GBIData.com Mining Truck Full Sample Report

GBIData.com Mining Truck Full Sample Report

Truck and Loader Optimisation

I have spent the last couple of blogs discussing issues relating to optimising truck and loader sizing and how common it is to find poor matches.  I find it incredible how you can go to two different mining companies and you get two completely different approaches to optimising output from their truck and loader fleets.  Some mining companies believe you undertruck to optimise cost and others believe you should overtruck to optimise output.  Both approaches are right but I am sure most companies don’t understand the link between strategy and actions on the ground.

Variation in performance can be a major contributor to reduction in efficiency and it is this variation which must be understood and controlled if the operation is to achieve their strategic goals. In statistics, a result within three standard deviations of the average is considered to be under control. Given a normal distribution of results, 0.14% of cycles should be expected to be more than 3 standard deviations above the average.  Under normal circumstances the key “interaction parameters” – Wait on Trucks and/or Wait on Loaders, will not be normally distributed and should be skewed strongly to the right or “positively skewed”. (Skew or skewness is the lack of symmetry in a frequency distribution. Positive skew has a long tail to the right of the peak – high percentage of results with a low result.) Most mines have both wait on truck and wait on loader very strongly and significantly skewed to the right. In truck and loader operations most wait on truck and wait on loader results, up to 5% of cycles can be more than three standard deviations above the average.  However, to meet one of the two key strategies our mines follow most of the time you actually only want one of these strongly skewed.

A high skew (maximum frequency of low values with a long tail to the right) on both parameters is required for optimising efficiency but this will deliver neither maximum output nor minimum cost. The higher the measure of skew the more efficient the operation.  It is not unusual for the value of skewness statistic divided by standard error of skew to be over 100.  A significant difference in skewness statistic / standard error of skew for wait on trucks cf wait on loader is an indicator of overtrucking (skew of wait on truck is stronger than wait on loader) or undertrucking (skew of wait on loader is stronger than wait on truck) and one of these is what is required for most mines.

Another way of measuring the efficiency of truck and loader usage is the proportion of time where the truck and loader wait for less than 30 seconds (excluding spotting).  To optimise the mine’s execution of strategy it is often necessary to have these two measures significantly different.  For example you might find that wait on trucks is less than 30 seconds 90% of the time and wait on loader is less than 30 seconds 35% of the time.  This demonstrates a strongly over-trucked scenario.  This will deliver high system output but will not be the most cost effective way to operate the fleet.  However, if it is your mine’s strategy to optimise output at any cost then being overtrucked is a good thing.  These results can be reported on a month by month basis to demonstrate the strength of the overtrucking (maximum wait on truck events less than 30 secs and minimum wait on loader events less than 30 secs).

As already discussed most operations usually follow one of two strategies in relation to matching number of trucks to the loading unit. These operations either follow an over trucked or an under trucked approach.  Under trucking is a lower cost option, delivering a higher utilisation on the trucks while sacrificing the loading units utilisation. Over trucking will cause a higher cost per tonne, will have a higher utilisation on the loading units and a lower utilisation on the truck fleet although moving more material.  Sometimes an approach will be taken to attempt to optimise output and cost but this usually ends in underperformance in profit and/or output.

The challenge for all mines is how best to represent this match of numbers of trucks reporting to each loading unit in a way which makes the outcome meaningful for ongoing optimisation.  The optimal matching of trucks is the critical element for a loading unit to achieve its required production rate and it is essential that the supply of trucks to the loading unit is sufficient to meet the required output.  In most cases the average haul distance varies from the beginning of a new bench to the end of the bench and truck numbers need to taken into account as well as managing the dump areas (long and short dumps).

Wait on truck delays (loading unit entered delays) that are less then 2 minutes in duration are generally considered to be part of normal operational delays.  Wait on truck is typically where there are no trucks available to be loaded by the loading unit and highlights one or more of the following problems in the circuit:

·         The loading unit is under trucked.

·         The trucks are being delayed in the circuit by one or more of the following:

·         Delays on the dump, waiting for dozer work or queuing.

·         Haul road grades too steep, poor road conditions, grading of roads etc. slowing trucks down.

·         Sub optimal Operator performance / speed / technique

·         Dust / weather / blasting etc.

If the trucks are queued, waiting to load, this is called wait on loader.  Most mines that have relatively high wait on loader time (queue time) are over trucked.

