You handle KPI changes by versioning the KPI definition instead of silently replacing it. If the formula, denominator, source system, event timing, unit of measure, inclusion or exclusion rules, or data latency changes, treat that as a new KPI version. Keep the old version available for prior periods, mark the effective date of the new version, and make the break in comparability explicit.
The main rule is simple: do not rewrite history unless you can fully and reliably restate history from raw source data under the new definition. In many plants, that is not realistic because historical source data is incomplete, event semantics changed over time, or legacy systems do not retain the needed detail.
What usually works
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Maintain a governed KPI catalog with version numbers, owner, business purpose, formula, source systems, grain, exclusions, and effective dates.
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Store KPI results with their definition version attached so each reported value is traceable to the exact logic used.
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Show trend charts with a visible change marker at the cutover date.
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When possible, run old and new definitions in parallel for a limited period to quantify the gap.
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If stakeholders need continuity, publish a bridge analysis that explains how much of the change is operational and how much is definitional.
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Require change control and approval before a KPI definition moves into production reporting.
When you can keep a single historical trend
You can sometimes preserve a continuous trend if the change is cosmetic or mathematically neutral, such as a label cleanup, presentation formatting, or a source field rename with identical meaning and validated mapping. You may also be able to restate history if you have retained raw, time-stamped source data at the necessary level of detail and can prove the transformation is reproducible.
That proof matters. In regulated operations, a restatement should be documented, reviewable, and reproducible. Otherwise, you risk creating a cleaner-looking chart that is less trustworthy than an explicit break.
When you should split the metric
Split the trend or create a new KPI version when the change affects business meaning. Common examples include:
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Changing what counts in the numerator or denominator
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Moving from manual entry to automated event capture
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Changing aggregation grain from line to work center, order, batch, or lot
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Switching source systems, such as spreadsheet to MES, or MES to ERP-derived reporting
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Changing cut-off logic, time zone handling, or late transaction treatment
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Adding or removing rework, scrap, downtime classes, suppliers, or product families
In those cases, a single uninterrupted trend line can be misleading.
Brownfield reality
In mixed MES, ERP, PLM, QMS, historian, and spreadsheet environments, KPI changes often break trend analysis because the underlying event model was never standardized in the first place. Two systems may both report yield or downtime while meaning different things. This is why a canonical metric layer, business glossary, and mapping rules are usually more important than a dashboard refresh.
Full replacement is often not the practical answer. In long-lifecycle, regulated environments, replacing core systems just to standardize KPIs can fail because of validation effort, qualification burden, downtime risk, retraining, and integration complexity. A more realistic approach is to govern metric definitions above the existing systems and improve source alignment incrementally.
Tradeoffs
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Versioning preserves trust and traceability, but it can make executive dashboards less visually simple.
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Restating history improves comparability, but only if source data quality and lineage are strong enough to support it.
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Parallel runs improve confidence, but they add temporary reporting overhead.
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A strict governance process reduces KPI drift, but it can slow metric changes that business teams want quickly.
If you need one practical policy, use this: any KPI change that alters business meaning gets a new version, an effective date, documented rationale, and either a parallel-run bridge or a clearly marked trend break.