INDUSTRIES / MANUFACTURING
Manufacturing, transformed with intent.
We connect the shop floor to the cloud and put AI on top: predictive maintenance, vision-based quality inspection, and production intelligence that turns OEE from a report into a lever.
OVERVIEW
How we work in manufacturing
Manufacturers know exactly where money leaks — unplanned downtime, scrap, changeover time, energy waste — but the data to fix it is trapped in PLCs, historians, MES databases, and paper travelers. Ilmora's manufacturing practice bridges OT and IT: we have instrumented 200+ production lines across automotive, electronics, industrial equipment, and CPG plants, building the data foundation first and the AI on top of it.
Our engineers speak both OPC UA and Kubernetes. We deploy edge inference where latency matters, cloud analytics where scale matters, and we design every solution to survive plant realities: harsh networks, shift-based operations, and skeptical operators who have seen pilots die before. Adoption on the floor is a design requirement, not a hope.
Measured outcomes
- reduction in unplanned downtime on instrumented lines
- 32%
- reduction in unplanned downtime on instrumented lines
- fewer quality escapes after vision inspection rollout
- 47%
- fewer quality escapes after vision inspection rollout
- average OEE gain within twelve months
- 9pt
- average OEE gain within twelve months
- energy cost reduction across optimized utilities
- 18%
- energy cost reduction across optimized utilities
THE CHALLENGES
What's standing in the way
The problems we hear most often from manufacturing leaders — and the ones our engagements are scoped to solve.
Unplanned downtime
A single hour of unplanned stoppage on a constrained line can cost six figures. Reactive maintenance and calendar-based PMs both miss the failures that matter.
Quality escapes and scrap
Manual visual inspection is inconsistent across shifts, and defects found at end-of-line — or worse, by customers — carry the full cost of everything upstream.
OT/IT data silos
PLC tags, historian data, MES records, and quality systems don't share identifiers or timestamps, making cross-line analysis and root-cause work painfully manual.
Tribal knowledge walking out the door
Experienced operators and maintenance techs retire with decades of undocumented know-how; new hires face a years-long learning curve.
OUR SOLUTION
From constraint to capability
We start with a unified namespace: streaming OT data from PLCs and historians (OPC UA, MQTT/Sparkplug B) into a governed industrial data platform where machine, MES, quality, and ERP data finally share context. That foundation typically pays for itself through visibility alone — accurate OEE, loss attribution, and energy monitoring across lines and plants.
On top, we deploy the AI workloads with proven ROI: vibration- and sensor-based predictive maintenance models, computer-vision inspection cells running at line speed on edge hardware, process-parameter optimization for yield and energy, and LLM-powered knowledge assistants that make thirty years of maintenance logs and SOPs searchable by any technician on a tablet.
Predictive maintenance
Anomaly detection and remaining-useful-life models on vibration, thermal, and process data, integrated with your CMMS work-order flow.
Computer-vision quality inspection
Edge-deployed defect detection at line speed — surface defects, assembly verification, label and packaging checks — with operator-friendly review stations.
Connected-factory data platforms
Unified namespace architectures streaming OT data to cloud lakehouses with ISA-95 contextualization.
Production intelligence & OEE
Real-time loss attribution, bottleneck analysis, and plant-to-enterprise dashboards that replace end-of-shift spreadsheets.
Process optimization
ML-driven setpoint recommendations for yield, energy, and throughput on continuous and batch processes.
Digital work instructions & knowledge AI
LLM assistants over maintenance history, SOPs, and OEM manuals, plus guided digital workflows for operators.
TECHNOLOGY
The stack behind the solutions
Representative platforms and frameworks we deploy in this sector — always selected to fit your estate, not a vendor agenda.
OT & Edge
- OPC UA
- MQTT / Sparkplug B
- Ignition
- NVIDIA Jetson
- K3s
- TimescaleDB
AI / ML
- PyTorch
- YOLO / vision transformers
- Prophet
- MLflow
- ONNX Runtime
- LangChain
Cloud & Data
- AWS IoT SiteWise
- Azure IoT Operations
- Databricks
- Kafka
- Grafana
- Terraform
HOW WE DELIVER
A process built for regulated reality
Five phases, each with a concrete artifact and a go/no-go decision — so you always know where the engagement stands.
- 01
Loss mapping
Quantify downtime, scrap, and throughput losses per line; pick the use case where data readiness meets financial impact.
- 02
Connect & contextualize
Stand up edge connectivity and the unified namespace so machine data lands with product, order, and shift context.
- 03
Model & validate on the line
Train on historical data, then validate predictions against real events on one line with maintenance and quality teams in the loop.
- 04
Integrate with operations
Wire outputs into CMMS work orders, andon systems, and operator stations so predictions trigger action, not just dashboards.
- 05
Scale across lines & plants
Template the deployment for repeatable rollout, with fleet-level model monitoring and a plant enablement playbook.
USE CASES
Where clients start
Proven entry points with clear ROI — most engagements begin with one of these and expand from evidence.
Rotating-equipment failure prediction
Vibration-based models flagging bearing and gearbox degradation weeks ahead, feeding prioritized CMMS work orders.
In-line visual inspection
Camera cells catching surface and assembly defects at station level, before value is added downstream.
Energy optimization
Per-asset energy monitoring and ML setpoint tuning across compressed air, HVAC, and process heating.
Maintenance knowledge assistant
A technician-facing AI that answers troubleshooting questions from manuals, logs, and past work orders.
FAQS
Questions we hear from leaders
Direct answers to the questions that come up in the first conversation.
RELATED SERVICES
Services behind these solutions
Ready to move the numbers in manufacturing?
Bring us your hardest operational problem. In a 45-minute consultation, our practice leads will map the highest-ROI starting point and a realistic delivery plan.