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.
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”).
