How a focus on equipment performance promises to unlock billions of dollars in productivity returns for miners. August 2014 Mining for efficiency
www.pwc.com.au/mib Executive summary
Australia’s declining productivity is one In this report we have diagnosed the Table 2 for differences by class of of the most important challenges for extent of the productivity challenge at equipment), the majority of which our economy. It calls into question the both a macroeconomic and operating can’t be attributed to different path to future prosperity and our global level, in Australia and across the other mining conditions or embedded competitiveness unlike any other topic. major mining regions. For the latter, we issues associated with existing mine And when it comes to productivity, no have drawn upon 20 years of operating plans. For example, hard rock mining industry has received greater attention performance data from 136 mines and conditions are a well-worn excuse of late than mining. 4,760 individual machines – in all, for poor productivity performance, this represents more than 47 million when in fact the data reveals there With the evolution of new technology operating hours. are many mines digging very hard and mining methods, combined with materials who are achieving best projects of ever increasing scale, Key findings practice. The extent to which these one might have reasonably expected variances are monitored, rationalised productivity in the Australian mining • The global mining industry’s open or dismissed is unclear as data sector to have increased over time. But cut equipment productivity (ie capture management practices are for a range of reasons, at an industry annual output / capacity of input) still evolving compared with many wide level, the reverse has actually been has declined by 20% over the past other industries. The Tier 1 assets the case. seven years despite a push for increased output and declining have the best ore bodies in the world. The popular tagline of the mining market conditions. Imagine how profitable they would sector is that the miners are serious be if they also delivered best in class about productivity. We suggest that • Mining equipment in Australia runs productivity performance. most are reducing costs and increasing at lower annual outputs than most • Productivity is heavily dependent volumes but there are precious few of its global peers. Australia is not on the way people act. A better with legitimate claims to improving best in class for output from any rated piece of equipment might core productivity in their open cut category of equipment and is below deliver 5-10% output improvement, operations. Miners are banking the the annual output of North America and require additional capital, first available dividend, selling or across all classes of equipment. but changes in the work practices segregating mines deemed too hard • There is an inherent conflict between can, in our experience, deliver to fix and tempering expectations of a productivity plan based on 20%+ gains, often at little or no further productivity gains by citing increased volumes and one based cost. Again, industrial relations a combination of labour laws, high on cost reduction. Those mines with issues are perceived as the primary costs, regulatory hold ups and mine well delineated strategies which are constraint to productivity, yet the configuration constraints. There is no followed with discipline by their data shows significant divergences in question that sustainable productivity people make up the majority of those performance from mines operating dividends are harder to achieve, but if achieving top quartile equipment in close proximity, chasing the same tackled properly they will drive superior performance. commodity, and under very similar long term returns. • Company-wide equipment IR conditions. Many have been quick to point the performance for many global miners finger at the overhang created by sit in the second and third quartiles, the volume maximisation strategies and the differences between their that prevailed during the commodity best and worst performing mines are boom years, where absolute output stark (see Figure 5). The differences was deliberately prioritised. But between median performance and understanding why productivity fell best practice output by equipment during this period, and has continued to category can be over 100% (see fall since, is a complex issue.
2 Mining for efficiency Implications • Mining companies understand implicitly that productivity carries a value, but are not armed with the right data to make informed choices on the risks/rewards involved. Costs deferred or eliminated, as well as volume increases, have become the proxy for productivity gains. What’s more, in the current environment there is little patience for a productivity dividend that might be six or twelve months in the making, let alone one that needs an outlay of substantial capital to get there. • Sizing the productivity prize will vary for each mine. To give some sense of the magnitude of the upside we considered the gains that could be made for a single item of equipment moving from median to best practice annual output, and then applied a conservative cost per • Benchmarking of equipment The implications for improving tonne (representing the marginal performance has generated productivity in the Australian mining cost of having that incremental significant gains in some quarters sector are clear. Companies serious material being moved by some and served to highlight diminished about both cost control and productivity other method, by an additional performance for others. On page 13 need to have a greater focus on the loader, truck, excavator, etc) – refer we provide brief case studies from efficiency of their equipment. This Table 3. As an example, a front end mines across the globe. means stepping beyond short term cost loader of average bucket capacity reduction initiatives and a preoccupation • In our view the easiest gains can be that could shift from median to with extra tonnes leaving the mines. It’s made in the areas of payload and best practice would increase annual about what’s happening inside the gates availability. Annual performance is output by 6.1 million tonnes and that is the key to arresting the industry’s more highly leveraged to payload generate cost savings of between productivity decline. $1.50-2.00 per tonne. (ie a return than any other metric, yet this is of $9 – 12 million per annum often overlooked. Maintenance per machine). Best practice may practices can make the difference not be possible on all sites, but between equipment achieving apply this benefit to a substantial typical availability rates of 85% and portion of a miners fleet and the those achieving best practice of 90% financial upside quickly mounts. or more. Again, some examples are included on page 13.
