Unplanned equipment downtime
Failures are discovered when machines stop, causing lost production and emergency repairs.
02 / Automation
Industrial Automation
Plants already collect a great deal of data through PLCs, SCADA systems and sensors. Much of it is never used beyond the control room. We connect operational technology with IT systems and AI so that data informs maintenance, quality and production decisions, without compromising safety or control integrity.
At a glance
- Service
- Industrial Automation
- Pillar
- Automation
- Typical starting point
- Plant data and systems assessment
- Typical first phase
- 4 to 6 weeks for assessment; pilots typically 8 to 16 weeks
- Key technologies
- Internet of Things, Edge AI, Computer Vision
Durations depend on scope, data readiness and integration needs.
What is Industrial Automation?
Short answer
Industrial automation is the use of control systems, sensors, software and increasingly AI to operate and monitor industrial processes and equipment. In our practice it focuses on connecting operational technology (OT) data with IT and analytics systems for monitoring, predictive maintenance, quality inspection and production optimisation.
Industrial environments are different from office IT. Equipment runs for decades, safety comes first, networks are segmented for good reason and downtime is expensive. Any AI or automation work has to respect those realities.
We typically work above the control layer. Rather than changing how PLCs control machines, we collect data from them through secure, read-only connections, contextualise it with maintenance, quality and production records, and build applications that help engineers and operators make better decisions.
Where closed-loop control is involved, changes follow your engineering change process and are validated by the plant's control engineers.
Business problems
What problems does Industrial Automation solve?
These are the situations where organisations most often find this service worthwhile.
Manual quality inspection
Visual checks are slow, inconsistent between shifts and miss subtle defects.
Data locked in control systems
PLC and SCADA data is available in the control room but not connected to planning or maintenance systems.
Energy and material waste
Inefficient settings and process drift increase energy use and scrap without anyone noticing quickly.
Paper-based shop floor records
Logs, checklists and downtime reasons are recorded on paper or spreadsheets and rarely analysed.
Knowledge held by a few experts
Troubleshooting depends on experienced engineers whose knowledge is not captured.
Use cases
Where can Industrial Automation be used?
Representative applications. The right use case depends on your processes, data and constraints.
| Area | Example application |
|---|---|
| Maintenance | Condition monitoring from vibration, temperature and current data with early warning of degradation. |
| Quality | Camera-based inspection for surface defects, dimensions and assembly completeness. |
| Production | OEE tracking, downtime reason capture and bottleneck analysis across lines. |
| Energy | Energy monitoring by line or asset with anomaly detection for unusual consumption. |
| Process industries | Soft sensors that estimate hard-to-measure quality parameters from process variables. |
| Safety | Vision-based detection of safety zone breaches and personal protective equipment compliance, as an aid to existing controls. |
Who should consider it
- Manufacturers with connected equipment but limited use of its data
- Plant heads and maintenance leaders facing downtime or quality issues
- Process industries seeking better visibility of process variation
- Engineering teams planning Industry 4.0 or smart factory initiatives
How AARHIT approaches it
How we deliver Industrial Automation
Each stage ends with something you can review, so decisions are made on evidence rather than on promises.
Stage 1 of 6
Site and systems assessment
We review equipment, control systems, networks, existing data, maintenance records and the problems that matter most to plant leadership.
Output: assessment report with data availability, security considerations and ranked use cases.
Stage 2 of 6
OT and IT architecture
We design secure data collection, typically read-only through gateways, with appropriate network segmentation and a data model for assets and events.
Output: architecture approved by plant engineering and IT security.
Stage 3 of 6
Data collection and contextualisation
Sensor and control data is collected, time-aligned and linked with maintenance logs, quality results and production schedules.
Output: a contextualised data foundation for the pilot area.
Stage 4 of 6
Model and application development
Analytics, anomaly detection or vision models are developed with engineers, then wrapped in dashboards, alerts or inspection stations.
Output: working pilot on selected equipment or a line.
Stage 5 of 6
Validation on the shop floor
The pilot runs under real conditions. Alerts and predictions are reviewed with maintenance and quality teams to confirm usefulness.
