Latest news with #predictive maintenance
Zawya
a day ago
- Business
- Zawya
How GenAI tools can cut mining maintenance costs by 10%?
Unexpected maintenance can reach up to 60% of total mine maintenance spending. But now, research has found that using generative AI (GenAI) tools can cut costs by a significant 10%. When mines deploy predictive maintenance systems, they tend to do so in isolation and rely on static thresholds. This lack of integration limits visibility and prevents coordinated responses, which erodes productivity and efficiency. Unlike traditional, fixed-schedule methods based on legacy telemetry data, GenAI solutions can synthesise structured and unstructured data to perform smart diagnostics, support real-time parts ordering, or augment in-field support. The differentiator is GenAI's ability to interpret patterns and contextualise valuable outputs from vast datasets. An orchestration layer powered by the right technology can process information from every sensor feed and every set of technician notes seamlessly, and across sites. Using this approach, we have seen mining companies increase fleet availability by 15% within six months, improve technician job effectiveness and optimise technician job durations by up to 20%. Problem areas Even the most advanced diagnostics fall short if parts are not available. Delays in remote sites can halt production, but excess stock will tie up working capital. Costly instances of reactive ordering, low inventory turnover, and weak integration between planning and procurement. GenAI addresses this by connecting asset health forecasts with real-time inventory, supplier lead times, and planned maintenance windows. Value is derived from ensuring optimal inventory of the right parts, purchased at the right time and price, to support planned and unplanned maintenance. Field technicians are on the front lines when it comes to maintenance, but are often underserved. They face complex repair issues, incomplete documentation, and inconsistent knowledge sharing - especially in multi-site operations. A conversational GenAI agent specifically for field technicians is capable of translating complex fault codes into actionable steps, leveraging historical data, and identifying relevant OEM guidance, effectively providing synthesised, asset-specific support on fault identification and resolution. GenAI can help tackle the talent challenge in the mining space by embedding learning into operations, providing junior team members with guidance while saving experts time to focus on high-value work. These applications of GenAI also reduce human error and operational risk significantly by providing step-by-step guidance, flagging safety concerns, and addressing problematic conditions before they escalate. The result is more consistent field decisions and a more agile, confident and safe workforce. Jumping on the bandwagon Taking the GenAI jump does not require digitally advanced operations; the key is just getting started. On the journey towards the autonomous maintenance ecosystems of the future, this is a crucial step. Perfect data foundations and digitally advanced operations are not required to begin reaping the rewards of GenAI integration. Even mines with low digital maturity using a tier-one enterprise asset management (EAM) system can adopt modular, pragmatic GenAI solutions that quickly demonstrate their value. However, two fundamental elements need to be in place. - The first is organisational readiness: aligned leadership and visible support from the top; building trust and localised change-management; a digital enablement culture that promotes usability; incentives for adoption linked to KPIs. - The second is certain technical foundations: cloud connection; network infrastructure; lightweight event bus or middleware for monitoring and alerts across platforms; about 12-24 months of maintenance data; security and access control. Performance gains are enhanced when integration has been carried out end-to-end. Autonomous coordination GenAI in mining is not a distant aspiration. Modular, mine-ready solutions are already available that deliver operational impact in complex, remote, and resource-constrained environments. Mining company leaders looking to unlock value from GenAI in operations can start with intent, alignment, and a plan, rather than waiting for the perfect conditions. Mining's next frontier is autonomous coordination. GenAI is already improving diagnostics, inventory, and technician workflows, but agentic AI will go further by scheduling interventions, placing orders, and intuitively escalating issues at the right moments.
