AI
FIVE WAYS ARTIFICIAL INTELLIGENCE CAN REVOLUTIONISE MANUFACTURING
Companies that embrace AI technologies are building more efficient, resilient and sustainable operations, says Mike Bradford, director of strategic business development at DELMIA. As AI continues to evolve, its capacity to innovate and optimise will further solidify its role as a cornerstone of modern manufacturing.
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rtificial intelligence (AI) is fundamentally reshaping the manufacturing sector. By integrating intelligent systems into production lines and supply chains, companies are unlocking unprecedented levels of efficiency, precision and innovation. This evolution goes beyond simple automation, introducing systems that learn, adapt and predict outcomes. The result is a smarter, more resilient and more sustainable manufacturing landscape.
This article identifies five key areas where AI is making a significant impact, then goes further to cover the broader use cases, benefits and challenges manufacturers are navigating today.
WHAT IS AI IN MANUFACTURING? AI in manufacturing applies technologies including machine learning, generative AI and agentic AI to industrial data, models and knowledge to improve analysis, decision-making and execution. Unlike general-purpose AI, industrial AI can operate in the context of products, processes, production systems and supply chains, helping manufacturers predict outcomes, explore alternatives and take informed action.
A commonly cited AI use case in manufacturing is predictive maintenance, but the applications extend far beyond it. AI enables manufacturers to offer mass customisation, allowing products to be tailored to individual customer preferences without slowing down the line, and it increasingly touches nearly every stage of production, from initial design to final quality inspection.
WHY AI MATTERS NOW: INDUSTRY 4.0 AND THE DATA EXPLOSION AI is crucial to the concept of ‘Industry 4.0’, the trend towards greater automation in manufacturing settings and the massive generation and transmission of data on the factory floor. In essence, the Fourth Industrial Revolution is the trend towards automation and data exchange in manufacturing technologies and processes, encompassing cyber-physical systems, the Internet of Things (IoT), cloud computing, cognitive computing and artificial intelligence. Industrial IoT and smart factories now generate large amounts of data every day, from vibration sensors on a motor to cameras watching a weld seam. That volume is exactly why AI has become
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necessary rather than optional: it is the only practical way to turn that flood of sensor data into decisions a human can act on in real time.
1. PREDICTIVE MAINTENANCE: PREVENTING FAILURES BEFORE THEY HAPPEN Unexpected equipment failure is a primary cause of costly downtime in manufacturing. Traditional maintenance schedules, based on fixed intervals, often result in servicing equipment too early or too late. Predictive maintenance, powered by AI, offers a more intelligent solution. It uses machine learning algorithms to analyse data from sensors on equipment, identifying subtle patterns that signal an impending failure.
By monitoring variables such as temperature, vibration and performance metrics, AI can predict when a component is likely to fail. This allows maintenance teams to intervene proactively, scheduling repairs during planned downtime and avoiding catastrophic failures. This data-driven approach significantly reduces unplanned operational stoppages, extends the lifespan of expensive equipment and lowers overall maintenance costs. The shift is from preventive ‘maintain it on a fixed schedule’ and reactive ‘fix it when it breaks’ approaches to a predictive, strategic model that optimises maintenance time and effort and minimises equipment failures.
Some manufacturers take this further by using a digital twin, a virtual model kept in sync with real- time sensor data, to assess patterns in equipment behaviour and performance. These systems can alert operators to potential issues early, allowing them to prevent breakdowns before they escalate. Automotive manufacturers, for example, widely employ predictive maintenance on assembly-line robots, significantly reducing unplanned downtime and resulting in substantial cost savings.
2. PROCESS OPTIMISATION: ENHANCING QUALITY AND THROUGHPUT Achieving peak efficiency on the production line requires constant monitoring and adjustment. AI excels at this complex task by analysing vast datasets from the manufacturing process in real time. Machine learning models can identify bottlenecks, inefficiencies and deviations from optimal parameters that might be invisible to human operators.
For instance, in precision manufacturing, AI can analyse images captured by cameras on the
production line to detect microscopic defects in products, thereby ensuring higher quality control standards. In chemical production, algorithms can adjust ingredient mixtures or temperature settings to maximise yield and minimise waste. Food and beverage manufacturers similarly use AI-powered systems to track ingredient usage in real time. By continually learning from production data, AI systems refine processes to achieve greater speed, consistency and quality, leading to higher throughput and reduced operational expenses. Another example is generative AI in machining, where AI can ‘learn’ from machining history and automatically generate NC machining programs based on the starting dimensions of the raw material and the 3D model of the part to be machined, helping to address the increasingly critical shortage of skilled workers in manufacturing. Similar AI capabilities can be applied in additive manufacturing. In electronics manufacturing, intelligent detection systems powered by machine learning are already being used to reduce preparation time in tasks such as PCB layer identification.
3. INTELLIGENT AUTOMATION AND ROBOTICS
Automation in manufacturing is not new, but AI is making it smarter and more flexible. Traditional industrial robots are programmed to perform a single, repetitive task with high precision. AI-powered robots, including collaborative robots or ‘cobots’, can perceive their environment, make decisions and support work alongside human employees. Equipped with advanced sensors and machine vision, these robots can handle more complex and varied tasks, such as assembling intricate products or sorting mixed items. They learn from experience, improving their performance over time. This new generation of automation enables manufacturers to adapt quickly to changes in product design or demand, resulting in more agile production lines. It also frees human workers from dangerous or monotonous tasks, allowing them to focus on more creative and strategic responsibilities.
This capability can be extended into the virtual world using generative AI. For manufacturers that need to relocate a production line, add a new line or even build a new plant, a virtual model can be created based on a point cloud generated by
October 2026 Instrumentation Monthly
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