GBIData.com Loading Unit Full Sample Report

GBIData.com Loading Unit Full Sample Report

GBIData.com Mining Truck Full Sample Report

GBIData.com Mining Truck Full Sample Report

Syndicated Project for Rope Shovel Benchmarking in Russia

GBI are formulating a syndicated benchmark specifically for rope shovels in Russia with a focus on the Kartex EKG range. If you have these machines and would like to participate in this benchmark to find out how your equipment is performing compared to Best Practice please contact me for more information on laura.seviour@gbimining.com.

Best Practice Standards Series - Top 10 Dragline Operating Standards Feedback

On 28th August 2012 GBI conducted its Best Practice Standards Series - Top 10 Dragline Operating Standards Course. We wanted to share with you some of our feedback and the course rankings:

"Overall very useful and informative course. Covered a broad range of topics well. Information was presented objectively with supporting data and facts which was very valuable."

"Very good dragline course. Bucket and rigging section was very involved obviously due to the large amount of knowledge GBI has amassed in this area. Well presented and the info was delivered at the right level for the target audience"

"Great workshop overall, very informative on a range of levels."

"Being relatively new to Dragline operations and management, this course has been really beneficial."

The course achieved an overall course ranking of 4.68 out of 5. Where 1 is poor and 5 is excellent.

The Ranking for Content achieved was 4.42  out of 5.

The next Best Practice Standards Series are to be held on the following dates at Colorado School of Mines, Golden, CO 80401:

October 4th - Top 10 Mine Operating Standards
October 5th - Top 10 Dragline Operating Standards
October 8th - Top 10 Mine Operating Standards
October 9th - Top 10 Operating Standards

Contact Karen.Trott@gbimining.com or go to http://www.gbimining.com/GBI-Training.html for more details.

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

Understanding & Improving Truck and Loader Operations course receives great feedback

Our "Understanding & Improving Truck and Loader Operations" course has received great feedback.  See below for breakdown;


To date:

• We have achieved an average of 4.4 out of possible 5 for Total Course Content
• We have achieved an average of 4.6 out of a possible 5 for Course Facilitation

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.

Truck and Loader Matching Part 2

I have seen many examples of trucks being loaded perfectly in two and a half or three and a half passes.  As I said in the last blog, for many mines the issue of matching truck capacity to loader capacity is problematic and more often than not results in a majority of trucks being under-loaded.  As trucks and loading units increase in size the number of passes required to fill the truck is decreasing and the difficulty in attaining the match is becoming more difficult.   

Mines generally use one of five methods for selecting equipment size/capacity.

1.    Allow the supplier to decide.  Suppliers love this because they can sell the mine the same as someone else has received which cuts down their costs significantly.  However, if the mine abrogates their responsibility to run their mine they get what they deserve.  Remember back last year when I discussed the 62.7 CuM rope shovel.  The calculation had fill factors and all sorts of multipliers to arrive at the correct answer.  However, you don’t need to be as cynical as me to be struck by the fact that it was exactly the same dipper being used on exactly the same make and model shovel at a mine about 150km away.  Were they digging the same spoil? No.  Were they using the same bench heights? No.  Surely they were at least loading the same trucks?  No.  A completely different operation and yet (quite by chance?) the supplier came up with the same dipper as being the right size. Mining with a computer is really easy but it rarely provides the answer which will help the mine optimise what they are doing.  Understand this – if you allow the supplier to specify the size of the equipment you will get the capacity which is best for their profit, not yours.  It saves them much design, engineering and fabrication cost if a supplier can simply sell you the same capacity that someone else has.  

   A quick example from the coal mines on suppliers providing the same product when something different was needed.  A mine ordered a dragline bucket from the dominant supplier.  In this case the supplier has about 75% market share and the mine was justified in choosing them.  After doing some computer mining the bucket supplier arrived at 57 CuM capacity.  Once it went to work the mine was very unhappy with its performance as the average payload was about eight tonnes below what they previously achieved and the operators were complaining about it not digging.  We were called in to investigate.  We found the geometry of the bucket was not matched to the geometry of the pit being dug.  I found the exact same bucket had been built for another mine about 9 months earlier and they were very happy with it.  This operation had an average pit depth of 50 metres and the design matched perfectly.  The second 57 CuM bucket was exactly the same as the first but the digging depth rarely exceeded 20 metres.  End result – the mine lost substantial production and potential profitability.  Anyway, back to the other methods of selecting equipment capacity.