Mining for efficiency 3 The productivity doldrums
Understanding why productivity fell mining sector. We need to recognise Drawing on this database, PwC has following the commodity boom is a that these measures are not based on developed a number of metrics that complex issue. There are a range of an understanding of how individual allow companies to better understand impacts arising from the increasing mines perform at an operational level. the operational-level drivers of scale of open cut mines and complexity While they help identify and support productivity. For example, PwC has of mining operations, which may at underlying trends, they cannot provide developed the Mining Equipment first glance seem counter-intuitive. For the detail required to make optimised Productivity Index (MEPI), which example: strategic decisions and comparisons provides a more precise estimate of the with other mining nations are difficult. productivity of mining operations by • Performance actually decreased as measuring the physical output of the equipment capacity increased for A more detailed understanding of mine equipment. draglines, hydraulic excavators and productivity in mining based on front end loaders (see Figures 10, 20 operational-level has been made Our operational-level analysis has & 25 in the Appendix). possible by data collected by PwC’s revealed that: Mining Intelligence & Benchmarking • For some equipment manufacturers • The global mining industry’s open practice1. Our database is the leading new, larger models have not cut equipment productivity has source of information about the produced immediate, proportional declined by around 20% over the productivity and reliability of open cut improvements. For example, OEM past seven years despite a push for mining equipment in the world. 1 in Figure 29 of the Appendix, increased output (see Figure 3 of the produces decreasing unit The database constitutes performance main report) performance as the models get larger. data sourced directly from equipment • Australian mining equipment appears monitoring systems over a period of 20 • During the boom years some mines to be run at a significantly lower level years. The data covers five continents were forced to acquire equipment of annual output compared to most and 136 mines. It includes 308 different which was available rather than other mining countries (see Figure 4 makes and models with over 4,670 what they really needed and at the of the main report) individual machines and more than same time due to talent shortages 12,000 years of operating data. Those • There does not appear to have recruited relatively less skilled machines have more than 47 million been effective or significant change labour to operate it. operating hours and more than 700 in operational mining strategy The recent downturn in commodity million cycles. during this time, despite changes in prices in particular has now led mining commodity prices. The approach that has been developed companies to take strong steps to within the database seeks first to This last point was particularly evident improve productivity. Many have stated normalize for a range of factors during 2011 – 2013 where a number publicly that productivity is top of outside the mine’s control (including of commodities declined in price their agenda and they are extolling the commodity being mined, what is being significantly (for example, coal and virtues of cuts to employee numbers dug, location - including weather, gold) but a detailed examination of and spending. pit geometry and make and model of mining productivity data from mines But is productivity actually improving? the equipment) and then measures with those commodities revealed a lack The short answer is that it depends best performance. This determines an of change in mine site strategy, despite on what measure you use. This report optimum performance level against what the miners may have stated looks at a number of publicly available which variances can be measured for publically. macro performance measures typically that particular mine. used to describe productivity in the
1 Formerly GBI Mining Intelligence, which was acquired by PwC in September 2013
4 Mining for efficiency What the macro measures reveal
Mining productivity is typically Figure 1: Labour and Capital Productivity (Source: ABS) described by reference to publicly available data from the Australian 120 Bureau of Statistics (ABS), the Reserve
Bank of Australia (RBA) and the 110 Australian Bureau of Agricultural and Resource Economics and Sciences 100 (ABARES). The publicly available data is largely 90 focused on labour, capital, capacity and 80 output. Whilst important for monitoring industry-wide trends these metrics do 70 not provide sufficient understanding Indeces (#) (2003 = 100) of what’s happening at the operational 60 level to enable executives to make changes to their strategies that will 50 maximise productivity. Furthermore, the metrics do not appear to support 40 the recent productivity claims of mining companies, which argue that following 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 reductions in headcount and spending they are now ‘doing more with less’. The ABS Labour Productivity Index ABS Capital Productivity Index following sections explain why. and 2009-10 and may be just ‘noise’ in PwC maintain that while labour and ABS labour and capital an otherwise downward trend. Capital capital should be important aspects productivity indices Productivity has fared even worse, with of analysis regarding the financial no substantive increase since 2001. sustainability of the mining industry, If productivity in the mining sector they should not necessarily be the were simply about the deployment In previous analysis we have released primary focus for understanding or of labour and capital, then we would on mining productivity it was clear that improving mining productivity. expect to see the headcount and austerity approaches have largely failed cost reduction strategies recently to deliver improved productivity2. For adopted by the miners leading to example, despite widespread reports a demonstrable improvement in of redundancies and operational productivity. But it’s simply not the case. headcount reductions in the mining industry in 2013, Labour Productivity An analysis of the ABS’s Labour and has only risen 3.2 per cent. And even Capital Productivity Indices reveals though the capacity of new equipment only limited gains of late (see Figure being commissioned decreased 44 1). The Labour Productivity Index per cent in 2013 compared with 2012 in 2013 was just 3.2 per cent higher (Parker Bay Mining), the Capital than 2012. This is less than the minor Productivity Index still fell by 7 per cent. corrections that occurred in 2006-07
2 Productivity not austerity: Productivity scorecard – mining focus, PwC, 2013
Mining for efficiency 5 Capacity and As commodity prices rose over the past Presuming a consistent split of open cut decade, so did the mining industry’s and underground mining over the 10- output metrics investment in capacity, which has also year period, the total increased capacity The decline in labour and capital been well above the long-term trend. acquired has been nearly eight times productivity in the mining industry Figure 2 shows that from 2003 to the aggregate amount of additional since 2001 has been broadly attributed 2012 the Australian mining industry commodities sold from these mines. to companies making a shift from a annual investment in new earthmoving Productivity, therefore, has gone down cost conscious strategy in the 1990’s equipment capacity3 increased by 17 per as the rise in inputs is greater than the (minimising marginal costs and cent, on average. rise in outputs. Clearly, attempting to exploit high commodity prices has cost accepting lower output volumes) to a Production output4 data indicates that mining companies dearly. volume strategy from the mid 2000’s the industry was not, however, able to (achieving the maximum output effectively capture the opportunity high Understanding the reasons for the despite increasing marginal costs) commodity prices presented. gap between investment and output in order to capitalise on the boom in (from Figure 2) is critical to improving During the same 2003 to 2012 period commodity prices. But a deeper analysis Australian mining productivity. reveals that the causes of decreasing the aggregate commodities actually performance are not so simple. mined grew by an annual average of PwC data reveals that the main reason 5 per cent a year, just a small amount for this discrepancy is that most of the ahead of the industry’s longer-term productivity issues are operational. increase of 4.2 per cent.
Figure 2: New Australian Mining Equipment Capacity (Source: Parker Bay Mining) and Aggregate Australian Mining Output (Source: RBA) 1988-2013
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0 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Capacity of New Equipment (Parker Bay Mining) Aggregate Commodities Mined
3 Source: Parker Bay Mining 4 Source: RBA
6 Mining for efficiency What operational-level data reveals about productivity
Productivity across the Figure 3: PwC’s Aggregate Mining Equipment Productivity Index (2003 = 100) industry: MEPI (Source: PwC’s Equipment Productivity and Reliability Database)
Because understanding what’s 115 happening at the operational level is so crucial, operational-level data is the key to improving productivity in 110 the Australian mining sector. To this end, PwC has developed the Open Cut 105 Mining Equipment Productivity Index (MEPI). 100 The MEPI measures the efficiency of open cut mining operations by Indeces (# ) comparing how much material 95 mining equipment is moving from one period to the next. It draws on 90 performance data from a combination of equipment: dragline, rope shovel, hydraulic excavator, front end loader 85 and truck performance, and is based on PwC’s proprietary mining equipment 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 productivity and reliability database.
PwC’s MEPI supports the claim that the Figure 4: PwC’s Mining Equipment Productivity Index by Region efficiency of mining equipment across (Source: PwC’s Equipment Productivity and Reliability Database) the sector is in decline. Equipment operating efficiency reached a peak 140 in 2006, and has decreased ever since (Figure 3). In 2013 it was 18 per cent 130 lower than for 2006. 120 On a regional basis (Figure 4) mining equipment performance has been 110 declining at different rates across all mining jurisdictions. Australia’s mining 100 equipment productivity underperforms our international competitors with the 90 exception of Africa5. 80
70 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Africa Australia South America North America Asia
5 The issue about Australia’s relative position in the global mining industry is a substantial topic and is beyond the scope of this paper. Australia’s position is a function of work practices, culture, leadership, strategy, etc. and not simply a function of poorer equipment or the environment. This is an issue that needs to be taken up and studied in more detail.
