modelling, not just the software architecture. Second, they have established relationships with manufacturing customers who are generating the real-world data needed to train and validate the models. Third, they have a clear path to platform economics — starting with a single use case (typically predictive maintenance) and expanding into process optimisation, energy management, and quality control within the same customer.
The companies that build defensible positions in vertical manufacturing digital twins will be among the most valuable industrial technology companies in Europe by the end of this decade. The transformation is quiet, but it is real, and it is happening now.
Scott Dylan is the Founder of NexaTech Ventures. He writes on technology investment, AI, and European industry. Read more at scottdylan.com.
