Operational excellence Unfold the blind spots in your production process – even on legacy equipment – and raise productivity end to end. The opportunity Industries must continually raise productivity to keep pace with market demand and competition. Individual machines have already been optimised in isolation, making further ROI harder to achieve. Contextualising the process across an end-to-end, integrated data layer enables data science and machine learning to model the next productivity gains. Much of the installed equipment is old enough that it was never built to generate the data points needed to understand the context. Blind spots prevent a full view of cause and effect across the production process, and retrofitting sensors is expensive and may not deliver ROI – particularly for proprietary vendor software that does not integrate with other platforms. The models capture the process know-how of experienced staff. It stays in your company and keeps quality consistent – even when people leave. How it works Individual machines are digitally mature, but the dependencies and interactions in the overall system are rarely addressed. The architecture Connectivity and data collection, a Unified Namespace as Baseline Digital Twin, soft sensors that combine artificial intelligence with your process knowledge – and applications from performance intelligence to predictive maintenance. Data sovereignty and process expertise remain with you. Technology partners One language for everyone The Unified Namespace structures the data; the ontology on top gives it meaning – turning fragmented, heterogeneous sources into a model every stakeholder can read, not only data scientists. Vendor-neutral by design. Soft sensors via the models Data science and process knowledge fill the blind spots in the digital twin, and the learned pattern transfers across a fleet – including older, sparsely-instrumented assets. Potential Once the blind spots are closed, data science can model the productivity gains. Detect wear and failures before they stop production. Automate what is understood – step by step. Make energy consumption visible and reduce it. Optimise the production process end to end. Make process knowledge available to everyone who needs it. Related solutionsProductivity with data science
Integrated processes are the biggest remaining source of productivity
What stands in the way
A welcome side effect
From isolated machines to soft sensors
From IoT data to applications in your plant
Enabled by proven technology
Cryptic machine tags become readable measurements for everyone
From isolated sub-systems to soft-sensor-based control
What it delivers in your plant
Predictive maintenance
Automation
Energy savings
Process optimisation
Knowledge through LLMs
What often comes next
Productivity with data scienceClaude Code2026-10-07T15:43:30+00:00