Mining for efficiency 7 It is important to distinguish the declines Figure 3A: PwC’s Mining Equipment Productivity Index - Median vs Best Practice represented in Figure 3 and Figure 4 (Source: PwC’s Equipment Productivity and Reliability Database) from any suggestion of structural shift 200 in the industry due to non-controllable factors (eg. increased emphasis on safety, more difficult mining conditions, 180 changes in labour laws, increased government regulations, etc). As Figure 3A shows, although median performance 160 has declined since 2006, the gap between median and best practice, 140 what we regard as the total productivity opportunity, has grown. Index (# ) 120 Productivity of different types of mining equipment 100 Equipment-level data drawn from our database also reveals the performance 80 of individual classes of equipment over the past 10 years, including 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 draglines, rope shovels, excavators, Median (50th Percentile) Best Practice (95th Percentile) front end loaders and trucks. A detailed breakdown of performance data These falls reveal an enormous for the different classes of open-cut Table 2: Best practice equipment output opportunity cost associated with the mining equipment is included in the gain versus median output, 2013 loss in material movement across the (Source: PwC’s Equipment Productivity Appendix. Following is a brief outline industry worldwide. It should also be and Reliability Database) of the key insights from that analysis. noted that best practice has fallen by Dragline 56% The median productivity of all classes of less than median. The reasons for this Electric Rope Shovel 64% equipment across all mining jurisdictions seem obvious. One characteristic of best Hydraulic Excavator 85% and commodities has fallen since 2006 practice is that these mines continually (see Table 1 below). There are, however, focus on what they have to do to not Front End Loader 156% significant differences in performance only maintain performance, but to keep Mining (Haul) Trucks 82% between different countries. improving. That same focus and drive is not evident across a large percentage of It is proposed this represents the Table 1: Reduction in median productivity the industry. potential gain for the median machine of equipment in 2013 since the 2006 peak in each class. Whilst no two mines are (Source: PwC’s Equipment Productivity and Interestingly, Australia is not best-in- the same, our experience, supported by Reliability Database) class for output from any category of extensive operational data, provides few Dragline -20% equipment and is below the annual reasons why most equipment cannot output of North America across all Electric Rope Shovel -21% achieve close to best practice levels of classes of equipment. Australian annual performance. Hydraulic Excavator -14% output relative to Asia, South America Front End Loader -23% and Africa is also generally lower. Mining (Haul) Trucks -32% The difference between best practice and median is also wide in all classes of open cut equipment (See Table 2).
8 Mining for efficiency Productivity of global • There is a high correlation between Sizing the prize some of the poorest performers in the mining companies table and those with a poor financial Table 3 below outlines the potential Figure 5 below plots the equipment and share price performance in the gain in MT per annum by equipment performance of ten large mining past 12-24 months class from moving from the median performance to best practice for each companies, showing the best, worst • There are significant divergences and average performing mines within class of equipment. Using a conservative in the locations of better and worse cost model, each of the tonnes “lost” their portfolios. Companies names are performing mines. Some miners necessarily removed, but it is worth costs a mine between A$1.50 and have their best performers in South A$2.00 (which is the cost of having to noting that most were included in America, others have their poorer our recent global Mine 2014 report move those tonnes with another truck performers in that region. Similarly, and loader fleet) except in the case of a covering the largest 40 miners by market for those with operations in Australia, capitalisation.6 dragline, where the cost is around $0.50 we can identify best in class for some per tonne. Once you start factoring in The analysis reveals some telling miners and worst for others. the size of a fleet and the number of comparisons with how the market is • Some of the most productive mines mines in a portfolio, even capturing a viewing productivity performance and are those that have been offloaded small portion of this upside will quickly how it is being portrayed within the in the past by the majors. It could amount to a prize measured in tens of industry. For example: be argued that some of these have millions of dollars per annum. • Many of those represented in the to be productive, and are often chart have poor performing mines raising the bar on best practice, Table 3: Best practice equipment output gain within their portfolios, but these because they operate on very slim versus median output, MT per annum, 2013 (Source: PwC’s Equipment Productivity and aren’t necessarily the ones identified margins. Productivity improvement is Reliability Database) as marginal or underperforming in necessary to survive. Dragline 18.4 their operating or financial reports. • Some miners who have announced Electric Rope Shovel 11.9 Similarly, some commodity groups or productivity improvements have Hydraulic Excavator 11.4 specific operations heralded as high focused their benchmarking on achievers on a broader productivity internal comparisons and as a Front End Loader 6.1 scorecard have relatively low result are found outside top quartile Mining (Haul) Trucks 1.6 equipment efficiency. performance across the industry.
Figure 5: Mining Equipment Performance by Selected Large Mining Company (Source: PwC’s Equipment Productivity and Reliability Database)
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0% ABCDEFGHIJ Company average Best mine (top 5%) Worst mine (bottom 5%)
Example Mining Companies
6 Mine: Realigning expectations, PwC, June 2014
Mining for efficiency 9 Assessing mining Figure 6: Open Pit Loader and Truck Performance, 2003-2013 (2003 = 100%) strategy effectiveness (Source: PwC’s Equipment Productivity and Reliability Database) One of the key benefits of using 120% equipment-level data is the ability to assess the effectiveness of a mining 115% strategy. This can be done across the industry, or for an individual mine. 110%
As previously noted, mining companies 105% tend to adopt to varying degrees, one of two broad business strategies: 100% a volume strategy (achieving the maximum output despite increasing 95%
marginal costs); or a cost strategy Unit Output (2003=100%) (minimising marginal costs and 90% accepting optimised output volumes). 85% When implemented effectively, these strategies follow a typical pattern of 80% equipment use. Under a volume strategy, an increase in loader output and a 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 decrease in truck output usually occur. Loaders Trucks This is because companies will invest in more trucks to ensure there is no idle the previous period of lower commodity Furthermore, when Figure 2 is time at the loader. One consequence, prices rather than a demonstration of an considered, it can be seen that the however, is that trucks are often sitting effective volume strategy. industry predominantly responds to idle waiting for their turn to be loaded. commodity prices when investing (or The efficient execution of a cost strategy not) in new capacity, with apparently Under a cost strategy the opposite is the after the 2003–2011 boom would have little focus on the best strategic approach case. A high focus on costs generally seen individual truck performance for existing capacity. means fewer, and therefore more highly increase (as numbers were optimize utilised trucks, while it’s the loaders that to minimise unit cost) and loader In summary, analysing productivity often sit idle waiting for them. performance level off or even decline. at the equipment level casts doubt on Our analysis of equipment productivity The fact both loader performance and whether the industry has responded well shows that overall the industry has truck output declined during this time, to changing economic circumstances at not been very effective in the adoption however, creates doubt that it was a any time during the past 10 years. of either a volume strategy or a cost strategy-related result. strategy. Our interpretation of Figure 5 is that the During periods of high commodity industry’s strategic response to market prices, such as occurred over the past conditions over the past decade was not decade, many mining companies sought optimally effective, for the following to adopt a volume strategy. An effective reasons: uptake of the volume strategy during • Truck output rose from 2003 to the 2003–2011 boom would logically 2006 – the effective execution of a have seen loader performance increase volume strategy during a boom would and truck performance decline during typically see more trucks used with this time. This was not the case however, individual truck output falling. with both truck and loader performance increasing and then decreasing over the • Loader output declined from 2009 decade (Figure 6). to 2011 – the effective execution of a volume strategy during a boom would Although loader performance did typically see more trucks used with improve through to 2009 the falls in loader output increasing. 2010 and 2011 were not expected from an industry attempting to maximise • Truck output declined from 2011 to output. The fact that both loaders and 2013 – an effective execution of a cost trucks increased from 2003 to 2006 is strategy during a bust would typically likely more a function of the take-up of see fewer trucks used with individual underutilised capacity resulting from truck output increasing.
