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# L0 — Production Enterprise
The top-level enterprise context for the production organisation.
Typical concerns:
- production portfolio;
- plants and sites;
- high-level KPIs;
- safety, quality, environmental and financial targets;
- enterprise data governance.

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process:
id: "oipf.l0.production-enterprise"
level: "L0"
name: "Production Enterprise"
purpose: "Represent the enterprise-level production context in which operational intelligence is deployed."
inputs:
- production strategy
- plant portfolio
- business KPIs
- compliance obligations
outputs:
- production intelligence scope
- enterprise-level operational objectives
children:
- "oipf.l1.production-domain"

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# L1 — Production Domain
Defines the domain context for production operations. The generic library remains industry-neutral, but domain packages can specialise it.
Examples:
- food and beverage;
- chemicals;
- pulp and paper;
- water and wastewater;
- pharma;
- metals and materials.

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process:
id: "oipf.l1.production-domain"
level: "L1"
name: "Production Domain"
purpose: "Represent a domain such as food production, chemical processing, water treatment, pulp and paper, pharma, or metals."
inputs:
- plant scope
- domain-specific production constraints
- asset classes
- regulatory context
outputs:
- domain process model
- domain package selection
parent: "oipf.l0.production-enterprise"
children:
- "oipf.l2.production-line-operations"

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# L2 — Production Line Operations
Represents a production line, unit operation, or operational area.
This level maps real plant systems into OIPF structures:
- asset hierarchy;
- tag mapping;
- material and energy flows;
- operating states;
- events;
- procedures;
- constraints.

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process:
id: "oipf.l2.production-line-operations"
level: "L2"
name: "Production Line Operations"
purpose: "Represent a line, unit operation, or production area where operational intelligence is applied."
inputs:
- asset hierarchy
- tag list
- historian data
- operating modes
- events
- procedures
outputs:
- mapped operational context
- selected optimisation use case
parent: "oipf.l1.production-domain"
children:
- "oipf.l3.operational-intelligence-loop"

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# L3 — Operational Intelligence Loop
The reusable intelligence loop:
```text
Observe → Contextualise → Infer → Check → Recommend → Approve → Act → Record → Learn
```
This is the central reusable process pattern of the library.

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process:
id: "oipf.l3.operational-intelligence-loop"
level: "L3"
name: "Operational Intelligence Loop"
purpose: "Convert contextualised production data into governed recommendations and learning feedback."
pattern:
- observe
- contextualise
- infer
- check
- recommend
- approve
- act
- record
- learn
parent: "oipf.l2.production-line-operations"
children:
- "oipf.l4.recommend-act-record-learn"

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# L4 — Recommendation-to-Outcome Work Task
This level describes the concrete work task by which an AI/ML/physics model recommendation becomes a governed operational action.
The key output is not only the action. It is the recorded outcome, because outcome feedback allows the intelligence loop to improve.

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process:
id: "oipf.l4.recommend-act-record-learn"
level: "L4"
name: "Recommendation-to-Outcome Work Task"
purpose: "Execute a human-in-the-loop recommendation cycle and record the measurable outcome."
inputs:
- model output
- recommendation
- constraint result
- operator approval
outputs:
- action record
- measured outcome
- audit event
- feedback signal
parent: "oipf.l3.operational-intelligence-loop"