Some mines operate day-to-day using a Match Factor, i.e. where a MF of 1 means that the number of trucks are perfectly matched to the digger such that the cycle times are integrated and should no delays occur, trucks will arrive and depart in a perfect scenario exactly matching the digger’s truck requirement.  The method of calculating MF varies but the formula used by GBI is
MF = (1-%wait on loader time)*(1-%wait on truck time)

This is only of value where a mine is passing through this phase of balancing output and cost.  Fleets operating a strategy of optimising the balance between loaders and trucks should achieve an MF >=0.70.  This allows for the typical mining delays and means that as a result of them, some time the digger waits for trucks, and other times trucks are queued at the same digger (normal every day mining).  For most mines the important factor is a comparison of either wait on truck or wait on loader.  This will give them a direct indication of how well they are meeting their strategy.

Truck and Loader Matching Part 6

This blog I want to present a case study where a mine had a large shovel with 44 CuM dipper loading 218 tonne trucks perfectly in two and a half passes!  (Situation normal for most!) The dilemma, faced by multitudes of mines around the world, is do you put a third small pass in the truck or do you send it away 80% full?

The average payload of the shovel was 85 tonnes.  The original methodology for determining the match was not known but the performance of the dipper was quite good when looking around the industry.  It appears likely that the original aim was to fill the 218 tonne trucks in three passes.  Two passes sent trucks away with an average of 170 tonnes payload.  The decision was made not to put the third pass into the trucks due to the loss in productivity, damage caused to trucks by overloading and the increased spillage. 

The desired average payload was 218 tonnes per truck (109 tonnes per dipper).  The mine had a quote from the OEM to change the boom geometry of the two shovels and provide two new dippers. Quote was for $6M+.

Using a combination of data analysis and physical modelling four stages of work were undertaken with the following outcomes;

Stage 1         Analyse data. Process changes recommended.  Discussions held with operators.

Result - Payload increased to 95 tonnes on average which was in line with best practice dipper performance.

Stage 2         Physical modeling of the existing dipper, the supplier’s recommended dipper and two boom geometries.

Result – Modelling proved accurate.  Modelling demonstrated under-performance of supplier’s recommended dipper relative to existing dipper.  Recommendation made not to change boom geometry.  Recommendation not to purchase new dipper due to substantial under-performance.  Recommendation to test changes to existing dipper.

Stage 3         Physical modeling of changes to the dipper.

Result – A number of changes had a positive impact on payload but none gave enough by themselves to increase payload to 109 tonnes. Recommendation to conduct further testing combining various options to modify the dipper.

Stage 4         Four options were presented which met the target 109 tonne average

payload, (Figure 1).

The mine chose the preferred option with a slight change, engaged a structural engineer to design the modifications and a local business undertook the modifications to one dipper (Figure 2).

End Result    All up cost $350,000, Average Payload 111 tonnes. Value to mine at the time $8M per annum.

Consequently a second dipper was modified for the second shovel. 

All up cost was $470,000 with two dippers achieving 111 tonnes and 109 tonnes average payload. Cash saved on the project >$5.5M.  Value to the mine $15M per annum.

The most important lesson here is that you can’t achieve anything if you won’t have a go.  The four stages here took 18 months and were rigorously evaluated before proceeding, but the key is that they did it and they added real value.

Truck and Loader Matching Part 5

This blog continues to investigate the issue of why many trucks are being perfectly loaded in 2.5 or 3.5 passes.  In this discussion I am looking at rope shovel capacity and why we need so much steel to carry what is often a very poor payload.

How is it possible that best practice in dipper performance provides a payload of 2.16 times capacity but the dominant manufacturers provide dippers which only achieve around 1.70 times capacity?  This is more than 20% less payload for the same capacity and around the same weight of steel.  This rhetorical question actually has a real answer.  It is because the mines don’t care.  So long as it keeps going and is supported when it breaks then that is OK.  Many mines don’t even complain when the loader truck match is 2.5 or 3.5.  To someone who has worked in equipment productivity for over 20 years this is really depressing.

 

Looking at some issues which impact shovel payload.  Firstly, dipper issues which the mine can have some impact on.  The tooth attack angle is really important. Payload increases by around 0.5% per degree as the tooth attack angle is increased.  However, it is not possible to simply keep steepening the tooth attack angle of the dipper due to the interaction between the heel and the bank.  Relative heel wear rises exponentially after about 65 degrees tooth attack angle.  By 70 degrees the heel wear is probably unacceptably high.  Many buckets are in the range 50-55^o and are losing a lot of payload.

