Over the past decade, technology vendors have either developed or consolidated to offer a full suite of information-based business intelligence solutions for every mining process, from production drill monitoring, fleet management systems through to integrated mine planning/performance management solutions. The mining industry is now immersed in the information age, drowning in the vast amount of data, yet starving for practical information.
The advantage a specific vendor’s technology provides is often dwarfed by the enormous impact implementation and business process change plays in the outcome. Such changes can almost never be delivered by the vendors themselves. Any claims that complex business intelligence technology — any technology that uses information for making improved decisions — is easy to implement and use to its fullest extent are false.
To effectively use these technologies and the data they generate requires education in information technology, systems engineering, business process redesign, and information management, which presents a distinct problem: none of these areas are part of a typical mining engineer or manager’s education.
The mine of Babel
In the pit, equipment monitoring technology has several benefits, primarily as an aid to the equipment operator. For example, a dozer operator using GPS-based dozer monitoring can have a virtual section and plane view of his machine and the position of the blade with respect to the ground, so that he can cut or fill to very exact specifications. The benefits of such operator-aids are relatively easy to achieve. This technology can also generate performance information, such as the productivity of the operator, delays or equipment health information.
Taking a step back to make full use of this range of data requires much more effort, due, in part, to the challenge of integrating the databases from the range of these technologies because mines typically use products from a variety of vendors, which rarely truly cooperate. The industry must adopt the next order of innovation, a vendor-independent business intelligence system, to convert this data into useful and productive information that can help guide decision making.
Building a warehouse of data
In practice, mine IT support staff do not have the skill set to develop nor maintain a business intelligence system. That role belongs to a business intelligence analyst who not only mines the data, but analyzes the findings, allowing decision-makers to devise an operational response. Most vendor products are delivered with web-based reporting tools; however, those reports or dashboards are for keeping score or driving behaviour, not for analysis or problem solving, especially not in the multi-vendor patchwork found in most contemporary operations.
The development of a data warehouse is an essential first step in extracting greater value from the expanse of data. A data warehouse is a subject-oriented, integrated, time-varying, non-volatile collection of data, used primarily in organizational decision making. In laymen’s terms it is a database of databases, linked in a manner that allows a business intelligence analyst to view data from a multitude of systems in a single format; in the case of the dozer, information relating to operating cost, maintenance records and productivity information often located in separate information systems. Traditionally, if mine planners want to determine dozing costs per unit of productive work, they would likely have to consolidate data from all those sources into a spreadsheet. A data warehouse can process a substantial amount of data in less time, collect data from multiple sources, adapt information from users, and track processes over time in a far more controlled and sustainable manner than the traditional monster-spreadsheet approach.
Within the data warehouse, the business intelligence analyst can mine the data using statistical algorithms or queries tools such as Online Analytical Processing (OLAP) cubes. OLAP cubes are a way of presenting information, where all possible combinations of data are pre-calculated during a cube processing step, allowing users to query extremely large data sets using complex calculations almost instantly. These tools can determine the causes of under- or over-performance of a specific business process and provide a range of practical solutions.
To avoid corporate exhaustion with underperforming technologies due to vendor overpromises and internal implementation challenges, experienced technophiles should communicate potential pitfalls so that they can be avoided. Due to time constraints, it is increasingly difficult for senior mine or corporate managers to participate in such knowledge sharing conferences, which leaves online or printed forums the ideal form of communication.
Business intelligence technology can have a powerful impact on contemporary mining. Common industry missteps, such as thinking that to gain the benefits of such technology one can simply purchase it from a vendor, can be avoided. The industry requires a forum to garner advice from a variety of experts and showcase examples of business intelligence technology. Case studies are particularly important, since examples of less-than-successful implementations of technology are rarely discussed candidly. Currently, true experiences with technology are often communicated at international mining conferences, but only outside of the formal technical talks.
Sean Dessureault is an associate professor at the University of Arizona’s mining and geological engineering department and president of MISOM Consulting Services Inc. He has researched and consulted widely in automation, IT and sustainable development, and provides service to the industry through short courses and leadership roles in mining computer applications and continuous improvement symposia. firstname.lastname@example.org