Manufacturing & Industrial

The plant already knows. The boardroom finds out later.

Manufacturing generates more operational data than any sector we work in, and loses more of it before anyone can act. That is an architecture problem, not a sensor problem.

Talk to us about manufacturing Use cases that matter
Where this is going

Three shifts already underway.

Not predictions. Things we watch changing inside estates we work in, and what each one asks of the platform underneath.

Now

Localization raises the evidence bar

Industrial localization is shifting plants from output targets to performance and traceability targets. Reporting that satisfied a production manager does not satisfy a regulator or a global customer.

What it means: the number now has to be defensible, not just available.
Next 18 months

Agents move onto the line

Predictive maintenance, vision quality and shift copilots are leaving pilots and entering operations, where a wrong answer costs output rather than credibility.

What it means: permissions and audit trails become production requirements.
Structural

The edge and the cloud stop competing

Control systems answer in milliseconds, enterprise platforms in minutes. The estates that win run both and reconcile them, rather than choosing one and losing the other.

What it means: context, not connectivity, becomes the hard part.
The gap

Where the value leaks.

The same three gaps appear everywhere we work. This is what they look like here.

The data gap

Machine and plant-floor data sits in historians, PLC logs and spreadsheets, structured for the system that produced it rather than the question anyone wants to ask. Two lines report the same metric and mean different things by it.

Consequence: no two plants can be compared honestly.

The intelligence gap

Downtime, scrap and energy losses surface days after the shift that caused them. By then the crew has changed, the batch has shipped and the cause is a memory.

Consequence: decisions made on last week.

The oversight gap

Models get deployed against operational data with no lineage, no classification and no record of approval. It holds until someone asks the platform to justify a decision it already made.

Consequence: AI that cannot be defended.
From plant floor to decision Five layers of industrial data: machine signals at ten milliseconds, control systems at one second, edge normalization sub-second, a governed lakehouse in minutes, and operational decisions taken within the shift. The gap most plants never close sits between the control layer and the lakehouse. Machine PLC, drives, sensors 10 MS Control SCADA, historian, MES 1 SECOND Edge OPC UA, Modbus, MQTT SUB-SECOND Lakehouse Governed, contextualized MINUTES Decision OEE, downtime, yield, energy THIS SHIFT The gap most plants never close. The signals already exist. Shared context, governance, and one agreed definition of OEE do not. From plant floor to decision Five layers of industrial data: machine signals at ten milliseconds, control systems at one second, edge normalization sub-second, a governed lakehouse in minutes, and operational decisions taken within the shift. The gap most plants never close sits between the control layer and the lakehouse. Machine 10 MS PLC, drives, sensors Control 1 SECOND SCADA, historian, MES The gap most plants never close. The signals already exist. Shared context, governance, and one agreed definition of OEE do not. Edge SUB-SECOND OPC UA, Modbus, MQTT Lakehouse MINUTES Governed, contextualized Decision THIS SHIFT OEE, downtime, yield, energy
Scroll to see the full picture
The use cases that matter

Where the value concentrates.

A plant can chase two dozen initiatives at once. In practice three of them carry most of the return, and all three fail for the same reason. Here is what each actually asks of the data underneath it.

USE CASE 01
OEE that survives a comparison between two plants
What it actually needs

One downtime reason code list, one agreed ideal cycle time and one definition of a good unit, applied across sites that each evolved their own over a decade.

Without the foundation

Both plants report 87%. Neither number can be added to the other, ranked against it, or acted on by group.

How you know it worked

Group can rank lines and nobody disputes the basis.

USE CASE 02
Predictive maintenance that names the right asset
What it actually needs

An asset hierarchy that ties sensor tags to the physical machine, and survives rebuilds, relocations and tag renames.

Without the foundation

The model predicts a tag. Maintenance is dispatched to the wrong equipment, and within a month nobody trusts the alerts.

How you know it worked

Model-raised work orders get closed as valid rather than cancelled.

USE CASE 03
Defect detection you can actually act on
What it actually needs

A defect linked to the product, batch, material lot and process settings in force at the moment it was produced.

Without the foundation

You detect defects accurately and still cannot say what caused them, so nothing changes upstream.

How you know it worked

Time from detection to a corrected process setting, measured in shifts.

Our position: the order matters more than the list. Defect detection built on inconsistent product master data produces confident nonsense, and predictive maintenance without asset context predicts the wrong machine. In the readiness session we map the use cases you want against what your foundation can carry today, and we tell you which ones are not reachable yet rather than quoting for them.

What we bring

How we close it here.

The same architecture, sequenced for what this sector actually runs on.