The concept of Bail vs Bail-less is a function of where the hoist connection is made to the dipper. The connection of hoist ropes at the rear of the dipper increases payload.  Where the connection is 25% along the dipper the difference is -10% which is significant. 

The width : height : depth ratios as well as teeth arrangements have an impact on payload but there is little impact site people can have on these issues once you have the dipper so I won’t expand on these issues here.

The other side of the payload issue is operational issues.  Many of these can be controlled by the mine.  What is being dug causes variation in average payload by up to 20% in the same dipper. Herein lies a significant issue relating to truck/shovel matches.  It is possible that the same dipper, even on the same minesite, can get differences in payload of 20% simply due to the spoil being dug.  The key to higher payload is the degree of fragmentation.  The highest payloads are achieved in spoil where there is a range of particle sizes; not all large and not all small.  The implication is that payload is significantly enhanced by good blasting practices.

The power made available to the operator has a major impact on payload.  In harder digging, ie. blocky, poorly shot, etc., increased power provides increased payload up to 120% of the standard power level.  In softer spoils the shovel dipper achieves higher payloads at lower power levels.  In summary, it is beneficial (in terms of payload) to increase power to the maximum.

Bench height plays a major role in determining payload.  At any bench height greater than 30% of boom point height a full payload can be achieved consistently.  Similarly, the distance from the face has a major impact on payload.  The variation from cycle to cycle is quite large but a consistent trend is seen for each digging position.  The first few digs have the loading unit very close to the face.  During these cycles the payloads are reduced possibly due to the inefficient application of power to the trajectory of the dipper / bucket.  The payload increases as the face “moves” away from the shovel.  Once the dipper starts having trouble reaching the face the payload reduces quite quickly.   The decision about when to move the loader is not an easy one to get right.  Generally the operator will decide to move the loader when they encounter difficulty in loading the truck in the designated number of cycles.  To optimise the productivity a range of factors need to be considered, including, payload, fill time, another truck waiting, what the face is like.  As a general observation, if the loader is under-trucked, it would appear prudent to move the loading unit frequently.  If the shovel is over-trucked it becomes a multi-dimensional equation as to when the most efficient time to move is.

                                                      

It became evident from a very early stage in the work on shovels that on some loading equipment the efficiency of the bucket / dipper was severely compromised by large voids inside the dipper / bucket (Figure 1).  These voids ranged from 5% inside a backhoe bucket up to 25% inside rope shovel buckets.  The impact of these voids is included in the previously described impacts on payload.

Finally I would direct your attention to Figure 2.  This shows the variation in dipper payload for P&H and Cat (previously Bucyrus), (both unidentified) and VR Mining Dippers.  I have spent my career helping mines be more productive and the VR Mining dipper is the most efficient dipper design I am aware of.  I am aware there are maintenance, support and financial issues to purchasing a dipper but speak to dipper manufacturers, not just the OEM, the next time you want a dipper.

Just so you know: I worked for VR Mining in 1997 and 1998; before they designed this dipper.  GBI has had a number of small consulting jobs from VR Mining over the last 10 years.  I had no input into the VR design.  Neither I nor GBI receive anything from anyone for the comments made here.  They are simply my honest opinion – the VR dipper is the best and the mines are costing themselves a bundle by not looking at it.  Even if the mines used this fact to put pressure on P&H and Caterpillar to do better, the industry would benefit.

Truck and Loader Matching Part 4

Over the last few weeks I have systematically pulled apart the issue of nominal truck capacities to demonstrate why big mining trucks achieve 5-15% below what the manufacturer says they should get on average.  I don’t believe this is an issue that too many truck manufacturers’ want to address and the cynical side of me suggests that this article won’t help.  Maybe a single voice in the wilderness can gain support to force change. 

