Connected Data as the Backbone of the Digital Factory

Building digital factories through a continuous data foundation

Digital-factory ambitions are often framed around artificial intelligence, automation, efficiency, and resilience. Yet the underlying issue is more basic: whether information can move reliably across the full life of a facility. When planning, engineering, construction, production, and maintenance operate through disconnected tools and files, teams can lose time reconciling versions, recreating models, or acting on stale information. Those interruptions can limit the practical value of advanced technology.

A connected data platform is presented as a way to establish continuity between those functions. Rather than treating factory development and operations as separate stages, it can create a shared information base that supports decisions from early design through day-to-day performance management. The potential uses include layout planning, simulation, digital twins, and analysis of operating conditions. The source notes that 95% of data generated during factory construction goes unused, illustrating the scale of knowledge that may fail to reach later stages.

Eaton’s example focuses on facilities before production begins. The power-management company upgraded energy-management software to support building digital twins, converting fragmented information into interactive 3D representations of factory environments. Visualization and predictive analysis were then used to support asset monitoring, maintenance planning, operational-risk reduction, and building performance. The case highlights how data collected for a facility can remain useful when it is structured for ongoing operations.

Porsche faced a different challenge in developing its Taycan factory in Stuttgart: an urban site with tight requirements for space, speed, and performance. The company introduced design, engineering, and building-information-modeling workflows to coordinate the facility, production systems, and construction process. According to the source, these workflows reduced construction risk, increased flexibility, and improved the facility’s value. The shared model also gave designers, planners, and suppliers a common basis for coordination.

For Viessmann, speed to production was the priority. The HVAC manufacturer planned a heat-pump plant in Legnica, Poland, with a two-year window from initial planning to production. It used an AI-powered 3D planning model as a single source of truth, allowing partners to work with current plans while bringing architecture, construction, assembly, and logistics into one process. The stated benefit was not only fast construction but closer collaboration among groups with distinct responsibilities.

The source also describes Bridgestone’s expansion of an initial six-month automation effort into a continuing transformation program. With a common data foundation, factory improvements became more cumulative, helping the company address changing products, capacity requirements, and supply-chain pressures while reducing waste and rework.

For manufacturing leaders, the immediate task is to identify where information is reentered, translated, or lost. Closing those gaps can make automation and AI more useful by giving them a more complete operational context. The digital factory, in this view, is less a single technology deployment than a sustained effort to connect data, workflows, and decisions.