With artificial intelligence continuing to reshape the manufacturing sector and driving its shift toward Industry 4.0, we can see more manufacturers investing in high-impact AI use cases, such as generative design, automated visual inspection, or inventory optimization. More examples of AI in manufacturing include predictive maintenance, a highly popular AI application that can deliver measurable ROI and tangible efficiency gains while reducing the risk of interruptions to the business.
In short, this use case involves applying AI to analyze how conveyor machines, robotics, vehicles, and other types of manufacturing equipment perform to accurately forecast potential performance issues and address them before they cause malfunctions and breakdowns. This way, manufacturing companies can minimize unplanned downtime, extend machinery lifespans, and optimize maintenance costs.
Gathering and preparing data
To accurately forecast equipment failures, AI-powered predictive maintenance systems need to have a holistic overview of the machine's real-time performance and physical environment, as well as past operational history. This, in turn, requires collecting and merging information from disparate sources, such as real-time insights from IoT sensors and historical data from enterprise systems, such as CMMS, ERP, and MES. As collecting and preparing all this information efficiently is simply impossible with manual methods, modern manufacturers typically delegate this task to AI tools.
For example, manufacturers can deploy AI models directly on local IoT gateways to preprocess raw sensor data instantly at the edge and then transmit it to a centralized cloud storage. Additionally, they can use AI-enabled optical character recognition tools to automatically extract unstructured data from static documents and photos – such as handwritten maintenance logs or scanned and photographed equipment operator notes – standardize it in the format required for the analysis, and also store it in the cloud.
As manufacturers handle increasing volumes of structured and unstructured information, protecting sensitive data throughout collection, processing, and storage is essential to maintaining security and compliance. A predictive maintenance system can then ingest this historical and real-time data to forecast equipment failures.
Conducting baseline profiling
Even with a rich and high-quality dataset, identifying anomalies or degradation in equipment performance is impossible without a benchmark – often called a baseline profile – that establishes what is "normal" operation for a specific piece of equipment. Instead of creating such profiles manually, manufacturers can program AI algorithms to analyze historical and real-time equipment data, taking into account variables such as equipment workload, age, or speed, to build accurate, tailored baselines for various equipment pieces.
Detecting anomalies and predicting failures
With an established baseline, predictive maintenance systems can continuously collect real-time data streams and compare them against the set parameters. Once an anomaly exceeding the predefined threshold is detected, AI algorithms can automatically classify and map it to known failure patterns, compare it to similar historical examples, and then accurately predict how many operating cycles or days and hours the equipment has left before the breakdown.
Executing diagnostics and root-cause analysis
In addition to predicting equipment failures, AI-powered systems also help manufacturers explore detected issues and identify their root causes. For example, after spotting an anomaly, AI algorithms can compare real-time IoT data with historical data associated with past failures from maintenance logs to pinpoint the specific component and spare part that is starting to wear out.
AI can also evaluate the exact conditions at the time of the anomaly (e.g., runtime hours, load cycles, or operating temperatures) to determine if a component is wearing out due to operational misuse or mechanical stress. Once the problematic component or root cause of failure is identified, a predictive maintenance system can recommend specific actions – such as load reduction, recalibration, or component replacement – to prevent the issue from recurring.
Automating maintenance scheduling
AI-enabled predictive maintenance systems can automatically generate, prioritize, and optimize maintenance schedules, presenting them for human review and approval, thereby helping manufacturers accelerate administrative maintenance processes. Specifically, after diagnosing a potential issue and calculating time-to-failure, the AI-driven maintenance system can automatically check information related to technician work hours, spare parts inventory, and production line usage, generate a work order, select the optimal time slot for maintenance jobs, and even create step-by-step maintenance instructions for the technicians.
Subsequently, the generated work order, together with the task priority and a detailed task description, can be automatically sent to the technician's field service management mobile app.
Final thoughts
There are many notable AI use cases in the modern manufacturing industry, and predictive maintenance stands out as one of the most popular and high-impact applications.
Using AI models to continuously analyze equipment performance data, manufacturers can accurately forecast equipment failures before they occur and address them in a timely manner, therefore reducing unplanned downtime, eliminating routine inspections, and extending the overall usable life of production assets.