10 Mining for efficiency How to improve operational productivity at a mine-site level
Based on our experience and data- Key factors for mining productivity execution and success driven insights, we have identified three key factors that miners should address to improve equipment efficiency and in turn improve productivity. Our analysis suggests that three key factors (see graphic right) have the largest impact on mining execution and Mine Strategy Data Management People success and should be top priorities for all mining executives and general managers. Mine strategy: define, plan, Data management People: identify and articulate and execute system: develop recruit people with the Mine Strategy a clear mine strategy, an equipment-level right “abilities” (what including expectations performance data one is born with) for As the equipment performance for specific equipment management process each job; then provide data shows mine strategy was not productivity and removing and use it to steer daily proficiency-based necessarily optimised for economic and impediments. decision-making. training to all levels market conditions, even when a strong within the mine financial imperative to do so existed. This was especially true during commodity price downturns, where Developing a clear mine strategy is Data Management mine managers often requested across technically easy. However, translating the board cost reductions, which often a volume or cost strategy into clear System conflicted with asset optimisation. equipment performance metrics is Many industries have embraced the These directives tended to lead to often difficult due to the complexity use of data to drive decision-making reduced mine performance. of interlinking processes on the mine and rely on methodologies like TQM On the other hand, mines that had site. The challenge should not be and Six Sigma to bring about step- clear cost or volume strategies often underestimated. changes in performance improvement. articulated equipment-specific Additionally, communicating with The mining industry, however, has targets that led to significantly better senior management and operators embraced the ‘data acquisition’ stage, performance during both boom and about equipment performance trade- but is yet to embark on the ‘data use’ bust periods. PwC’s own data and offs and gaining approval for targeted stage of performance management in a experience with mining companies equipment performance reductions significant manner. indicated that mines with well- when the established mantra has been Data overload has caused many managers articulated strategies represented more “productivity above all” can be daunting to question whether data can help them than 80 per cent of companies achieving for even the most seasoned mine make better decisions. Many mines top quartile loader performance veterans. simply do not use data and information (volume strategy) and 80 per cent of that could potentially lead to significant companies achieving top quartile truck productivity improvements. performance (cost strategy).
Mining for efficiency 11 A number of miners say they review regularly. Tailored analytic methods cause of this decrease is not clear, it performance data on a monthly or and reports should be developed and is known that the natural abilities of quarterly basis but don’t trust it for then incorporated into a process of operators (those related to productive decision-making. Others, however, data-based management. Best practice operation of large earthmoving embrace internal and external mines often have a ‘data steward’, equipment) are approximately 10 per comparisons to support business someone whose responsibility it is to cent lower now than 20 years ago. improvement as well as the analytic collect and store this kind of data. That means selection of operators approach to management. On the is being done less efficiently now. whole their mines are well rewarded. People Of concern here is the fact that the Mining companies that gather, The full potential of a clear mine previously dominant system of selection analyse and use their data on a daily strategy and sound data management by seniority has been replaced with a basis account for 92% of top-quartile systems will not be achieved if the system where mines may choose the equipment performance. mining company lacks the right people people they want. The use of available data is a proven and skills. The process of training to initially distinguishing feature of mines that Selecting the right people, particularly achieve competency and eventually achieve outstanding equipment at the operational level can lead to proficiency in any given task requires performance. Measurement and data- higher productivity. Research in Human constant attention and it is apparent management systems should be in place Factors Engineering has found that from the data that the required to accurately and consistently record all accounting for individual differences attention has waned. It is known that key performance indicators, including during selection and recruitment can the skills acquired through training and productivity, time management, fleet increase equipment output by up to experience are 3 to 4 per cent lower management and safety issues for all 14 per cent. (relative to what they are capable of) mining equipment. now than they were 20 years ago. Mining companies need to recognise As a minimum, payload should be the role of both people selection and Finally, it’s important to recognise that recorded for every cycle plus load and proper training of those people. The these three factors – strategy, data time-based events in the cycle. Problem importance of selection is particularly and people – must be considered as actions, (such as speeding trucks in salient considering the fact that over an integrated whole. Mines achieve corners, shovels digging incorrect R.L’s, the past 20 years average operator exceptional performance when they under or over trucking, etc.) should be performance has declined. In 2007 it value the experience of their people and identified and recorded. The database was 9 per cent below the performance attempt to contextualise this experience structure should be documented achieved in 19937, and from our recent with data-derived knowledge and a and available. Internal and external experience we believe that the situation clear mine strategy. benchmarking should be undertaken has not improved. While the precise
7 Lumley G 2007, Improving Dragline Operator Selection And Support Processes, University of the Sunshine Coast, Thesis submitted for the degree Doctor of Business Administration. Case studies
We have summarised below examples charged with reviewing all activities Why benchmarking alone of successful turnarounds where to give priority to filling every load equipment benchmarking drove (loader and trucks). This had the is not the solution operational changes which in turn paid a flow-on effect of improving their truck 1. A gold mine was being developed. handsome dividend. On the other side of and loader matches so that fewer A benchmark was identified within the coin, we summarise situations where trucks were sent away from the loader their mine planning assumptions underlying flaws in the approach to not full, and there were less loader that required two large rope shovels benchmarking hampered progress. part loads putting the last tonnes in to have usage rates equivalent to the the truck. They subsequently achieved 95th percentile. For the first few years Successful outcomes the equivalent of best practice the mine failed to meet production 1. A mine with multiple loading performance. targets and failed to use the data units and trucks benchmarked the 3. An Australian dragline was very low from the benchmark to improve. The equipment against the performance of in output. The mine developed a shovels have never achieved the rates similar makes and models. In response program of improvement activities used in planning. They have now this mine immediately started working such as optimised selection of new achieved target total mine output on their utilisation to address large buckets, increasing target suspended through the acquisition of additional gaps between their performance load, improved diggability from capacity (which came at additional and best practice in operational blasting, improved availability and cost). The basis for the company’s standby time; especially under “No utilisation, changing their input reserves statement and the feasibility work available” and “No operator”. layout, and