Artificial Intelligence (AI) is no longer a futuristic concept in manufacturing. By analysing data, identifying patterns, and making predictions in real time, AI enables smarter operations, greater efficiency, and more resilient production lines.
From predictive maintenance and quality inspection to supply chain optimisation and digital twins, AI solutions are delivering tangible improvements that reduce downtime, improve safety, and cut costs. Below, we explore some practical examples of AI in manufacturing.
Machine Learning and Industrial Edge
AI and machine learning are often used interchangeably—but they’re not the same. Not all AI is machine learning, though machine learning is one of the most practical ways AI is applied in manufacturing.
Machine learning enables systems to learn from data and improve over time. Instead of relying on fixed programming, ML systems adapt, detect inefficiencies, and make better decisions as they process more information.
Solutions such as Siemens Industrial Edge take advantage of this. Siemens Industrial Edge is a secure environment for deploying industrial applications directly at the machine level. While not every application within Industrial Edge uses AI or ML, certain applications are designed specifically for advanced analytics, predictive insights, and machine learning functions. By running these applications locally at the edge, manufacturers gain real-time optimisation, reduced latency, and faster decision-making.
Quality Inspection and Control with Inspekto
Maintaining consistent quality is one of the toughest challenges in manufacturing. However, AI and ML can help to improve quality control.
Siemens Inspekto is an AI-powered quality inspection system that uses computer vision to carry out automated visual inspections, detecting faults and inconsistencies with high accuracy. By identifying issues early before products move further down the line, therefore reducing waste, cutting costs, and improving overall product reliability.
Unlike human inspectors, who may fatigue over repetitive checks, Inspekto maintains high accuracy throughout. It is also designed for simple deployment and integration, making advanced AI-based inspection both accessible and scalable for manufacturers.
Siemens Engineering Copilot - TIA Portal Co-pilot V21
AI isn’t just transforming the factory floor—it’s also reshaping how engineers design, test, and optimise automation systems.
The Siemens Engineering Copilot, embedded in TIA Portal, acts as an AI-powered assistant for engineering teams. It can:
- Generate and explain SCL code
- Create and adapt WinCC Unified visualisations
- Speed up searches in TIA Portal documentation
By reducing repetitive tasks and providing instant support, the Copilot frees up engineers to focus on innovation and solving complex challenges.
Digital Twins and Predictive Maintenance
One of the most powerful uses of AI in manufacturing is predictive maintenance powered by digital twins.
A digital twin is a virtual model of a machine or production line that reflects its real-world condition using live sensor data. Parameters such as vibration, temperature, and pressure are constantly monitored, allowing AI algorithms to detect anomalies and forecast potential failures.
Instead of reacting to breakdowns, manufacturers can plan interventions before problems occur. This proactive approach reduces downtime, extends machine life, and optimises maintenance schedules.
What are the benefits of using AI in manufacturing?
Greater efficiency – Faster production, fewer errors, and optimised workflows.
Cost savings – Reduced downtime and waste thanks to predictive maintenance and AI inspection.
Improved safety – Fault detection prevents dangerous breakdowns and protects the workforce.
Data-driven decisions – Real-time analysis enables smarter planning and risk reduction.
What are some challenges of using AI in manufacturing?
Cybersecurity risks – More connected systems mean stronger protection is needed.
Upfront investment – Sensors, software, and integration can require significant spend; ROI depends on scale and quality of data.
Skills gap – AI demands new skills and training for employees.
Change management – Adopting AI means new ways of working, which can meet resistance without proper support.
Why Choose Parmley Graham?
At Parmley Graham, we help bridge the gap between AI innovation and your complex manufacturing challenges. Whether it’s integrating new solutions, deploying AI-powered inspection, or developing digital twin strategies, expert guidance can help ensure successful implementation tailored to your operations.
Support can include:
- Personalised consultation and guidance on AI integration
- Training and upskilling for teams to work effectively with new technologies
- Workshops and best-practice advice to help manufacturers adopt AI solutions smoothly
With the right support, manufacturers of all sizes and sectors can optimise processes, reduce downtime, and fully leverage the potential of AI in their operations.
Contact our team today at support@parmley-graham.co.uk or 0191 478 0404. You can also fill out the form below, and we’ll be in touch.