AI-powered maintenance leverages algorithms and machine learning models to manage large datasets from sensors, maintenance logs, and operational data to deliver valuable insights. This is about more than simple automation though. By using AI-powered maintenance technologies, businesses are able to optimize operations, predict equipment failures, and make better, more informed decisions.
The reactive approach of fixing assets after failure or even scheduled preventive maintenance is often inefficient and costly. Businesses today require more agile, predictive methods that reduce unplanned downtime and prolong asset life.
This approach involves unifying data from Asset Management (EAM), Resource Planning (ERP), and Field Service Management (FSM) systems into an AI-driven platform that can do the heavy lifting of analyzing immense volumes of complex data, and then providing the insights that help improve overall operations.
The incorporation of AI into maintenance management provides high-level benefits that span multiple facets of operations. These include:
- Enhanced operational effectiveness and efficiency
- Reduced downtime
- Improved asset longevity
- Resource optimization
- Faster decision-making and insights
Enhancing operational effectiveness and efficiency
AI-powered analytics software helps maintenance teams cut through the noise so they can better understand where resources are needed—and where they may be needed in the future.
With AI, real-time data from equipment can be analyzed instantly, triggering predictive alerts and optimizing scheduling. AI systems automatically prioritize tasks, ensuring that the most critical maintenance activities are completed promptly, while less urgent tasks are scheduled accordingly.
Reducing downtime and improving asset longevity
Unplanned downtime is one of the most expensive challenges that maintenance teams face, leading to lost production, repair costs, and reduced asset reliability. With AI algorithms doing the heavy lifting of sifting through maintenance and operational data, the system can automatically generate insights about potential deviations and risks that may not seem obvious when manually trying to sort through all that data.
By acting on these insights early, maintenance teams can prevent unexpected breakdowns, ensuring equipment stays in peak operational condition. Moreover, AI extends the longevity of assets by ensuring that maintenance is performed at the right time—neither too late, which risks damage, nor too early, which wastes resources.
Resource and inventory optimization
AI can help reduce some of the administrative burden of downtime by streamlining resource and inventory availability. By using data on historical, current, scheduled, and predicted maintenance needs, the system can generate work orders that ensure maintenance tasks are carried out efficiently.
AI-powered analytics can also predict demand for spare parts and supplies based on historical usage patterns and current equipment conditions. Traditional approaches to stocking spare parts often result in either overstocking (which ties up capital) or understocking (which leads to delays). With AI, maintenance teams can have the right parts available when needed, improving efficiency and reducing the costs associated with overstocking or emergency part orders.
Supporting faster and more informed decision-making
Decision intelligence leverages AI, advanced data analytics, and decision making processes to turn raw data into actionable business insights. By applying it to maintenance operations, leaders can leverage real-time data and insights to make faster and more accurate decisions. AI systems can also learn from past decisions, continually refining their models to provide even more accurate predictions and suggestions over time.
This can be especially crucial in complicated and urgent situations, such as an unexpected equipment malfunction. When this failure can also impact multiple systems and lead to widespread disruptions, it’s important that maintainers act swiftly. By leveraging AI to quickly identify the source of the problem, assess the severity, and give them the best course of action to take, the problem can be contained before it has negative downstream effects.
One challenge in using AI for decision-making, however, is understanding how these models arrive at their decisions—a critical issue for maintenance teams who need trust and transparency in their systems. As AI has become more advanced, it has developed a “black box” nature, making it difficult to interpret how it arrives at certain results or recommendations. This is where Explainable AI comes into play.