My focus is on mines moving more for less and apart from the engineering design work to increase the capacity of trucks from the 150 tonne maximum size 25 years ago to the 360 tonne maximum size now I don’t think that the truck suppliers have helped the “move more for less” equation too much.  Even the notion of bigger trucks being a great innovation and assistance in efficiency enhancement is questionable.  I will repeat something from a previous blog.  On the whole bigger trucks are less efficient than smaller trucks.  They carry less payload (as a percentage of nominal capacity) and work less hours. However, this is not a consistent picture between OEM’s.  In terms of nominal capacity the 360 ton trucks are 50% bigger than a 240 ton truck. however, in terms of actual annual capacity, average 360 ton trucks move just 20% more than 240 ton trucks.  I am not pointing the finger at one supplier. 

Figure 1 shows the 2010 median performance for each major mining truck make and model.  Some of the older and newer models are not included due to lack of data.  Mining truck performance is presented in this analysis as annual tonnes (normalised for full year operation) * km travelled per tonne of nominal tray carrying capacity.

Trucks with different designations (usually A, B, etc used by Cat and Liebherr) have not been separated in this analysis.  The capacities for these “sub-models” are generally similar as is the output.   It is important to note that this plot does not attempt to say whether the make and model results actually reflect better trucks or the operating characteristics of the sites at which they are used.  The trends with increasing size of mining trucks are mixed.  The Liebherr trucks become more efficient with increasing size while the Cat trucks become less efficient with increasing size.  The Hitachi, Komatsu and Terex trucks achieve peak efficiency with the 240 ton (218 metric tonne) capacity size EH4500, 830E and 4400 respectively.  The larger capacity trucks are not as efficient with these OEM’s.  Of the larger trucks the Liebherr T282 is the highest performer with Terex and Komatsu both achieving 20% less annual tkm/t and Cat 23% less annual tkm/t.  It is not without precedent for larger equipment to have lower unit production (ie. draglines) however, the exceptional performance of the Liebherr T282 range demonstrates that this is not a necessary outcome.  Another clear finding from this plot is that the performance of the smaller Cat trucks (777 and 785) was, and continues to be, relatively high.  They however, are not suitable for loading with the larger loaders. 

This industry has lived in a world where bigger is better.  But frequently when bigger equipment is released it just doesn’t perform well.  Those of us who remember the release of 240 ton trucks would remember that they had real problems.  It seems too easy for a poorly performing mine to just get bigger equipment and that is what they tend to do.  They waste more millions of dollars when the improvements they need are available by just operating more efficiently and would actually cost very little.

To demonstrate this point I will set up a scenario of a PC8000 hydraulic shovel loading Cat793 trucks.  These have not been chosen for any particular reason except it should be a comfortable three pass match.  The average PC8000 loader will require 7.5 average Cat 793 trucks.  Four crews plus spares plus trainees (you should always have a pool of people training) probably means around 40 truck drivers.  If a mine then goes and purchases Cat797 trucks the typical method of determining number of trucks is to simply work out the proportional capacity.  New trucks = old trucks * 793 capacity / 797 capacity.  Using this formula five new Cat797 trucks would be purchased with the expectation that around 13 people would be saved along with reduced running and maintenance costs.  Unfortunately, this scenario is fictitious.  In the real world the PC8000 on average needs 5.8 * 797 trucks and only saves 9 people.  Bigger trucks cost more to buy and more to run, so how far ahead are you?

OK so returning to the real point of this column; technology is progressing fast.  We now know that trucks are not carrying the nominal payloads.  This has not gone unnoticed by companies which make their way in the world by making equipment work better.  For the OEM the real money seems to be in the chassis and tyres.  Improvements in payload are coming from specialist tray suppliers.  Truck trays are no different to most other mining equipment.  What the equipment carries is made up of steel and payload and the aim is to maximise the payload and minimise the steel while achieving acceptable life.  In the past with trucks this was a nothing equation because OEM’s told the mine what payload the truck would carry.  We now know this was almost always wrong.  Truck trays seem to be following where the industry has been with draglines.  Now Bucyrus and P&H build draglines and shovels but CQMS currently build the most efficient dragline buckets while VR Mining have the most efficient shovel dippers.  In trucks you have specialised truck tray manufacturers like DT HiLoad, Duratray, Esco, Philippi-Hagenbach, Westech, etc. who seem to get it; the chassis is built to carry a certain load and if you can reduce tonnes of steel and increase tonnes of payload then the mine must be ahead. 