Foundation

Governed lakehouse for operations

One governed platform where OT signals, enterprise systems and contextual data land together, with ownership and classification settled before the first workload.

Delivered on Lakeora Asas.

Connectivity

Plant floor to lakehouse

Integration that brings machine, line and site data in real time, contextualised with product, shift, batch and asset so a fault code becomes an answerable question.

Protocol-level work, not a nightly extract.

Intelligence

Operational intelligence and OEE

Downtime, waste, quality, throughput and energy measured consistently across lines and plants. OEE programmes fail on inconsistent downtime codes and disputed cycle times, not on modelling.

Definitions settled before dashboards.

Use cases

Predictive operations

Prioritised use cases deployed onto the governed platform rather than into a sandbox, each scoped against hours, scrap or energy recovered.

Sequenced against foundation readiness.

Agents

The industrial AI workforce

Purpose-built agents for the shift, the line and the excellence team, running on shared definitions so six of them cannot produce six contradictory answers.

See the workforce model below.

Ownership

Capability transfer

Enablement so plant IT and the central data team can extend the platform and onboard sites without waiting on us.

Runbooks handed over.

Industrial AI

Six jobs. Three altitudes. One definition of the truth.

The operator at the machine, the supervisor on shift and the excellence team comparing sites ask different questions on different clocks. They only help each other if all three read the same numbers.

The industrial AI workforceSix jobs grouped by altitude: asset, site and enterprise level, all resting on one governed foundation.Asset levelOperators, techniciansSECONDSShift handoverWhat changed, what needs escalation, what the nextteam picks up.Machine co-pilotTroubleshooting at the machine. Local inference, nocloud round trip.Site levelSupervisors, line managersTHIS SHIFTProduction lossWhere the hours went, and what to correct first.Quality investigationScrap and variation traced to a cause, with theevidence attached.Enterprise levelExcellence and DX teamsWEEK ON WEEKCross-site benchmarkingWhy the same line runs better in one plant thananother.Compliance evidenceAudit-ready packs built from what the platform alreadyrecorded.ONE GOVERNED FOUNDATIONSame definitions, same lineage, same access controls, at every altitude.The agents are the easy part. Making them agree is not. The industrial AI workforce Six jobs grouped by altitude: asset, site and enterprise level, all resting on one governed foundation. Asset level SECONDS Operators, technicians Shift handover What changed, what needs escalation, what the next team picks up. Machine co-pilot Troubleshooting at the machine. Local inference, no cloud round trip. Site level THIS SHIFT Supervisors, line managers Production loss Where the hours went, and what to correct first. Quality investigation Scrap and variation traced to a cause, with the evidence attached. Enterprise level WEEK ON WEEK Excellence and DX teams Cross-site benchmarking Why the same line runs better in one plant than another. Compliance evidence Audit-ready packs built from what the platform already recorded. ONE GOVERNED FOUNDATION Same definitions, same lineage, same access controls, at every altitude. The agents are the easy part. Making them agree is not.
Scroll to see all three altitudes
Acting safely

Autonomy is a foundation problem.

On a plant floor an agent does not just answer. It holds a batch, raises a work order, moves a setpoint. Every one of those steps reaches down.

Autonomy is a foundation problemA manufacturing agent loop: sense, ground, decide, gate, act, with every action recorded. Each step draws on a governed foundation of context and lineage, access and permissions, quality and confidence, and an audit trail.EVERY ACTION RECORDED, WITH ITS REASONTHE GOVERNED FOUNDATIONContext and lineageAccess and permissionsQuality and confidenceAudit trailLine 3 stoppedSenseWhich product, shift, lotGroundLikely cause, confidenceDecidePermitted? Who signs?GateWork order, setpoint, holdActAn agent without this is not autonomous. It is unsupervised. Autonomy is a foundation problem A manufacturing agent loop: sense, ground, decide, gate, act, with every action recorded. Each step draws on a governed foundation of context and lineage, access and permissions, quality and confidence, and an audit trail. EVERY ACTION RECORDED, WITH ITS REASON Line 3 stopped Sense Which product, shift, lot Ground Likely cause, confidence Decide Permitted? Who signs? Gate Work order, setpoint, hold Act THE GOVERNED FOUNDATION Context and lineage Quality and confidence Access and permissions Audit trail An agent without this is not autonomous. It is unsupervised.
Scroll to follow the loop
Next step

Which of these are reachable today?

That is the question the readiness assessment answers. We map the use cases you want against what your platform can support now, and tell you which need foundation work first.

Request your assessment All sectors
Also relevant
  • Lakeora Asas. The governed Databricks foundation, deployed in days. See how it works
  • Databricks services. The delivery catalogue behind every engagement. Explore