How to Leverage Maintenance Data
How to Leverage Maintenance Data
In the world of mining, maintenance costs represent a significant portion of operational expenditures. According to a study conducted in Chilean open-pit mines by the University of Queensland researchers, maintenance is responsible for an average of 44% of mine production costs, with averages of 35%, 56%, and 44% for blast hole drill, shovel, and haul truck fleets, respectively. This cost is exacerbated by the staggering hourly cost of unplanned downtime, which can exceed CA$130,000. Despite the critical nature of these expenses, a surprising 80% of mining companies struggle to fully quantify the complete cost of equipment downtime, according to the North American Mining Journal. This lack of precise understanding underscores the urgent need for optimized maintenance strategies.
Data Availability Issues
The primary barrier to optimizing maintenance in the mining industry is the availability of high-quality data. Maintenance data is often scattered, inconsistent, and difficult to interpret. Machines generate fault codes that are manually ingested into ERP systems, while technicians provide maintenance logs in natural language. This creates a mix of heterogeneous, unsystematized data with untapped potential. The data exists on different servers and in different formats.
Maintenance Data Potential
The optimization of maintenance scheduling, reduction of downtime, prevention of critical failures, and increase in asset life are all dependent on robust data-driven decision-making. However, without high-quality maintenance data, these goals remain out of reach.
The Way Forward: Integrated Approach
The mining industry must develop methods to work with historical and real-time maintenance data. Symboticware is leading the charge with Squares, our innovative maintenance management platform designed for hardware independence and powered by natural language processing AI.
Symboticware’s Squares is a cutting-edge maintenance management platform that leverages natural language processing AI to integrate various maintenance data sources. It transforms disparate maintenance logs and fault codes into cohesive, actionable data. This enables mining companies to gain unprecedented insights into their maintenance costs, optimize their schedules, and, ultimately, extend the useful life of their assets.
Want to learn more about how Symboticware and Squares can revolutionize your maintenance strategy? Book a demo with us today and discover the future of data-driven maintenance in the mining industry.