Industrial organizations have more operational data than ever. Yet understanding the operation often still depends on dashboards, spreadsheets, meetings and individual experience.
We started ValueTwin around a simple idea:
If we can model the operation itself, we can bring data and analysis closer to the way the operation actually works.
Sophisticated conceptual image of engineers and operations professionals looking at a visual representation of a complex industrial operation. Communicate curiosity, understanding and systems thinking. 16:9.
Companies have invested in collecting data. They have invested in ERP, MES, SCADA, BI and other systems.
But the existence of data does not automatically create operational understanding.
The difficult part is often connecting:
That is the problem we work on.
Sophisticated conceptual bridge between fragmented industrial data sources and clear visual understanding of an operation. Disconnected data sources on one side, connected operational model on the other. 16:9.
Our work sits at the intersection of:
We use these disciplines to create analytical experiences around the systems people are actually trying to operate and improve.
how it works
the relationships
the relevant data
what is happening
what changed
where possible
This is the thinking behind ValueTwin.
Sophisticated visual sequence of six stages: Understand, Model, Connect, Analyse, Explain, Improve. Integrated with a real industrial operation. 16:9.
A number becomes more useful when its operational context is visible.
Performance often emerges from interactions between different parts of the system.
Organizations should be able to build on the technology and data they already have.
Complex systems become easier to investigate when their structure can be seen.
We use technology where it helps make operational understanding and decision-making better.
We want to make it easier for people responsible for operations to understand the systems they run.
Not by adding another layer of reporting.
But by bringing together:
Whether you are trying to understand a bottleneck, improve asset performance, connect fragmented data or explore a new operational analytics use case, we'd like to understand the problem.