they changed some of studies were subsequently called Their actions were targeted around their operators; all which was assessed into question. This mine has never having the optimum number of trucks against quarterly benchmarking. They returned the projected ROI. allocated to each loader. Further, they targeted a 5% improvement every 2. A number of mines which have established a more flexible approach quarter which they achieved in eight conducted benchmarks of their open to which trucks reported to each successive quarters. cut equipment have failed to act on loader. After three months the mine 4. An Australian dragline was very the recommendations. When the reported improvements in individual benchmark is repeated, a remarkably loader and truck utilisation (of up to high in output for every year for five years in a row. They still increased similar result is identified which, 10%) which they estimated added without intervention, tends to decline $40M to their annual operating profit. output by 1-4%, despite the fact that they were at or above the 95th slowly over time. For example, a large 2. A coal mine had a 550 tonne class percentile each year. They looked at coal mine was part of a company- excavator. Over three years they had the gap between their KPI’s and the wide benchmarking exercise which doubled annual output from 7 MT to best practice group and focused on was repeated annually for 9 years. 14 MT and subsequently undertook areas where they were below. They At the end of the 9 years the mine’s a benchmark against worldwide pushed the dragline to load past the equipment performance was more performance. They were stunned to manufacturer’s stated load (while than 10% below what it was at the find they were still 32% below best putting suitable controls in place); start. For this mine the main issue was practice. The most significant gap they reduced bucket and rigging falling production hours. Availability between what they were doing and weight and converted it into payload; was constant but non-operating best practice machines was payload. and they increased their production activities increased and as a result In the following two years they time. Their simple aim was to be the utilisation fell. improved output by simply bringing best dragline in the mining company’s focus on to payload – everyone in fleet and they achieved this goal. the truck and loader operations was
Mining for efficiency 13 Appendix: Trends in performance of open cut mining equipment
This Appendix, which is based on data • All data in the PwC Database was probability of falling above or below it. collected by PwC’s Mining Intelligence obtained from third parties. PwC Median can also be described as & Benchmarking service8, provides a has not verified, validated or audited the 50th percentile. detailed analysis of the performance of any of the data in the PwC Database Best Practice means, for each individual different categories of open-cut mining and makes no representations or production or time utilisation KPI, equipment up to and including 2013: warranties regarding its accuracy the average for that KPI calculated or completeness or its suitability for • Draglines from the top 10% of machine years any purpose. PwC is not liable to any (as defined below) for loading units • Electric rope shovels party, for any inaccuracy or error in, in an agreed benchmark population or omission from, any information • Hydraulic Excavators (face shovels when ranked by total annual output. in this document or on which this and backhoes) Best practice will be close to the 95th document is based, regardless of percentile but is not necessarily exactly • Front end loaders (wheel loaders) the cause of the inaccuracy, error or equal to the 95th percentile. That is, omission. • Mining haul trucks the machine years for loading units • The data in this document was in the agreed benchmark population It is a rewritten and updated version based on available data in the PwC are ranked by total annual output, the of the 2012 paper by GBI Mining. Database as at the date of analysis. top 10% of machine years are selected Some of the results are different to and separated out and the average of this previous paper due to additional This analysis has made no effort to each individual production KPI and units available in the data now as well define what equipment was doing time utilisation KPIs calculated for the as some enhancements to comparative during the available time. For example, selected machine years only. techniques. The following points some fleets may be doing clean-up need to be understood prior to while others are doing production Important note: A particular production analysing the results. work. When issues of what a piece of or time utilisation KPI, calculated as the equipment was doing are impacting average of that KPI recorded by the top • Methods and metrics are used performance a further question is then 10% of machine years for loading units which allow comparisons amongst a posed. “Are the particular activities in in an agreed benchmark population number of operations. question the best asset allocation and when ranked by total annual output, • The data which PwC can provide use of capital for this mine?” may be lower than what is achieved in this document is limited by the for the same KPI when considered in data which is in PwC’s Equipment Definition of ‘Median’ isolation. There is no machine in the Productivity and Reliability and ‘Best Practice’ PwC Database which achieves the best Database. Where possible, PwC result in each individual KPI. Further, have classified all data to the PwC Median means, for each individual a number of KPIs in combination are Standard Time Usage Model however production or time utilisation KPI, counter-productive. For example, best such detail was not always possible denoting or relating to a value or practice filling times (lower is better) to represent. For example, not all quantity lying at the midpoint of a rarely provide best practice payloads mines will record all activities down frequency distribution of measured (larger is better). to details such as shift change etc. values, such that there is an equal
8 Formerly GBI Mining, which was acquired by PwC in September 2013
14 Mining for efficiency Draglines Figure 7: Worldwide Dragline Annual Unit Production (BCM/t of RSL) 1994-2013 by Performance The analysis of draglines used annual 130,000 output in bank cubic metres (BCM) (normalised for full year operation) per tonne of rated suspended load (RSL). A 120,000 bank cubic metre is the load in tonnes (as weighed by a monitor) divided by the in-situ specific gravity (tonnes per cubic 110,000 metre). The RSL is a number which the manufacturer places on the machine as 100,000 being a safe working load. BCM / t of RSL Figure 7 presents the trends in median 90,000 and best practice annual output for worldwide draglines from 1994-2010. 80,000 The peak productivity for draglines occurred in 2004 at around 127 000 BCM per tonne of RSL for best practice 70,000 and 98 000 BCM/t for the median 1994 1995 1996 1997 1998 1999 2000 2001 2002 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 dragline. Best practice and median performance declined 14% and 10% from 2004 to 2010 respectively. Since Median Best Practice 2010 the median has declined to 20% below 2004 while best practice has Figure 8: Median Dragline Annual Unit Production (BCM/t of RSL) 1994-2013 by Location recovered to be only 4% below 2004. The difference between median and best 120,000 practice was reasonably consistent up to
2009 with best practice being between 110,000 30% and 32% higher than the median. Since 2009 this difference has grown to 56%. 100,000 Figure 8 is a plot showing the differences between median Australian 90,000 dragline performance and that in
South Africa and North America (USA BCM / t of RSL 80,000 and Canada). These are the three predominant areas where large walking draglines are used. Draglines have been 70,000 employed in Northern Africa, India and Europe but these have not been included 60,000 due to lack of data and the generally
smaller capacity. 1994 1995 1996 1997 1998 1999 2000 2001 2002 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Similar trends can be seen in each area as are seen worldwide. There has been Africa Australia North America a peak between 2003 and 2005 with a subsequent decline. The decline is particularly evident in Africa (-27%) and Australia (-23%). The decline has been less severe in North America (-5%) and has shown a change in trend in the last three years.