It is my proposal that we must here and now dispose of SAE Standard J-1363 for calculating truck capacity the same way suppliers have disposed of the CIMA formula for dragline bucket capacity.  We must also stop rating trucks based on a nominal payload.  We should establish a rated capacity for the truck trays which is struck capacity (contained capacity with no heaping according to computer models) multiplied by a factor.  With dragline buckets the factor is 0.9 which I have always disagreed with but everyone knows it and accepts it.  I believe the rated capacity of a truck tray should be equal to the struck capacity, (factor = 1).  In the same way that we have a Bucket Efficiency Ratio for draglines and a Dipper Efficiency Ratio for shovels, which is payload / rated capacity, we need a Tray Efficiency Ratio (payload / rated capacity) for trucks - TER.  There is also a steel weight ratio (Tray Unit Weight (TUW)), which is the weight of the tray divided by the rated capacity.  The formula for the optimum truck tray rated capacity is then;

OTC    =        GVM – Chassis Wt

                      TER + TUW

Only then can we get the best tray design with the right capacity to meet the gross vehicle mass.  At least then we will be covering Step 1 in the optimisation process; mines will be selecting the right piece of gear.

Truck and Loader Matching Part 3

Why do mines end up with trucks which are not able to carry their nominated payload?  What is the problem with truck capacity?  SAE Standard J-1363 is still used by most suppliers of truck bodies to define the capacity.  However, with the advent of larger and larger trucks (and loaders) more sophistication is demanded of the truck tray capacity.  Many mines simply don’t (and most can’t) achieve the truck’s nominal capacity on average without the addition of a door on the rear and/or hungry boards.  A calculation of the geometry shows that the field volume can be 5-15% below the SAE rated volume.  The main error in SAE Standard J-1363 is that the capacity requires a 2:1 heap from all sides and 1:1 slope off the rear to the point where it intersects the top of the body sides.  The problems with this are;

1.    There are virtually no materials which will stack at 1:1.

2.    To put the 2:1 heap on top of the 1:1 at the rear is wrong. Some manufacturers will take the spoil off the back at 2:1.

3.    Spoil when dumped will form a cone.  Therefore the angular top of the truck body cannot be filled completely.

These three points are demonstrated in the accompanying figure 1 which is from Hagenbuch (2000).

Figure 1

4.    The angle of repose is almost never 2:1 (26.6^o).  The problem is magnified the larger the angle is.  Interestingly enough dragline engineers are taught that the angle of repose is 37^o.  In reality it is rarely that high.  Most angles of repose are between 30 and 35^o.

5.    The angle towards the front is almost always shallower than the angle at the rear and the angles on the sides.  The difference between front and rear is up to 7^o. The difference on the sides is not consistent and has been measured from -7^o to +6^o compared with the rear angle, (Hagenbuch 2000).

The final difficulty then is the determination of density of material in the truck.  This is again broken into three confounding variables;

1. Different material has different density.
2. Different materials will have different swells upon loading, which will often be different to that in the dipper or bucket, and
3. The operators loading technique may alter the density in the truck.

As a further confounding issue, the operators’ placement of spoil in the truck may reduce the effective capacity due to loading on the axles.  This is not covered in this blog but is very important in the optimisation process.

When the five issues are considered the actual volume can be 5-15% below the SAE J-1363 Standard.  Now I do need to say that there are a number of truck tray manufacturers in the market who are doing this much smarter than others and are providing a more accurate calculation of nominal capacity.  However, if truck supplier X says they will carry 291 tonnes in their tray and truck tray manufacturer YY says that theirs will carry 285, guess which one most choose?  The problem is that the standard tray which comes with most 291 tonne (320 ton) trucks may only carry 270 tonnes.  Maybe the truck tray manufacturer YY can carry 285 tonnes but most of the time they won’t?  Some do consistently carry what they say they will, however, most truck suppliers know that their trays won’t carry the nominal payload.  The problem here is that unless you model it you simply don’t know.

In the second figure I have put a sample of truck makes and models and the payload they carry in “best practice” operations.  The trendline of average is also provided.  This clearly shows the reducing payload as a fraction of nominal load as capacity increases.  What this means is that you can’t expect to achieve the nominal payload for any truck over a Cat785 size.  You might get it but more than likely you won’t.  For a truck in the 327 tonne (360 ton) size, you might get 20-30 (or more) tonnes lower payload than you expect for the 20,000+ times the truck is filled per annum.  For a fleet of 8 trucks (and you might need more as I will discuss in the next few weeks), this is 4M tonnes of payload lost per annum.  How is your mine plan looking?  Scary thought.

