Industrial AI solution

OEE Improvement AI From Tracking to Improving

Stop tracking OEE in spreadsheets. Get real-time loss attribution, downtime prediction and shift-level coaching that actually moves the number.

Plan the use case Explore ROI tools

OEE improvement AI for manufacturing lines

Acceptance before scale

Baseline, representative test set, failure conditions and a named human owner are defined before production approval.

Direct answer

NeoBram delivers OEE improvement AI that automatically attributes losses, predicts upcoming downtime, and coaches shift teams in real time - typically lifting plant OEE 5-15 points within 6 months.

Private deployment available

Why evaluate it

Most Plants Measure OEE. Few Improve It Systematically.

The decision is whether this capability improves a defined workflow safely not whether an AI demo looks impressive.

AI changes that. With MES, SCADA and historian data, AI auto-attributes every minute of loss, detects micro-stops humans miss, predicts the next downtime event, and pushes shift-level coaching with specific recommendations.

We deliver OEE improvement programmes - not dashboards. The AI is one part; the operational rhythm, gemba conversations and CI loop are the other parts we help embed.

Uncited universal benchmarks from the legacy page were withheld. Define the baseline and acceptance threshold from customer evidence or a reviewable primary source.

Candidate capabilities

What a production solution may need to do.

Each capability is validated against representative data and the customer's workflow. Product or model names describe possible components, not partnerships, certifications or guaranteed compatibility.

Real-time OEE & loss attribution

Automatic OEE calculation per line, station and shift with every loss minute attributed to a category and likely root cause.

Per-station OEE updated each cycle
Six-big-losses auto-classification
Micro-stop detection (<3 minute events)
Drift detection vs baseline

Downtime prediction & prevention

Predicts the next likely downtime event by station, with lead time long enough to intervene.

Pre-emptive maintenance hand-off to reliability
Operator-level early-warning alerts
Closed-loop learning from each event

Shift-level coaching

Generates pre-shift briefings and live recommendations: top losses, planned countermeasures, expected throughput, target gaps.

Pre-shift briefing in 1 minute
Live target-gap visibility for line leaders
Auto-prepared shift handover notes
Recommended actions ranked by impact

Continuous improvement engine

Daily and weekly top-loss reports auto-prepared, with Pareto, candidate kaizen actions and tracked outcomes.

Auto-Pareto by loss category and station
Recommended kaizen actions with prior-success matches
Action tracker with closure verification
OEE lift attributed to each closed action

Architecture context

Select components after the boundary and test are clear.

The list is a design vocabulary. Final selection depends on licences, data location, latency, security, existing systems and customer approval.

Data sources

MES (Rockwell, Wonderware, SAP DM)
Production & quality
SCADA / PLC (OPC UA)
Real-time signals
Historian (PI, Ignition)
Time-series
Vision systems
Quality auto-capture

ML & analytics

PyTorch / XGBoost
Downtime prediction
Time-series segmentation
Micro-stop detection
NLP
Free-text loss tagging

Action & coaching

Shift handover app
Mobile coaching
Andon integration
Auto-escalation
Power BI / Grafana
Cell to plant dashboards

CI rhythm

Daily ops review
AI-prepared agenda
Top-loss kaizen tracker
Action loop
MTBF / MTTR trending
Reliability arm

Planning ranges

A standard path from decision to operation.

Discovery and qualification

1-2 weeks

Named workflow, owner, baseline, risks and go/no-go questions.

Readiness assessment

2-4 weeks

Data, integration, security, value and operating-readiness findings.

Technical proof of value

4-6 weeks

A bounded test on representative data with documented limitations.

Production pilot

8-12 weeks

One controlled workflow, integrated and evaluated with real users.

Enterprise or multi-site rollout

3-6+ months

Phased scale-out, monitoring, support and change management.

AI capability or CoE programme

3-6+ months

Governance, delivery methods, reusable assets and team enablement.

These are planning ranges, not guarantees. Readiness, validation, hardware, integration, access and change management affect the schedule.

Production safeguards

Private deployment is one control, not the whole control system.

Data boundary

Select offline, edge, on-premises or private-cloud deployment from the real operating constraints.
Document data flows, storage, deletion, backups and support access before production.
The customer approves every interface and any permitted external connection.

Model and application controls

Evaluate representative cases, uncertainty and harmful failure modes before use.
Use suitable access control, input handling and output guardrails for the selected risk.
Treat grounding and citations as testable behaviours, not as a promise of perfect answers.

Traceability and operation

Define identity, roles, logs, monitoring, updates, backup and incident handling.
Keep the evidence needed to investigate outputs and reproduce important decisions.
Assign a named business and technical owner for production operation.

Human authority

Domain, quality, safety and regulatory owners retain decision authority.
Compliance depends on the implemented system and the customer's validated controls.
Escalation and safe fallback are part of the acceptance criteria.

Limitations to plan for

Performance can change with data quality, equipment, process, product mix, documents, users or operating conditions. Third-party model and software licences still apply. AI output does not replace the responsible engineer, operator, quality owner, safety professional, legal adviser or regulator.

Buyer questions

Direct answers before you plan a pilot.

What OEE lift is realistic?+

This legacy draft included a quantitative benchmark that has not been published with a reviewable source or customer evidence. NeoBram now treats it as an open validation question and defines the baseline, test method, acceptance threshold and limitations during discovery.

Don't we already have OEE in our MES?+

Most MES tools track OEE but don't actively improve it. They show the number; they don't tell you which root cause to attack first, predict the next downtime, or coach the shift. Our system layers on top of your MES, doesn't replace it.

How does it detect micro-stops?+

Does this need new sensors?+

Rarely. Most plants already have enough signal in their SCADA / PLC layer. We occasionally recommend a small number of additional sensors (e.g. cycle-time triggers, vibration on rotating equipment) where there's a clear ROI.

How long to value?+

A focused production pilot is commonly planned over 8 12 weeks after scope, representative data, owners and acceptance tests are ready. Discovery or a technical proof of value may be shorter; integration, validation, hardware, site access and change management can extend the plan. This is a planning range, not a delivery guarantee.

Where does our production data live?+

The target can be on-premise near the plant network or inside your approved private cloud. The final architecture must document telemetry, backups, support access and any external service calls before a residency claim is made.

Bring one workflow

Define the evidence, boundary and acceptance test together.

Your team supplies process authority. NeoBram supplies AI architecture, engineering, evaluation and operating handover.

Plan the first project