Mining for efficiency 15 The final comparison is by make and There is an interesting characteristic are modified to normalise differences model. Figure 9 shows the 2009-2013 of this data which is worth noting and in the RSL. The correlation is good median performance for each make plotting in a different form. The unit although even with an R2 of 0.89 the and model. capacity increases with increasing difference between machines of similar machine size. In the case of draglines RSL can be millions of BCM per year. Each make and model has declined over this is not a strong trend but it is gaining By way of example, the two makes and time but the primary message in this plot strength with time as larger draglines models with RSL around 250 tonnes is the significant differences between have tended to perform better relative achieved 26.7 MBCM and 20.2 MBCM different makes and models. The most to smaller draglines over the last three per year. The 6.5 MBCM difference in productive make and model achieved years. This is demonstrated in the plot material carries a significant value. 106,000 BCM / t of RSL while the least in Figure 10 which is Output versus productive achieved 66,000 BCM / t of RSL. Bigger machines move more than RSL. The lowest is 38% below the top. smaller machines even after the results
Figure 9: Dragline Annual Unit Production (BCM/t of RSL) 2012 by Make and Model
120,000
100,000
80,000
60,000 BCM / t of RSL 40,000
20,000
0 Make and Model OEM 1 OEM 2 OEM 3
Figure 10: Dragline 2012 Output Versus RSL by Make and Model
25,000,000
20,000,000
R2 = 0.89 15,000,000
10,000,000 Output (BCM) Bigger machines move more than smaller 5,000,000 machines even after the results are modified to 0 0 50 100 150 200 250 300 normalise differences
Average RSL (t) in the RSL.
16 Mining for efficiency Electric Rope Shovels Figure 11: Worldwide Rope Shovel Annual Unit Production (t/CuM of Capacity) 2004-2013 by Performance The performance of rope shovels in this paper is based on annual 1,100,000 output in tonnes (normalised for full year operation) per cubic metre 1,000,000 of bucket/dipper capacity. There is divergence between reporting of shovel performance in coal mines and hard rock 900,000 mines. Coal mines generally report in volume (bank cubic metres or bank cubic 800,000 yards) while non-coal mines generally report in weight (tonnes or tons). In 700,000 this paper performance of electric rope shovels has been presented in tonnes to 600,000 allow consistency between all loaders t / CuM of Dipper Capacit y putting their load in trucks . Further to 500,000 this the rating of the shovel is in CuM of bucket capacity. All shovels have a 400,000 rated suspended load but this is not well
understood (and can be difficult to find 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 out for some models). It is felt a more meaningful measure of a unit of input for Median Best Practice a shovel is the dipper (bucket) capacity. There is some inconsistency between Figure 12: Rope Shovel Median Annual Unit Production (t/CuM of dipper capacity) 2004-2013 how a rope shovel bucket capacity is by Location defined and the way excavators and Front End Loader’s (FEL’s) are defined, 600,000 however this does not detract from the message contained 500,000 in the data. As of 30 September 2013, 1,712 large 400,000 electric rope shovels were operating worldwide (Parker Bay Mining). In 2013 they will move around 28.3 300,000 Billion tonnes or 32% of total material movement. 200,000
Figure 11 presents the trends in median t / CuM of Dipper Capacit y and best practice annual output for 100,000 worldwide electric rope shovels from 2004-2013. 0 The 2004 - 2013 trend for median shovel
output is down as it was for draglines. 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 The decline in median shovel output (21%) has been less than the decline Africa Australia North America South America for best practice output (30%). The difference between median and best practice reduced from 92% in 2004 to 64% in 2013. Figure 12 is a plot showing the differences in rope shovel performance amongst Australia, South Africa, North America and South America.
Mining for efficiency 17 There is a range of performance trends Figure 13: Rope Shovel Median Annual Unit Production (t/CuM of Dipper Capacity) 2004-2013 visible between different locations. Coal and Non-Coal There is however, one interesting point. The performance (apart from Africa) 550,000 does appear to be moving towards very similar median performance regardless of the location. North America generally 500,000 achieved the highest annual output with Africa the lowest. Figure 13 is a plot showing the 450,000 differences between the performance of rope shovels used in coal mines and those in non-coal mines. Due to the 400,000 quantity of data it is not possible to
provide a valid breakdown by t / CuM of Dipper Capacit y all commodities. 350,000 Different trends can again be seen in coal and non-coal, particularly prior to 300,000 2010. The performance of electric rope
shovels in coal mines is higher than in 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 non-coal mines. Coal mines achieved their peak in 2005 while the non-coal Coal Non-Coal mines improved to 2012. The final comparison is by make and below the top (compared with 38% line of best fit for loaders which load model. Figure 14 shows the 2012 median for draglines). trucks is presented as a third order performance for each make and model. As with draglines the unit output polynomial however, there are a number increases with increasing machine size. of different lines which could be fitted Most makes and models have again to this data. The reason for choosing the declined over time but the primary This is further demonstrated in the plot in Figure 15 which is Median Output third order polynomial is that as newer message in this plot is the significant larger equipment is introduced it is differences amongst different makes and versus Bucket Capacity. Bigger machines move more than smaller machines usual for performance of these models models. The differences are much larger to be lower for some time. In the case of than for draglines. The most productive even after the results are modified to normalise differences in the capacity electric rope shovels the largest shovels, make and model achieved 496,000 t / have been in the market for some time CuM of Dipper Capacity while the least of the dipper. The increasing efficiency with capacity is more pronounced with and performance is high so the levelling productive achieved 149,000 t / CuM of this plot is not observed. of Dipper Capacity. The lowest is 70% rope shovels than with draglines. The The correlation is reasonable. With an R2 of 0.76. The difference between Figure 14: Rope Shovel Annual Unit Production (t / CuM of Dipper Capacity) 2012 by machines of similar bucket capacity Make and Model can be millions of tonnes per year. That material carries a significant value. 500,000
400,000
300,000
200,000 t / CuM of Dipper Capacity
100,000
0 Make and Model OEM 1 OEM 2 OEM 3
18 Mining for efficiency Figure 15: Rope Shovel 2012 Output Versus Bucket Capacity
25,000,000
20,000,000
R2 = 0.76 15,000,000
Output (t) 10,000,000
5,000,000
0 0510 15 20 25 30 35 40 45 50
Average Bucket Capacity (CuM)
Hydraulic Excavators Figure 16: Worldwide Hydraulic Excavator Annual Unit Production (t/CuM of Capacity) 2002- (Face Shovel and 2013 by Performance
Backhoe) 900,000 As of 30 September 2013, 4,239 hydraulic excavators were 800,000 operating worldwide (Parker Bay Mining). In 2013 they will move around 35.3 Billion tonnes or 40% 700,000 of total material movement. Data pre-2002 is not of sufficient 600,000 quantity and quality to provide a valid comparison. The 2002 - 2013 500,000 performance is shown in Figure 16 and demonstrates significant changes t / CuM of Bucket Capacity over this period. 400,000 The 2002 - 2013 trend for median excavator output rises to 2009 before 300,000 falling significantly leading into 2013; 14% for median performance and 18% 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 for best practice. The difference between median and best practice increased from Median Best Practice 33% in 2002 to 96% in 2009 and has come back slightly to 85% in 2013.
