Digital underground

OptiMine Analytics uses predictive modelling technology to process overall equipment efficiency and productivity rates into actionable recommendations.

OptiMine, Sandvik’s modular production management system for visualizing and managing various data sources linked with mine instrumentation and for controlling mining operations, has evolved. A new component, OptiMine Analytics, transforms the acquired data into predictive insights and actionable dashboards. OptiMine Analytics builds on the IBM Watson artificial intelligence platform and merges its analytic and predictive modelling capabilities with Sandvik’s extensive knowledge of mining operations and equipment. Thanks to the joint expertise, the predictive modelling power of the system is clearly superior to generic analytics solutions.

“This is truly something that no one else can offer,” says Petri Mannonen, product line manager with Sandvik Mining and Rock Technology. “In the case of OptiMine Analytics, the word ‘unique’ is not hyperbole.” The starting point for OptiMine Analytics is the raw data obtained from the local mine instrumentation system, through other OptiMine modules and the My Sandvik fleet monitoring system. Other customer specific data sources can include HR, ERP and maintenance management systems, typically to provide operation monitoring data, location tracking data, scheduler data and task management data. Tapping into My Sandvik databases, with fleet monitoring data from more than 1,000 units of Sandvik mining equipment, is a key technique that enables extremely accurate predictive modelling capabilities. It goes without saying that stringent data security is an integral aspect of OptiMine Analytics. GDPR compliance is ensured by anonymization of all personal data.

Benefits of OptiMine Analytics

  • Readily available data from fleet monitoring and other business systems transformed into powerful predictive insights and actionable knowledge
  • Available to all types of underground mines and mining applications, across the board in the entire mobile fleet, including non-Sandvik equipment
  • Real-time dashboards for overview, production, operators and equipment
  • Seamless integration across the entire mine ecosystem with API interfaces
  • Over 40 percent more precise predictive analytics models compared with conventional models built without industry-specific application expertise
  • Fewer production losses
  • Higher efficiency over the fleet life cycle through predictive maintenance
  • Higher production quality thanks to operator competence development

Safe protocols and encryption technologies are used for all data exchange and storage operations to create a secure cloud environment. Redundant access control systems ensure that users can only access their own data. Sandvik was one of the first mining equipment suppliers to provide an Interoperability Policy to outline the principles of data accessibility, fleet data compatibility and data privacy in compliance with the GDPR. The backbone of OptiMine Analytics is the descriptive component that shows the current and historical values for the equipment, operator and productivity data integrated from all the available data sources. Based on these machine- and operator-linked KPIs and availability rates, the system indicates and visualizes the overall equipment efficiency (OEE) in a few uncluttered parameters: How does the actual production tonnage compare with the target? What is the breakdown of OEE losses in terms of their main causes? But OptiMine analytics is not simply a performance dashboard. It builds on the descriptive analytics data to offer predictive and prescriptive analytics. While the descriptive component answers questions such as “What was the availability figure for this loader during the last six months?”, the predictive analytics offer answers to questions like “Which component in this unit is likely to need unplanned maintenance next month?” The prescriptive analytics, meanwhile, advise the operator on how to avoid the predicted issues, such as by replacing the component that is likely to become faulty.

The real question is how to translate data into actionable insights, and that is where we can offer a truly unique solution.

Expressed on a more general level, the predictive analytics forecast potential problems and bottlenecks in mine operations, and the prescriptive analytics offer specific, actionable recommendations to increase overall equipment efficiency and productivity. In addition to performance fine-tuning and maintenance measures directly related to equipment, the analytics data can also be utilized for optimizing production cycles or identifying potential training topics.

A longer-term approach can include pre-emptive maintenance schedules, which again helps to minimize unplanned downtime and improves productivity. A major strength of OptiMine Analytics is that these predictive models do not rely on data from a single mine or customer. Through My Sandvik, the system leverages the big data obtained from Sandvik’s entire customer base. Thanks to this force multiplier, the accuracy of the predictions is superior to other comparable solutions, and this also keeps the capabilities of the system progressively improving. As with all technology, software and hardware alone are simply tools. Gaining real impact and results also requires qualified people to operate the systems. In addition to building up in-house capabilities, mine operators can make use of OptiMine 365 service. This practically means that Sandvik mining experts and data scientists join forces to help mine managers find bottlenecks or other issues critical for their specific operations. “The data is already out there,” Mannonen says. “Sensors and systems are producing it and databases are storing it all the time. The real question is how to translate data into actionable insights, and that is where we can offer a truly unique solution. OptiMine Analytics essentially processes data to information, knowledge and, at the end of the day, real-life OEE and productivity improvements.”

OptiMine Analytics and Petra Diamonds

Petra Diamonds, a leading independent diamond mining group, operates the Finsch mine in the Northern Cape province of South Africa. Finsch is a globally significant diamond mine and South Africa’s second-largest diamond operation by production. The mining operations, using block cave and sub-level cave methods, started in 1967 and currently extend to the depth of 700 metres. Petra Diamonds says a key factor in favour of starting a joint analytics project was Sandvik’s commitment and in-depth understanding of the local challenges. “The major impact at Finsch Mine has been [that it allows] us to track performance of both machines and operators, track the completion of various tasks and minimize potential delays and the re-assigning of resources during the shift,” says Alex Holder, group technical service manager. “We now have the ability to change to plan B on the fly,” he says. “Predictive maintenance and other predictive algorithms will in the future lower the need for implementing a plan B all the time.” OptiMine Analytics also turned out to be an effective training tool for Petra Diamonds. Improved visibility of the fill factors of trucks and loaders has driven better awareness of the productivity impact of these parameters among the operators. This resulted within a few weeks in improvements in truck and loader tonnages of 6 percent and 9 percent, respectively.