 Figure 2

Reference

Hagenbuch, L.G. 2000, Adapting the Off-Highway Truck Body Volumetric Process to Real World Conditions, SAE Technical Paper Series No. 2000-01-2652, International Off-Highway & Powerplant Congress & Exposition  Milwaukee, Wisconsin September 11-13, 2000

GBI presents a snapshot of our "Understanding and Improving Truck & Loader Operations" Course

After numerous requests we have put together a snapshot of your "Understanding and Improving Truck & Loader Operations Course" to give you a taster of this 2 day course.

Please contact lea.andlovec@gbimining.com if you have any questions or would like to book into this course.

Mining and complexity – paradigm, paradox or parody?

I introduced the issue of complexity in my last blog and stated that there is little evidence in open cut equipment production data that “complexity” plays any role in decreasing equipment productivity over time.  This is a controversial view in the mining industry, particularly the large mining companies where increasing complexity has been used as an excuse for falling equipment productivity rates for some years now.

I stated in my last blog;

It is my theory that the corporatisation of the mine site is to blame for the reduction in availability and consequent productivity.  It is the focus on process and not the result.  Managers are often judged on how they do their job, not the end result, and a declining result can be hidden behind exceptional processes.  Part of that change is an increasing focus on safety but not the majority of it.  Because most managers have little real natural management expertise they embrace the processes which are encouraged by corporatisation.  Six Sigma or Lean are great because they provide the manager with a focus on process.  You can actually point to what you have done.  Unfortunately the performance metric is wrong.

I believe that the silent majority support this view but many just have to fit within the confines of the company that employs them.  I received the following from someone running a mine this week after they read my last blog (that makes two of us who read it).

You are so right about people getting hung up about the process of a process and the process of process improvement rather than the bottom line impact of the outcome it produces

You can extend this further by explicitly focussing on added value as the principle and proper measure of improvement. eg "For any given operational outcome, a process 'improvement' that does not measurably generate positive added value or improve safety without negative impact on the firm's overall value is no improvement at all." No matter how exceptional it might be.

This industry needs to take more notice of Prof Michael Porter - the father of the value chain concept. He had it spot on. If it doesn't add measurable value, prune it.  However, remember not all value is financial - reputation, employee wellbeing, and other "soft" forms of value also matter to different degrees in different companies.

Six Sigma and Lean do not cover the value chain concept well I reckon, and their experts too frequently have no wider business management training to know any better.

A few other personal operational observations for you;

• Pits do get more complex sometimes but usually just deeper and/or less "rich". Any complexity is mostly human induced.
• You are right about availability being a function of age. BUT its more complicated and its only true beyond a certain age. There is a trade-off between depreciation of new equipment with age and repair with age on 2 axes vs availability with age on the third. If you map profit (or net value added) against these axes you will find here is a reasonable sweet spot for average fleet age where profit is maximised - and it’s not at any of the extremes. Operational rosters (eg 4 days a week, 24*7 etc) change the sweet spot quite a bit.
• I've never seen any specific mining industry research on this and there are a lot of misconceptions out there.
• Availability is an issue but not the only one. Cost saving pressures, lack of professional knowledge, managerial ignorance and inappropriate performance metrics are an even bigger part of it. Maybe some would argue this is the actual "complexity" causing most of the problems, eg...
• Payload and digging cycle time (esp truck shovel) are affected (often severely) by poor pit design (relative to deposit and equipment), poor road placement, poor matching of blast performance, poor dump design, but also limited communication between the engineers and mining supervisors - the latter usually make the shift to shift decisions with no knowledge or understanding of the former's work (= poor decisions frequently).

I will repeat my last paragraph from the last column.  Commodity prices (maybe with the exception of gold) are going to decline.  You won’t be able to keep making money without focusing on the real reason you are in business.  You need more of your commodity going out the gate at a lower cost, not a new business improvement process every week or month. 

