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.