Mining for efficiency 19 Figure 17 is a plot showing the Figure 17: Hydraulic Excavator Median Annual Unit Production (t/CuM of Bucket Capacity) differences amongst median 2002-2013 by Location hydraulic excavator performance in Australia, Africa, Asia, North 300,000 America and South America. There are two distinct trends 250,000 observable. Firstly, Asia, Australia and Africa have been falling since 200,000 2007 / 2008. Secondly, North and South America rose strongly to 2010 / 2011 and have maintained the high 150,000 levels up to 2013. Figure 18 is a plot showing the 100,000
differences between the performance t / CuM of Bucket Capacity
of rope shovels used in coal mines 50,000 and those in non-coal mines.
0 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Africa Australia Asia North America South America
Figure 18: Hydraulic Excavator Median Annual Unit Production (t/CuM of Bucket Capacity) 2002-2013 Coal and Non-Coal
550,000
500,000
450,000
400,000 t / CuM of Dipper Capacity 350,000
300,000 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Coal Non-Coal
20 Mining for efficiency Figure 19: Hydraulic Excavator Annual Unit Production (t / CuM of Bucket Capacity) 2012 by Different trends can again be seen in Make and Model coal and non-coal, particularly prior to 2011. The performance of hydraulic 600,000 excavators in coal mines is higher than in non-coal mines. Coal mines achieved their peak in 2008 while the non-coal 500,000 mines improved to 2012. The final comparison is by make and 400,000 model. Figure 19 shows the 2012 median performance for each make and model.
300,000 The primary message in this plot is again the significant differences amongst different makes and models. The most 200,000 productive make and model achieved
t / CuM of Bucket Capacity 522,000 t / CuM of Bucket Capacity (two makes and models were higher than 100,000 the highest electric rope shovel) while the least productive achieved 66,000 t
0 / CuM of Bucket Capacity. The lowest is Make and Model 87% below the top (compare with 38% OEM 1 OEM 2 OEM 3 OEM 4 OEM 5 for draglines and 70% for rope shovels). A similar characteristic is seen with this data as with draglines and rope shovels. Figure 20: Hydraulic Excavator 2012 Output Versus Bucket Capacity That is, the unit capacity increases with increasing machine size. This is 25,000,000 demonstrated in the plot in Figure 20 which is Output versus Bucket Capacity. Up to around 35 CuM bucket capacity 20,000,000 machines move more even after the results are modified to normalise R2 = 0.87 differences in the capacity of the dipper. 15,000,000 The ultra class excavators in 2012 were not as efficient which sees the plot start to level off. Output (t) 10,000,000 The correlation is good although even with an R2 of 0.87 the 5,000,000 difference between machines of similar bucket capacity can be millions of tonnes per year. That material carries 0 a significant value. 0510 15 20 25 30 35 40 45
Average Bucket Capacity (CuM)
Mining for efficiency 21 Front End Loaders Figure 21: Worldwide Front End Loader Annual Unit Production (t/CuM of Capacity) 2002-2013 by (Wheel Loaders) Performance
As of 30 September 2013, 3,511 600,000 front end loaders were operating worldwide in the mining industry (Parker Bay Mining). In 2013 they 500,000 will move around 10.2 Billion tonnes or 12% of total material movement. Data pre-2002 is not of sufficient 400,000 quantity and quality to provide a valid comparison. The 2002 - 2013 performance is shown in Figure 300,000 21 and demonstrates significant
changes over this period. t / CuM of Bucket Capacity 200,000 The 2002 - 2013 trend for front end loaders is very similar to hydraulic excavators. The median output 100,000 rose from 2002 to 2008 while best
practice rose from 2002 to 2009. 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Both fell significantly into 2013; (23% for median performance Median Best Practice and 19% for best practice). The difference between median and best Figure 22: Front End Loader Median Annual Unit Production (t/CuM of Bucket Capacity) 2002- practice increased from 25% in 2002 2013 by Location to 61% in 2013. 300,000 Figure 22 is a plot showing the differences amongst median front end loader performance in Australia, 250,000 Africa, Asia, North America and
South America. 200,000 There are again two distinct trends
observable. Asia, South America 150,000 and Africa have been falling for a number of years while North America and Australia have trended 100,000
up for most of the time presented. t / CuM of Bucket Capacity
50,000
0 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Africa Australia Asia North America South America
22 Mining for efficiency Figure 23: Front End Loader Median Annual Unit Production (t/CuM of Bucket Capacity) Figure 23 is a plot showing the 2002-2013 Coal and Non-Coal differences between the performance of front end loaders used in coal 400,000 mines and those in non-coal mines. Due to the quantity of data it is not 350,000 possible to provide a valid breakdown by all commodities. 300,000 In the case of front end loaders, non- coal mines are more productive than 250,000 coal mines. This is the opposite of rope
200,000 shovels and hydraulic excavators. Both achieved their peak in 2009. 150,000 The final comparison is by make and model. Figure 24 shows the 2012 median t / CuM of Bucket Capacity 100,000 performance for each make and model.
50,000 There is again a significant difference amongst different makes and models. 0 The most productive make and model achieved 267,000 t / CuM of Bucket 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Capacity while the least productive achieved 69,000 t / CuM of Bucket Coal Non-Coal Capacity. The lowest is 74% below the top (compared with 38% for draglines, Figure 24: Front End Loader Annual Unit Production (t / CuM of Bucket Capacity) 2012 by 70% for rope shovels and 87% for Make and Model hydraulic excavators).
300,000 A similar characteristic is seen with this data as with other loaders. That is, the unit capacity increases with increasing 250,000 machine size up to a point and then levels off. This is seen in the plot in Figure 25 which is Output versus Bucket 200,000 Capacity. Up to around 25 CuM bucket capacity machines move more even after the results are modified to normalise 150,000 differences in the capacity of the bucket.
100,000 t / CuM of Bucket Capacity
50,000
0 Make and Model OEM 1 OEM 2 OEM 3
Mining for efficiency 23 The correlation is good although Figure 25: Front End Loader 2012 Output Versus Bucket Capacity with an R2 of 0.75 the variability being explained by bucket capacity is less than is the case for draglines, 8,000,000 rope shovels and excavators. That is, 7,000,000 the variability amongst makes and
models is higher than other classes of 6,000,000 equipment. By way of example, the R² = 0.75 makes and models with approximately 5,000,000 20 CuM bucket capacity can have between 3.3M tones moved per 4,000,000
annum and 5.6 M tonnes per annum. Output (t) 3,000,000 Mining (Haul) Trucks 2,000,000 As of 30 September 2013, ~38,500 mining trucks were operating 1,000,000 worldwide (Parker Bay Mining). In 2013 they will carry around 73.9 0 Billion tonnes or 80% of total 0510 15 20 25 30 35 40 material movement in conjunction with loading tools. Average Bucket Capacity (CuM) The 2001 - 2013 performance normalised to six kilometre total equivalent horizontal haul distance is shown in Figure 26 and again Figure 26: Mining Truck Annual Unit Production (t/tonne of Nominal Payload) 2001-2013 demonstrates significant changes by Performance over this period. The 2001 - 2013 trend for mining trucks is very similar to other loaders although the peak occurred earlier (except for rope shovels).
