Use case: multi-site KPIs # Compare a KPI across sites, calculated the same way everywhere In Aditum Data a KPI is defined once, with an owner and a version, and each site maps its own tag names, units and meters to it. A new site is a mapping, not a new project, and every site's figure is calculated the same way. Facts checked 24 October 2026 ## Why do cross-site comparisons take so long? Because the logic is tied to one site's tag names, units and meters, so it gets rebuilt for the next site. Site A calls the steam meter one thing, site B another, and site C reports in a different unit. Each comparison becomes a spreadsheet exercise, and nobody is sure the numbers mean the same. ## How does one KPI definition work across sites? Write the KPI once, then map each site's tags to it; any exception is approved and recorded. - The definition has an owner and a version, so a changed formula is visible and can be compared with the old one. - Every step is checked with unit algebra: mixing units that don't fit is refused with a suggested fix. - Averages are time-weighted, so deadbanded and report-by-exception tags give correct numbers. - Two sites with the same tag names are kept apart, and each site can have its own time zone. ## Dashboards for every site A board built for one site can be copied to another by rules, without AI, and gets a site selector. - A tile with no single equivalent at the other site is left out, with the reason shown. - Sister assets (rigs, units, lines) can be ranked on a bar chart with statistical outliers marked. - Every number on a board answers "How was this made?" with its source tags and definition version. ## How do we know a number is right? Saved pipelines run without AI, so the same input always gives the same output. You can check any dashboard figure against your own SQL reference. Each KPI result also states which exclusion rules it applied. ## Questions **Do all sites need the same historian?** No. Each site connects its own sources (for example OPC UA at one site, AVEVA PI at another) and maps them to the same KPI definition. See [connectors](https://aditumdata.com/connectors). **Can we keep the results in our own data platform?** Yes. Results can be delivered to Snowflake, Databricks, PostgreSQL, Azure Data Lake and other destinations, or shown on dashboards in Aditum. ## Read next - [Energy and emissionsCalculate specific energy per tonne, energy per batch and steam properties from historian tags without code, the same way at every site.](https://aditumdata.com/use-cases/energy-emissions) - [No-code calculations (f(x))Build engineering calculations on any tag with blocks or a formula: LMTD, heat duty, OEE, specific energy, mass balance, SPC, with unit checks on every step.](https://aditumdata.com/use-cases/no-code-calculations) - [Aditum Data vs Cognite Data FusionCognite Data Fusion is an industrial knowledge-graph platform; Aditum Data is a fast path from plant sources to comparable KPIs and dashboards, and it can work alongside CDF. Sourced comparison.](https://aditumdata.com/compare/cognite-data-fusion) ## Discuss architecture with an industrial data specialist. Source compatibility, security boundaries, deployment and multi-site operation. Or request a 45-minute technical session to select one KPI and three sites and draft the acceptance criteria. [Book a technical demo](https://aditumdata.com/#contact)[sales@aditumdata.com](mailto:sales@aditumdata.com)