Forbes
07-08-2025
- Business
- Forbes
Predictive Maintenance: Five Ways To Enhance Data Quality
Illia Smoliienko, Chief Software Officer, Waites. More and more industries rely on real-time data analytics. In finance, AI algorithms analyze transactions. In energy, smart grid platforms balance supply and demand. In the industrial sector, predictive maintenance (PdM) powered by artificial intelligence is used to prevent equipment failures and reduce downtime. Continuous monitoring of equipment condition and vibration analysis helps prevent major breakdowns. Modern vibration diagnostics increasingly uses AI, but the accuracy of its conclusions—and whether a defect will turn into a serious malfunction—still depends on response speed and human expertise. That's why it's essential to design a system where analysts can deliver highly accurate results in a timely manner. In this article, I'll use examples from industrial analytics to explain what improves the quality of data analysis and why it's crucial to combine the precision of AI algorithms with hands-on human experience. People With Field Experience "Data analyst" is not an entry-level role. The quality of business decisions depends on their insights, and errors in data handling can cost millions. In many industries, the most effective analysts are those with hands-on field experience. For example, engineers who once oversaw processes on construction sites move on to analyze project data using building information modeling (BIM) systems. Professionals who spent years repairing machinery on the factory floor now work in computer-aided engineering (CAE), where they analyze part behavior in digital models and interpret data through the lens of their practical expertise. We hire vibration analysts who previously worked as maintenance engineers or vibration diagnosticians in industrial settings. Now, they operate in a digital environment, reading data streams from IIoT sensors, interpreting signals and making technical assessments without ever stepping into the plant. Distributed Teams In Different Countries If your company needs to respond to requests 24/7, there are two main ways to organize analyst workflows. One is to work from a single location in multiple shifts—a model often used by product-focused IT companies. However, if the role involves high responsibility, as it does with vibration analysts, night shifts aren't ideal. Disrupted circadian rhythms lead to faster mental fatigue, increasing the risk of errors in analysis—mistakes that can directly affect those relying on that data to make decisions. Human error during night shifts frequently causes industrial accidents. The second approach is a distributed team working across different time zones. This model suits industries where analysts' work impacts safety or critical operational processes. Our analysts operate in three different time zones, enabling round-the-clock equipment monitoring worldwide while each team works during their own daytime hours. Preliminary Conclusions System Today, practically every business talks about process optimization through AI. In predictive maintenance, it's about the efficiency of a process that can't be scaled manually. Analyzing and filtering signals that indicate defects would require hundreds of people. If you're currently working with a small data stream, instead of immediately implementing complex algorithms, you can use a two-tier analysis system: Junior analysts first filter out anomalous signals from the data set, while experienced specialists perform in-depth analysis and draw conclusions. This approach reduces the number of routine tasks for senior experts and allows juniors to learn from real cases. We introduced ML and AI algorithms when the data volume began to grow exponentially, automating the anomaly detection stage. Modern algorithms can identify defect types with 90% to 97% accuracy in some cases. However, they cannot replace analysts because they interpret data without considering operational context, such as changing equipment conditions. Besides equipment context, data quality plays a critical role in analysis accuracy. This depends not only on the development team but also on the client providing the data. In manufacturing analytics, poor maintenance of IIoT sensors often leads to low-quality data and delayed defect detection. Therefore, analytics quality depends on maintaining a strong data culture at all levels. A User-Friendly Tool For Analysts Analysts need a tool that organizes tasks and highlights priorities. If no existing solution fits your needs, you'll have to create one yourself or commission its development. For example, in PdM, the system should detect deviations from the norm and generate a prioritized task list. Whenever there are changes in equipment performance, all analysts receive push notifications, and when someone takes on a detected defect, the system records it. It's also important that the interface allows analysts to view not only current data but also historical information. Consider also the task distribution logic: Will tasks be assigned automatically by the system, or will analysts choose them themselves? In the latter case, there's a risk that people might pick easier cases and leave the more difficult ones to their colleagues. We developed a tool for analysts to work independently by combining equipment signal processing—spectral analysis—with task management in a single system. Learning Opportunities When a team grows professionally, it boosts the quality of analytics. From my experience, this happens through development in three key areas: • Regular Improvement Of Hard Skills: Organize lectures with guest speakers and encourage the team to get certified. This refreshes their knowledge and provides clear proof of their competence. • Internal Mentoring: Encourage colleagues to share their strengths, whether it's expertise with specific equipment or technologies or helpful automation tips. • Creating A Culture Of Questions: Set a clear rule: It's okay to ask when you don't understand, but making mistakes silently without trying to figure things out is not. If an error occurs, conduct a retrospective to identify its cause. This helps prevent similar issues in the future. Accurate analytics result from building a well-coordinated system that combines technology with specialists' practical experience. It's not a model that makes all decisions alone, but a thoughtful process from data analysis to action. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