  

Graham Lumley 

BE(Min)Hons, MBA, DBA, FAUSIMM(CP), MMICA, MAICD, RPEQ

Truck and Loader Dictionary First Edition Sample

Truck and Loader Dictionary First Edition Sample

Cost and Mine Planning

In the area of cost prediction and financial analysis the same issues with OEM and third party provision of information exist as in production information.  The bottom line is financial analyses are regularly not delivering the right answer for appropriate decision making.  In most cases production estimates are higher than what is achieved and cost estimates are lower.  I previously asked the question, is it a human trait to be optimistic or is it pressure to produce results which are good enough to gain shareholder or Executive Management approval?  I suspect it is a combination of both.  The issue I identified last blog about the continual challenge to turn mineral deposits into a financial return is not easy.  If the mine plan says it is not economic then shareholder money is wasted and employees don’t have a job.  We in the mining industry live in hope that something will change.  About every 25 years they do (and it lasts for 6-8 years) but there is an unmistakable longer term downward trend in commodity prices.

Through the last resource downturn (1985 – 2002) we saw mines start with fanfare and substantial capital spent.  Eventually owners lose patience and look for a buyer / partner.  One from overseas who has no specific knowledge of the industry is always good because you can make ambitious predictions on future prices with no real basis for an expectation that they might be accurate.  The classic example of this was when Agipcoal purchased 25% of the MIM NCA coal mines in the late 1980’s.  Here you had two mines (and a port) which were running at an operating loss less than ten years after MIM had spent hundreds of millions of dollars building a mine and upgrading another.  The financial predictions of future costs and income were simply never achieved and Agipcoal did not remain as a long term owner.  What happens is that assets change hands at lower and lower prices until someone can make money or the mine is closed or we simply wait long enough for the prices to turn.

So who is responsible for the cost (and income) predictions.  Each of the major mining consultants will tell you they have the cost data for all the major equipment.  But my question is where does it come from as it often bears no relationship to reality.  Those that do bear some relationship to reality - well who actually owns the data?  There is a bigger problem here.  Cost allocation, reporting and control is done very badly by a large number of the mines around the world.  While the quality of production monitor output is reasonably consistent and is getting better I am aghast at the quality of financial control.  When a cost benchmark is done it takes 2-3 weeks on site to access the data and put it into a format which is firstly credible and secondly can be compared with others.  This makes the quality of financial analysis on a mine very dubious because very few people have the time to get the data into an appropriate form.  In the majority of cases the cost of an individual piece of equipment is (much??) higher than what the mine thinks it is.  So the situation evolves whereby people on the mine have a very poor idea of cost and they seek confirmation from others of costs.  Unfortunately they often turn to mining consultants and suppliers of equipment.  Mines seem to think that just because they do it badly most others must do it well so consultants and suppliers must know equipment operating costs.   Wrong!!!  For starters suppliers have a vested interest in telling you low costs and consultants have a vested interest in making the economics look good to continue to further studies.  Both groups readily use low cost data.

Apart from the poor financial control demonstrated by most mines, the following are actual reasons why overly ambitious (low) costs have been used (some are mine and some have been provided by Rob Beckman of Red Button group);

• The data that is used can be many years old and does not include appropriate escalations,
• The costs can simply be wrongly estimated, taken from a small sample of cost that is not the long term average,
• The cost is often gained from contract prices that are only a subset of total cost of the assets,
• There is no consideration of duty cycle which as a dominant factor in the cost of the equipment (eg. Steep grades, ripping for dozers, double benching for excavators etc etc)
• Finally, the costs are a $/hr average in most cases which do not take into account the lifecycle variation of equipment cost.  The cost of a single piece of equipment will vary by 50% from year to year depending on the work that is done and it can be shown that even over very large fleets this does not average out year to year. 

In my next blog I will provide some examples of costs which were provided by a number of mine planning consultants and were just simply wrong.

Graham Lumley 

BE(Min)Hons, MBA, DBA, FAUSIMM(CP), MMICA, MAICD, RPEQ

White Paper - Mine Planners Lie with Numbers

White Paper - Mine Planners Lie With Numbers

White Paper - Trends in Performance of Open Cut Mining Equipment

GBI is excited to announce the release of Graham Lumley's White paper on Performance Trends of Open Cut Mining Equipment. 


Using our extensive (and rapidly expanding database), Graham has been able to glean some interesting and sometimes disturbing trends across the various makes and models of machines in the open cut mining space.


Take a look at the White paper here.


If you would like to discuss the findings of this white paper in further detail with Graham or perhaps understand how you can use the information held by GBI to further your productivity improvement please contact us at GBI (gbi@gbimining.com) or Graham directly (graham.lumley@gbimining.com).

