Yes, but the safest approach is usually not to replace legacy KPIs outright. In most plants, you map them into a new taxonomy by creating a governed crosswalk between old and new metric definitions, then running both reporting models in parallel for a defined period.
If you try to force a clean cutover too early, reporting disruption is common. The problem is rarely just naming. Legacy KPIs often differ in formula logic, event timing, aggregation rules, exclusions, master data quality, and source systems. Two metrics can look equivalent on a dashboard and still produce materially different numbers.
Inventory the current KPI set. Document each metric’s business purpose, formula, unit of measure, data source, refresh timing, owner, and known exceptions.
Define the target taxonomy separately. Do not start by renaming old metrics. First define the new standard terms, calculation intent, hierarchy, and reporting grain.
Create a KPI crosswalk. For each legacy KPI, classify the mapping as one-to-one, one-to-many, many-to-one, partial match, or no direct match.
Record semantic gaps explicitly. If a legacy plant metric excludes planned downtime but the enterprise KPI does not, that is not a minor detail. It must be documented as a calculation difference, not hidden in a label change.
Use a translation layer. In practice this is often a semantic model, reporting layer, data mart, or governed middleware mapping that lets existing reports continue while the new taxonomy is introduced.
Run in parallel. Keep legacy reports operating while publishing comparison views that show old KPI values, new KPI values, and the reconciliation logic.
Set retirement criteria. Decommission legacy metrics only after owners agree on variance thresholds, exception handling, and change control.
The key is backward compatibility. Existing reports, scorecards, and management routines usually depend on metric continuity. Instead of changing those assets first, preserve their inputs and outputs while adding metadata and mappings behind the scenes.
That often means:
keeping legacy KPI identifiers stable during transition
adding new taxonomy IDs and aliases alongside them
versioning definitions and effective dates
tracking which reports still consume legacy logic
reconciling variances before executive roll-up changes
In regulated and highly controlled operations, this matters beyond convenience. Metric definitions can affect investigations, batch or lot review context, supplier management, CAPA trending, and audit evidence packages. If a KPI changed meaning but the report history does not show when and why, traceability suffers.
Assuming same label means same metric
Ignoring differences in time buckets, shift calendars, or work center hierarchies
Mapping before master data is normalized
Letting each plant interpret the new taxonomy locally without governance
Changing dashboards before validating source data and reconciliation logic
Dropping legacy metrics that still feed ERP, MES, QMS, or customer reporting
Brownfield environments make this harder. Many plants have KPI logic split across MES, ERP, historian, spreadsheets, BI tools, and local databases. A full reporting replacement often fails because integration debt, validation effort, downtime constraints, and long-lived operational dependencies are underestimated. Coexistence is usually the lower-risk path.
metric definitions and formula versions
source-system precedence rules
effective dates for mapping changes
report ownership and approval
exceptions and local plant variants
validation and regression test results
If your environment is subject to formal change control, the KPI taxonomy and mapping rules should be handled like any other controlled configuration. That does not mean every dashboard change requires the same treatment, but where metrics support quality decisions, release evidence, or regulated records, validation scope and approval rigor may be higher.
If the goal is continuity, do not ask whether each legacy KPI can be renamed. Ask whether it can be translated without changing business meaning, historical comparability, or evidence integrity. If not, keep it as a legacy metric, map it as a non-equivalent or partial-equivalent term, and phase change more slowly.
The result is usually a staged model:
preserve current reporting
publish the crosswalk and target taxonomy
run parallel reporting and variance analysis
retire or consolidate metrics only after sustained reconciliation
That approach is slower than a forced standardization exercise, but it is usually more reliable and far less disruptive.
Whether you're managing 1 site or 100, C-981 adapts to your environment and scales with your needs—without the complexity of traditional systems.