Output: validation results and a scale-up recommendation.
Stage 6 of 6
Scale and sustain
Proven solutions are extended to more assets or lines, with model monitoring and ownership assigned inside the plant.
Output: rollout plan, runbooks and training.
Technology
What technologies are involved?
We choose tools on fit, cost, security and your existing estate. These are typical options, not fixed commitments.
Learn more about the underlying technologies
| Layer | Typical options |
|---|---|
| Industrial protocols | OPC UA, Modbus, MQTT, and vendor PLC and SCADA interfaces |
| Edge | Industrial gateways and edge computers for local processing and buffering |
| Data platforms | Time-series databases, historians and industrial data lakes |
| AI methods | Anomaly detection, remaining useful life models, computer vision |
| Applications | Dashboards, alerting, mobile maintenance apps, MES and CMMS integration |
| Security | Network segmentation aligned with IEC 62443 practices, read-only access, monitoring |
Outcomes
Potential business outcomes
Targets are agreed with you during scoping and measured against a baseline. We do not quote improvement figures before we have seen your process and data.
- Earlier warning of equipment problems and better maintenance planning
- More consistent quality inspection across shifts
- Real-time visibility of production performance
- Reduced energy and material waste where drift is detected sooner
- Engineering knowledge captured in systems rather than only in people
How long does implementation generally take?
Typical timeline
An assessment typically takes 4 to 6 weeks including site visits. Condition monitoring or vision inspection pilots commonly run 8 to 16 weeks, since they need enough operating time to observe meaningful events. Scale-up is planned line by line or asset class by asset class.
What determines complexity?
- Age and diversity of equipment and control systems
- Network and cybersecurity constraints
- Availability of failure history and quality labels
- Environmental conditions such as dust, heat or vibration
- Integration with MES, ERP or maintenance systems
Implementation
Implementation considerations and risks
Most difficulties in this kind of work are predictable. Planning for them early keeps delivery on track.
Protect the control layer
Data collection should not interfere with control systems. Read-only access and segmented networks are the default.
Failure data is often scarce
Well-maintained assets fail rarely. Anomaly detection and physics-informed approaches often work better than pure failure prediction at the start.
Environment affects hardware
Cameras and sensors need suitable enclosures, mounting, lighting and maintenance plans.
Involve shift teams
Alerts only help if operators and technicians trust and act on them, so they must be part of design and validation.
| Risk to consider | How we manage it |
|---|---|
| Interference with safe operations | Read-only integration, engineering change approval and validation by plant control engineers. |
| Cybersecurity exposure of OT networks | Segmentation, secure gateways, access controls and monitoring agreed with IT and OT security. |
| Alert fatigue | Tuned thresholds, prioritised alerts and feedback from technicians to reduce false alarms. |
| Pilot results that do not scale | Standardised data models and deployment patterns designed from the pilot stage. |
FAQ
Questions about Industrial Automation
Can AI be added to older machines?
Often yes. Older equipment can be fitted with external sensors for vibration, temperature or current, and data can be read from existing PLCs through gateways without replacing the machine.
Will connecting plant data create security risks?
Any connection must be designed with security in mind. We use read-only data paths, network segmentation, secure gateways and access controls, and we work with your IT and OT security teams to approve the design.
What is the difference between predictive maintenance and condition monitoring?
Condition monitoring tracks the health of equipment and alerts when readings look abnormal. Predictive maintenance goes further by estimating when a failure is likely, which needs more history. Many programmes start with condition monitoring.
Do we need a digital twin for industrial AI?
No. Many useful applications, such as anomaly detection or vision inspection, work without one. A digital twin becomes valuable when you need simulation or a unified model of a complex asset or process.
How do you work with our existing automation vendors?
We collaborate with system integrators and equipment vendors, respect their warranties and control responsibilities, and focus on the data, analytics and application layers above control.
Next step
Considering Industrial Automation for your organisation?
Share the process, product or decision you have in mind. We will ask the right questions, outline options and suggest a sensible first step.