PROCESS INTELLIGENCE
Operational DNA learns from reviewed experience.
In 420+, Process Intelligence describes the path from selected findings to substantively assessed operational knowledge. Experience is retained with its provenance, context, and scope. Responsible people assess which statements the observations support.
Experience develops operational DNA.
LEARNING WITH CONTEXT
A finding becomes a clear learning task.
In the described workflow, Process Mining hands over an analytical finding. The responsible roles define a learning task from it: What should be investigated, under which conditions, and against which assessment criterion?
420+ uses operational DNA to describe the versioned knowledge and learning model that connects operational insights to their provenance, context, and scope. The Context Model structures the people, objects, activities, rules, and states that make a specific operational situation understandable in substantive terms.
Knowledge Graphs can provide a representation for such knowledge, including provenance and revisions. Operational DNA here denotes operational knowledge and learning logic; it does not imply an additional limit of the graph model.
In the illustrative example on the Mining page, material group B occurs together with a higher result. The specific target metric and relevant safeguard metrics must first be defined for a trial. The finding alone establishes neither a cause nor a change to the material requirement.
LEARNING TASKHow does the result change when material group B is used under comparable conditions?
REVIEWED OPERATIONAL EXPERIENCE
Actual histories provide the learning and training foundation.
In process learning, the organization defines what may be changed during a trial and which comparison conditions apply. Process Intelligence supports the subsequent interpretation of results within the operational knowledge model.
The organization defines what is deliberately changed and which conditions must remain comparable.
Product, batch, material, SOP version, execution, conditions, measurements, standardized images, and result remain connected.
Machine learning therefore does not come first: only suitable, substantively assessed histories create a reliable foundation for organization-specific models.
Change material
Keep conditions comparable
Test the effect in new histories
VERSIONED OPERATIONAL DNA
Reviewed experience develops the knowledge model.
Substantively assessed experience can confirm, constrain, or extend the model, or refute a statement. Provenance, context, evidence, and scope remain part of the assessment. Versioned Knowledge Models distinguish changed versions from their activation.
The knowledge model connects not only a result, but also its provenance, context, evidence, and known scope.
Confirmed, disproven, and only partially valid relationships refine what is already known and where its limits lie. Machine-learning models built on this foundation can be trained with suitable histories and tested against new data.
This gradually creates a versioned model of how products, materials, processes, conditions, and results interact within this organization.
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01
LEARNING TASKDefine the relationship
Which relationship should be tested using new comparable histories?
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02
REVIEWED EXPERIENCEAssess the history in context
Change, context, history, and observed result remain connected.
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03
REFINED KNOWLEDGE MODELDevelop operational DNA
The experience confirms, limits, or extends the operational knowledge model.
EXPLORE THE KNOWLEDGE BASE
Operational context as usable knowledge.
Further articles show how concepts, relationships, conditions of applicability, model changes, and operational experience are structured and reviewed.
NEXT STEP
Operational DNA creates value in the work itself.
A maintained knowledge model can provide suitable experience for operational work. Assessment status and scope remain decisive during retrieval: an open assumption must not appear as an approved requirement.