Pharma & CPG · Manufacturing Excellence

Decision Ontology for Manufacturing Excellence

Every plant decision, reading from the same floor.

Purpose-built for GMP-grade shop-floor reality, not generic analytics.

Unified Context Causal Self-Learning

One Enterprise Brain

One brain, not six systems.

An MES, a QMS, a CMMS, and a WMS can each be excellent on their own, and still leave scheduling, quality, and maintenance teams working from different versions of "what's happening on the floor." The ontology is what replaces "six systems, six truths" with one.

Point Solutions Today

Each system holds its own recipe version, tries to hand off to the next, and drops it at the edge.

vs

One Ontology

Same six layers, one shared state. Every area, and every function, reads and writes the same record.

Same decision, two ways.

Three failure modes point solutions can't fix, and how a connected ontology fixes each one.

Causal Reasoning

Why did yield drop on Line 3?

Point solution

The MES shows a lower yield number. Changeover logs, maintenance history, and supplier material data sit in different systems, so nobody can trace why.

Connected ontology

The ontology traces it: a late supplier shipment (Supplier & Network) forced a rushed changeover (Line & Process) right as a machine's OEE was already declining (Asset & Maintenance). Full causal chain, not a yield number.

Grounded Applications

A schedule that couldn't actually run

Point solution

A scheduling app optimizes for capacity alone, blind to a live quality hold or a supplier delay.

Connected ontology

Built on the ontology, the same app inherits quality, maintenance, and supplier state automatically. The schedule it proposes is always executable, not just efficient on paper.

Surfaced Blind Spots

Deviations nobody linked to maintenance

Point solution

A batch deviation and a maintenance work order on the same line the week before never get connected. Quality and maintenance systems don't share a record.

Connected ontology

The ontology surfaces the correlation on its own, flagging that deviations cluster right after specific maintenance events. A blind spot becomes a leading indicator.

How It's Built

Built by agents, not a project.

The ontology isn't a one-time modeling project. Three kinds of agents build, maintain, and act on it continuously.

Always on, not a one-time build

1

Data Agents

Ingest & reconcile

Continuously ingest and reconcile data regardless of shape, MES tables and sensor telemetry as easily as scanned batch records, handwritten deviation logs, technician notes, and supplier PDFs. They resolve mismatched batch IDs, inconsistent units, and unstructured shift-floor data without a manual mapping project.

2

Ontology Agents

Model & maintain

Take what Data Agents reconcile and build the six layers themselves, inferring relationships, resolving the same batch or asset across systems, and flagging drift as recipes, schedules, and supplier terms change, so the ontology stays current without a re-modeling cycle.

3

Decision & Application Agents

Act & orchestrate

Orchestrate the systems that act on the ontology, a scheduling engine, a predictive-maintenance alert, a batch-release check. They can be schedule-triggered, event-triggered, or rule-triggered, and every trigger checks live ontology state first, so no action fires against stale or partial data.

Structured & unstructured floor data Data Agents reconcile Ontology Agents model Six living layers Decision & Application Agents orchestrate

The Ontology

All Plant Relationships & Causality, Mapped.

Six functional layers, the full ontology stack behind every plant decision, not a subset.

Data Foundation

Any format. Any mess.

Format comes first: tables or handwritten notes, clean or messy, Data Agents handle it. The systems below are just examples, not a checklist.

Structured

Tables & feeds

Tables, feeds, and structured records.

SAP PP/MM BOM tables MES/ERP schedule tables SCADA / PLC historian data LIMS results EBR data QMS workflow status IoT telemetry CMMS work orders MES production logs SRM / procurement tables WMS / TMS records EDI 856/210

Unstructured

Documents & free text

Documents, notes, scans, and free text.

Scanned recipe cards Batch record templates Shift-change texts Labor availability emails Operator downtime notes Shift handover logs Deviation investigation reports Inspection photos Scanned signature pages Health authority filing letters Technician notes Work-order comments Shift reports Changeover checklists Vendor quote PDFs Negotiation emails Cold-chain logger printouts Carrier exception emails

The Four Decisions

Different decisions. Same six layers.

Scheduling & Make-vs-Buy, Line Balancing & Yield, Quality Control & Batch Review, and Predictive Maintenance & Network each draw a different combination from the same ontology, no area waits on a separate data pull.

Decision 01

Scheduling & Make-vs-Buy

The order book, floor capacity, and supplier lead time combined into one view, so a make-vs-buy call and a schedule change are checked against the same numbers.

Reads from

Schedule & Capacity Ontology

Line & Process Ontology

Supplier & Network Ontology

Decision 02

Line Balancing & Yield

Cycle time and scrap data sitting together, so a line rebalance accounts for the yield impact before it's approved, not after.

Reads from

Line & Process Ontology

Yield & Waste Ontology

Decision 03

Quality Control & Batch Review

Deviation history, batch genealogy, and sign-off status resolved to the same batch record, so release decisions aren't waiting on three separate reviews.

Reads from

Line & Process Ontology

Quality & Compliance Ontology

Supplier & Network Ontology

Decision 04

Predictive Maintenance & Network

Asset condition checked against the live schedule and the broader plant network, so a maintenance window gets planned before a breakdown forces it.

Reads from

Schedule & Capacity Ontology

Asset & Maintenance Ontology

Supplier & Network Ontology

Agent Library

Decision & Application Agents, purpose-built for Manufacturing Excellence.

Pre-built agents that read directly from the six-layer ontology, ready to deploy, not built from scratch.

01Decision Agents

Reasoning primitives, reusable across every workflow.

02Application Agents

Purpose-built manufacturing workflows.

Architecture

From data to decision.

One flow, four tiers, agents run the first two so the ontology stays live without a manual pipeline.

Structured + Unstructured

MES / ERP tables

Deviation reports & photos

Technician notes

Vendor PDFs

Data + Ontology Agents

Reconcile & resolve

Model six layers

Keep state current

Flag drift

Area Layer

Scheduling & Make-vs-Buy

Line Balancing & Yield

Quality Control & Batch Review

Predictive Maintenance & Network

Outcomes

Production plan

Line rebalancing

Batch release

Maintenance windows

Governance & Trust

Built for GMP data.

Every layer, every agent action, and every recommendation is traceable, access-governed, and explainable by default.

01

Full batch genealogy

Every value traces back to its source system, batch, and timestamp, no black-box joins.

02

Role-based access

Scheduling, quality, and maintenance teams see the layers relevant to them, governed at the ontology level.

03

21 CFR Part 11 aligned

Electronic batch record sign-off handled to the standard batch release requires.

04

Explainable outputs

Every recommendation, and every Data Agent or Ontology Agent action, shows which layers and records it drew from.

Decision Ontology for Manufacturing Excellence

See your plant data here.

We'll walk through your current systems and show where the six layers slot in.

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