By Cay Mims and Collin Ziemerink

When examining mining asset and equipment availability statistics, Mark Twain’s warning about “lies, damned lies and statistics” springs to mind. Why? These figures are often difficult to explain and incomparable between sites or across mining organizations.

Disparate groups develop their own divergent assumptions, which are explained and dismissed as “how we’ve always done things around here.” Furthermore, as frameworks for viewing availability and utilization become entrenched, daily users of the information often fail to realize that the data no longer reflects the actual status of equipment. The result: accepted ways of working obscure operating inefficiencies. Without visibility into true asset utilization, managers tend to accept the status quo, especially when the numbers “look pretty good.”

But looking good isn’t good enough. Inaccurate and incomplete utilization figures lead to asset waste and profit loss. Mining companies that effectively quantify mobile mining equipment availability can significantly improve asset utilization and return on investment. The recent experience of a major bulk commodity operation in Brazil provides a useful illustration.

Dispelling Measurement Mythology
At the mine, management routinely reported mobile equipment availability percentages to be in the high 80s to low 90s. Therefore, mine managers and site leadership were confident that no significant operational improvement opportunity existed in mobile equipment availability or utilization. Executives at the corporate level, however, suspected otherwise, and asked for our help in identifying areas for productivity enhancements.

To begin, our team collected all available information on the mine’s mobile equipment. This included data for wet and dry seasons, as well as equipment status for the entire mobile fleet. Working with mine maintenance engineers and IT staff, we cleansed and standardized the data, clarified definitions, and eliminated outliers and data entry errors. The maintenance staff and team then assigned the data into simple categories: corrective maintenance, preventive maintenance, operational delays, internal unproductive hours, external unproductive hours and effective working hours.

Finding the Truth
Examination of the data revealed that the truck fleet had only 38% effective working hours per day. Operational delays—the time spent waiting for work or for instruction—constituted 32% of hours, on average (See figures below). Unproductive working hours—for instance, operators missing or on breaks, equipment being cleaned, etc.—accounted for another 20%.

The data for individual pieces of equipment exhibited even greater variance, and included extreme results that in many cases we could not eliminate as outliers. For example, the improvement opportunity for one truck's utilization was 24%, and that was just to attain the 85th percentile level of performance. Needless to say, the findings shocked everyone.

Making Informed Changes
All of the surprising revelations were the outgrowth of a highly cooperative approach to collating and standardizing data. Departmental groups and management teams agreed to collaborate and implement a more rigorous measurement methodology, and after seeing the initial findings, they accepted that their previous practices had failed to maximize asset availability and utilization. Armed with new visibility into true asset performance, the mine took rapid action to reduce the delays and unproductive time that its incomplete measurement methods had previously concealed. After a comprehensive, 10-month improvement initiative spanning more than 1,000 maintenance and operations personnel and contractors, the entire team is now confident that the key performance indicators they use make operational decisions reflect real operational status.

Transparency, simplicity and sensibly challenging the status quo are all paramount when seeking to identify operational productivity opportunities. The rewards of statistical scrutiny are clear: rigorous measurement often reveals robust opportunity. This mine’s subsequent ability to enhance asset utilization and return on investment proves that whether you’re mining metals or mining data, it’s amazing what you find when you start digging.


Cay Mims and Collin Ziemerink are senior analysts in the Mining & Metals Practice of The Highland Group, a global consulting firm.

 

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