This article is part of our series on The Role of AI-Powered Decision Intelligence in Modern Maintenance Strategies. It’s a deep dive into why AI, decision intelligence, and advanced analytics are vital parts of modern maintenance strategies and how to successfully implement these technologies in maintenance operations.
The cost of unplanned downtime is rising in all sectors. According to recent research by Siemens, every unproductive hour now costs automotive manufacturers $2.3 million. Likewise, the heavy industry has seen a fourfold increase over the last five years and even small-to-medium sized businesses can see losses of up to $150,000 per hour of downtime.
But keeping on top of the maintenance activities that prevent downtime has grown difficult. The data that operational systems produce has grown more complex, facilities are more globally widespread, and much-needed skills are more difficult to find.
AI-powered technologies have emerged as the way to overcome these strategic challenges and instead, optimize operations, predict equipment failures, and make better, more informed decisions. Underpinning this growth in AI usage is explainable artificial intelligence.
What is Explainable AI for Maintenance Operations?
Explainable AI (XAI) is a framework that helps humans understand and trust the insights created by their AI models. It lifts the “black box” off of AI, making it more clear how the algorithm arrived at certain results or recommendations. XAI bridges the gap between the complexity of AI and the human need for straightforward, approachable, and transparent answers.
This increases decisionmakers’ trust in these technologies and the likelihood that they will become more comfortable using them to build their decision intelligence muscle. One way XAI creates this trust is by delivering key insights and next steps in natural language and augmenting explanations with visualizations to help users better understand how the system came to its conclusion.
This means maintenance analysts can use platforms like Virtualitics to:
- Use embedded AI routines to generate multidimensional visualizations based on available data and contextual information.
- Deliver key insights in conversational language that maintainers with non-scientific backgrounds can easily and quickly understand.
- Use large language models (the technology that powers generative AI like ChatGPT) to suggest the next steps in the analysis based on user prompts. These prompts can be specific (“I want to understand why this machine needs early maintenance”) or more open-ended (“Tell me something interesting about my data”).
How Does Explainable AI Work For Decision Making?
Integrating AI into your decisionmaking processes is an invaluable way to reduce downtime across maintenance operations. For example, maintenance personnel can use AI-powered analytics to determine why the system is suggesting reducing the order for certain parts or why a machine not scheduled for maintenance needs repair within the next week.
However, even when the data shows what is the best actionable step to take next, decision makers might still be hesitant to act on insights that were put together by an algorithm. When it comes to strategic and proactive maintenance, speed is of the essence.
XAI is essential to decision intelligence. It uses specialized models and algorithms to provide clear explanations and reasoning for how it came to a certain conclusion. This enables maintenance teams to build trust in AI recommendations and speed up analytical and interpretation practices so that companies can get to more accurate answers faster.
When maintenance analysts can trust their AI to help them make the right proactive decisions, they’re able to optimize pre-existing maintenance activities while also anticipating future maintenance needs in the most efficient ways possible.
Where to Integrate XAI and Decision Intelligence in Maintenance Operations
Decision intelligence allows maintenance teams to anticipate issues before they occur, allocate resources more effectively, and make decisions that align with broader business goals. Combined with XAI, maintainers get a data-driven way to untangle complex problems and discover clear, actionable strategies to solve them.
But what does this actually look like in practice? Below are a few use cases.
1. Asset Risk Assessment
Traditional maintenance relies on reactive repairs after equipment has already failed or preventive maintenance based on fixed schedules. Both approaches are prone to inefficiencies—reactive repairs result in downtime, while preventive maintenance often leads to unnecessary interventions.
XAI-enabled decision intelligence systems can process real-time data from sensors embedded in equipment, historical maintenance records, operational conditions, and more to detect patterns that indicate an impending failure. For example, a machine’s vibration data might deviate from its usual pattern, signaling a potential issue. The system can alert maintainers to how long the equipment can continue operating safely and explain its recommendation for the optimal time for maintenance.
By identifying early warning signs of failure, these AI-driven platforms help maintenance teams avoid unplanned downtime and extend the lifespan of critical assets. It also ensures that maintenance is performed only when necessary, reducing overall costs. This enables maintenance teams to identify not only current issues but also future risks, providing them with a head start in mitigating potential problems before they escalate.
2. Resource and Workforce Optimization
Whether it’s technicians, spare parts, or maintenance tools, inefficient resource allocation can lead to delays, higher costs, and even equipment failure if the necessary resources aren’t available when needed.
Rather than trying to make sense of how to juggle all these resources, decision intelligence can analyze all this information for maintainers and use XAI to clearly explain to everyone how any decisions were made. It can generate a schedule that optimizes across resource availability constraints, all maintenance needs, and asset usage and uptime requirements. It can even recommend which team members should be assigned to specific tasks based on their expertise, availability, and proximity to the job site.
3. Inventory Management
Maintaining an appropriate inventory of spare parts is crucial for effective maintenance operations. Overstocking leads to higher storage costs and tied-up capital, while understocking risks prolonged downtime if critical parts aren’t available when needed.
Maintenance decision intelligence systems can predict demand for spare parts and supplies based on historical usage patterns and current equipment conditions. For example, if it detects that certain components in a piece of machinery are approaching the end of their useful life, it can recommend ordering replacements in advance. Conversely, if a part is rarely used, the system might suggest reducing inventory levels using conversational and explainable language.
This reduces the risk of stockouts and excess inventory so that the right parts are always available without tying up unnecessary capital in inventory.
4. Risk Management
In complex industrial environments, maintenance decisions are often high-stakes, involving trade-offs between production goals, safety concerns, and cost considerations. Decision intelligence supports better risk management by providing data-driven insights into the potential outcomes of different decisions. Together with XAI, it can even simulate the impact of different decisions, allowing maintenance managers to explore “what if” scenarios before taking action.
For instance, if a critical asset shows signs of wear, the platform can help the maintenance team assess the risks of delaying the repair versus performing it immediately. It might analyze factors such as the likelihood of failure, the cost of downtime, and the impact on production. Based on this analysis, the platform can provide trustworthy recommendations that balance the need for operational continuity with long-term asset health and safety.
The Future of Maintenance Decisions is Explainability
XAI is revolutionizing maintenance operations by building the trust needed to successfully leverage the full potential of AI and decision intelligence. From enhancing decision-making to enabling proactive maintenance and optimizing resource allocation, XAI offers transformative benefits.