The median performance rises from 11,476 tonnes per tonne of nominal truck payload in 2002 to 13,963 tonnes per tonne of nominal truck payload in 2006; a rise of 22% in 4 years. Best practice performance rises more in absolute terms but [[VUULVMUVTPUHSWH`SVHK not as much in percentage terms. Best practice rises from 15,722 tonnes per tonne of nominal truck payload in 2001 to 21,341 tonnes per tonne of nominal truck payload in 2006; a rise of 36% in 5 years. As has happened with loading units, 4LKPHU )LZ[7YHJ[PJL performance has fallen over the last few years. Best practice and median truck performance have fallen 32% and 18% respectively from the peak over the last six years. The difference between median and best practice increased from 33% in 2001 to 82% in 2013.
24 Mining for efficiency Figure 27: Mining Truck Annual Unit Production (t/tonne of Nominal Payload) 2001-2013 Figure 27 is a plot showing the by Location differences amongst median truck performance in Australia, Africa, Asia, North America and South America. The results are again normalised to a six kilometre total equivalent horizontal haul distance. Most mining jurisdictions have seen a decline in truck performance during the last 6-8 years. The declines in North America and Asia, where performance was much higher, have been more pronounced and have brought performance to a point where
[[VUULVMUVTPUHSWH`SVHK the differences between locations is much less. Figure 28 is a plot showing the differences between the performance of mining trucks used in coal mines
and those in non-coal mines. Due to the quantity of data it is not possible to (MYPJH (\Z[YHSPH (ZPH 5VY[O(TLYPJH :V\[O(TLYPJH provide a valid breakdown by all commodities.
Figure 28: Mining Truck Annual Unit Production (t/tonne of Nominal Payload) 2001-2013 In the case of mining trucks, coal Coal and Non-Coal mines are more productive than non-coal mines.
[[VMUVTPUHSWH`SVHK
*VHS 5VU*VHS
Mining for efficiency 25 Figure 29: Mining Truck Annual Unit Production (t / tonne of nominal payload) 2012 The final comparison is by make and by Make and Model model. Figure 29 shows the 2012 median performance for each 16,000 make and model. There are again significant 14,000 differences between different makes and models. The most productive 12,000 make and model achieved 13,485 t / tonne of nominal payload while 10,000 the least productive achieved 6,617 t / tonne of nominal capacity. The 8,000 lowest is 51% below the top.
6,000 The characteristic of unit capacity generally increasing with increasing t / tonne of nominal payloa d 4,000 machine size is not consistent in this plot although when the makes 2,000 and models are plotted together (Figure 30) up to around 200 tonnes 0 nominal payload trucks move more Make and Model even after the results are modified to OEM 1 OEM 2 OEM 3 OEM 4 OEM 5 OEM 6 OEM 7 OEM 8 normalise differences in the capacity of the bucket. The correlation is good with an R2 of Figure 30: Mining Truck 2012 Output Versus Nominal Payload 0.84 . However, again by way of example, the ultra-class makes 6,000,000 and models (nominal payload >300 tonnes) move between 2.9 M 5,000,000 tonnes per annum and 4.9 M tonnes per annum.
4,000,000 R2 = 0.84
3,000,000 Output (BCM) 2,000,000
1,000,000
0 0 50 100 150 200 250 300 350 400
Nominal Truck Payload (t)
26 Mining for efficiency Mining excellence at PwC
Delivering local solutions to global challenges The mining sector is facing a range of competing trends and a rapidly changing global “The positive story for miners is that need to balance shareholder dividend expectations whilst maintaining an investment the long-term growth fundamentals pipeline in the midst of increasing operating costs. Safety, environmental and remain intact. But, mining companies community principles also continue to shape the industry as miners look to achieve are facing significant downward their licence to operate and deliver on corporate responsibilities. pressure. As an industry, we need to Mining Excellence at PwC has been designed to mobilise and leverage PwC’s collective fully address the confidence crisis, global knowledge and connections to deliver an exceptional and tailored client before we are able to move on the experience, helping our clients navigate the complex industry landscape and meet next phase of the cycle.” their growth aspirations. Our team of specialists is exclusively focused on the sector John Gravelle, PwC Global Mining Leader and brings an industry-based approach to deliver value for you and your organisation.
Mining Excellence at PwC provides our clients: l ead ing edge connections to our vast the delivery of an knowledge and insight network of mining experts experience that meets our and global client portfolio the research behind our mining We have the widest network of industry With mining experts working around publications and a comprehensive experts who work out of strategic Australia our award winning industry learning and development mining hubs across the globe to help teams are helping clients deliver on program, our professionals can share better connect you to vital mining both industry and technical insight markets. productivity and growth aspirations. with our clients, such as: Our connections provide: We offer operational consulting, • A library of industry publications designed deals, tax and audit services to • seamless client service delivered with to help challenge “conventional” thinking global corporations and locally listed collaborative cross-border account and delve into topical industry issues. This companies. includes: management Mining Excellence at PwC complements – • maximised deal potential through a well- this with: connected global community of mining Mine and Mining Deals • a suite of niche mining consulting leaders – The Insight Series capabilities focused on optimising issues most important to miners • a well-connected and mobile workforce to value across mining operations and ensure effective service delivery in even the effectively managing risk to help our
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Review of global trends in the mining industry
www.pwc.com/ca/mining to ensure we are always improving and delivering on individual client needs.
• An extensive industry development program for our people and clients. Global Mining Leader Ken Su Beijing John Gravelle Toronto This features our annual university-style courses: Frank Rittner Moscow – Hard Hat: The Mining Experience Kameswara Rao Hyderabad (Australia) – Americas School of Mines Jason Burkitt London Wim Blom (North America) Stephen Loadsman Brisbane
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Mining for efficiency 27 Key contacts
Author Contributing author
Graham Lumley Matt McKee Director – Mining Intelligence & Benchmarking Director – Consulting +61 (7) 3257 5135 +61 (7) 3257 8168 [email protected] [email protected]
In 1999 Graham started GBI Mining and commenced Matt works as a consultant, primarily in the energy building the GBI open cut mining equipment and mining industries, and has deep experience in performance database. In September 2013 PwC strategy and operations improvement with a focus on completed the acquisition of the database and productivity. associated IP from GBI. Graham is now PwC’s Director – Mining Intelligence and Benchmarking.
National Perth Sydney Jock O’Callaghan Justin Eve Brett Entwistle Energy, Utilities & Mining Leader Partner Partner +61 (3) 8603 6137 +61 (8) 9238 3554 +61 (2) 8266 4516 [email protected] [email protected] [email protected]
Brisbane Melbourne Adelaide Stephen Loadsman John O’Donoghue Andrew Forman Partner Partner Partner +61 (7) 3257 8304 +61 (3) 8603 3067 + 61 (8) 8218 7401 [email protected] [email protected] [email protected] Franz Wentzel Director +61 (7) 3257 8683 [email protected]
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