Productivity and Mine Planning - Part 3

Mining companies don’t have the equivalent of the magic pudding (with apologies to Norman Lindsay for the analogy).  They have limited resources with which to create a return for their shareholders and as they are mined they deplete.  For all mining companies there is continual pressure to turn what is in the ground into a financial return.  This is one side of the issue which sees productivity rates and costs used in mine plans almost always optimistic.  I suspect the old saying, “Don’t let the truth get in the way of a good mine”, or something like that, is pretty apt.  The other side of this problem is that despite what most mine planners (consultant or company) say they don’t have enough data to provide (statistically) credible inputs.  The decision-making process by executive management and many Boards of Directors is at best doubtful, usually flawed, and in some cases, just downright dishonest.

This week I will use an example of a job we did for a mine planning consultant as a demonstration of how the mine plan goes seriously pear shaped. I should emphasise that in this case the consultant is using real inputs; they do understand the issues; and will be using the information correctly.  Shame they are in the minority!!!

The request was for benchmark information for an RH 340 hydraulic excavator with 34 CuM bucket capacity.  The first point to note is that in the particular application being looked at, the worldwide, average annual output for these machines was 12.6 million tonnes while best practice (average of the top 10%) was 23.1 mt.  Just a small difference there.  Can you believe a best practice RH340 moves twice as much as the average?  The natural tendency for the mine is to think, “of course we are good” and for the consultant to want to provide the best outcome.  More often than not a rate somewhere in the vicinity of, or above 75th percentile is used.  However, you have to be realistic.  Only one in four mines using the RH340 will achieve 23 mt or higher and maybe you are one of the 3 out of 4 who won’t.  If you have always had average performance then why would it suddenly improve?

The second issue is why do some people believe that a piece of equipment will move well over best practice?  This example provides the perfect demonstration.  The request from the mine planning consultant was for a benchmark of availability, utilisation and dig rate.  That is, they wanted 25th percentile, median, 75th percentile and best practice of these three KPI’s.  The availability, utilisation and dig rate combine to produce the annual output.  The problem is that there is no mine in the world using this loader where they achieve best practice availability, best practice utilisation and best practice dig rate.  In fact if you take best practice for these three KPI’s the output is in excess of 27 mt compared with the actual best practice output of 23 mt.

A number of human factors are at play here.  Firstly, different companies have different definitions of the KPI’s.  Availability for one company is not availability for another company.  So for mine X to say they achieve 90% availability and that makes them good is wrong.  Worse still is the executive who just simply applies numbers without understanding what they mean or what is included in them.  Secondly, people use results achieved for short time frames and apply them to longer timeframes.  Availability or utilisation achieved over one to three good months normally bears no semblance to what is achieved over 12 months.  A third problem is people extrapolate rates in a straight line up from smaller equipment and this is often not correct.  There are a range of factors at play as sizes get bigger.  For example, a best practice 218 tonne truck will carry 208 tonnes (95.4%) while a 327 tonne truck will carry 301 tonnes on average (92.0%).  Another example is draglines.  An M8050 with 50 CuM bucket will carry 107.5 tonnes of payload (2.15 t/CuM) on average and an M8750 with 100 CuM bucket will carry 200 tonnes at best (2.00 t/CuM).  Add to this the fact that bigger equipment operates for less hours and you will understand why you can’t just extrapolate up.  A fourth mistake which people make is to apply results from one manufacturer and say that the same equipment from another manufacturer will be the same.  It isn’t.  As an example the difference in actual annual output between different manufacturers’ hydraulic excavators in 2010 with 30-34 CuM buckets was up to 84%.  (Oh by the way, which one did you buy?)

At the end of the day we are interested in what the equipment will move in a defined time.  The defined time will depend on the level of accuracy required of the plan.  If it is a really short term plan (next shift or day) we might use the dig rate, (what is moved per operating hour).  As the time frame goes up more and more operational factors come into play.

I have a real issue with what some mine planners (company and consultants) are doing.  They don’t have sufficient data nor knowledge about performance but tell you they do.  I simply ask that if they have the information then why are mine plans continually wrong? 

OK, some companies don’t want the truth but some do.  The "mine development industry" will continue to get away with  producing poor plans until we as an industry plus shareholders and stock exchanges hold them accountable; now, 3 years, 5 years, etc into the future.

Graham Lumley 

BE(Min)Hons, MBA, DBA, FAUSIMM(CP), MMICA, MAICD, RPEQ