2.    Guess.  There are a number of forms which this takes.  Most people in the selection process will create the “truck-loader” matching spreadsheet but will make a number of guesses about key factors on density, fill factors, etc.  Often this process is aimed at justifying a particular capacity to management.

3.    Existing Data.  This is an extension on guessing.  Data is collected on existing performance and this is extrapolated to new equipment.  This is certainly a quantum leap up from options 1 and 2 but can fall down when data is sketchy or non-existent or when different equipment is ordered.

4.    Computer modelling. This is an extension on point 1.  Some suppliers have flow models for simulating material flow into their equipment but while being good for research and development, they are of minimal value for commercial decision-making.  This is due to the models not being far enough advanced to simulate specific spoil (as opposed to generic spoils).  Now I might get howls of opposition from highly intelligent researchers but I have never seen one good enough for commercial decision-making.

5.    Physical Modelling.  In 1977, D.J. Schuring, released “Scale Models in Engineering: Fundamentals and Applications”, Pergamon Press, New York, N. Y.  In this book, he devoted a section to earthmoving in general, (eg. Bulldozers, excavators, etc), in which he confirmed the accuracy of physical modelling in earthmoving applications.  Scale models have been used successfully on dragline buckets and rigging since 1985.  Similar techniques have been applied to rope shovels since 2000, truck bodies since 2002 and excavators since 2005.  Schuring (1977) found that the key to accurate results from scale models in earthmoving was that the behaviour of the spoil was accurately simulated.  

In my next blog I will carry this discussion on and look at the flawed standard being used to determine truck nominal capacity.

Graham Lumley 

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

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

Productivity and Mine Planning - Part 1

The return from many mining operations has been continually undermined by mine plans which either can’t be implemented or when implemented simply don’t provide the expected results.   There are some amazingly smart, technologically-advanced tools available for mine planning but they are being rendered useless by a poor approach to data and knowledge.  Remember from a previous blog; data is your most valuable strategic resource.  Think about that comment for a minute.  A strategic resource is something you can make money out of.

In previous blogs I have discussed the poor standard of data analysis provided by equipment suppliers and why mines must take responsibility for the really important strategic skill – analysing data.  This blog is about mine planning.

Mine planning is a multi-facetted science.  This simply means that despite the technologically advanced tools there are still multiple places where it goes wrong.  The key driver of this is a poor approach to knowledge management.  We have really, really smart tools and they are hungry for knowledge input, however, at best they are malnourished and normally they are comatose through starvation.  How long has your planning engineer been in the role? How does your mine planning process determine equipment rates?  It is now known that the best practice for large mining trucks is 112% higher than average or best practice for excavators is 168% higher than average, or 124% in shovels or 32% with draglines, etc.  Worse still, the average drill delivers only a quarter the annual metres drilled of the best practice drills.  The data is available but most mines don’t use it.  Despite this huge variation many mine plans assume rates which are higher than best practice and simply have no chance of being achieved. As an example the average dragline in Australia underperformed plan by 7% in 2008.  Not bad, but the average shortfall in coal uncovered was 25%.  Clearly there is something wrong with the planning and/or the execution of the plan.  What is the impact on a mine’s bottom line when the price of commodities are relatively low?  Finally, do you have improvement built into your mine planning and do you have a process in place for the operators to deliver it? 

We have demonstrated the performance of P&H4100XPC shovels in the northern part of Australia's Bowen Basin.  Best practice was 17.9MBCM per annum and median 14.1MBCM per annum.  The project team was under pressure from Executive Management to budget 25 MBCM per annum because in their opinion, “That is what that model is capable of.”  The GBI database indicates the P&H4100XPC shovel is capable of moving 25MBCM per annum, however, only one machine in 40 from around the world will achieve this level and none from the northern Bowen Basin.  In this case the use of 25MBCM in development models would make a huge difference in terms of predicted ROI and approvals for financing but is most likely going to end in the company not meeting their forecasts for the proposed development. 

Now a “competent person” will sign off on this and the deposit will be presented as economic, open cut reserves.  Financiers and shareholders will feel comfortable (they are after all one of the largest mining companies in the world) but industry standards suggest they haven’t demonstrated  economically mineable, open cut reserves.  Maybe they are economically mineable, underground reserves, but they haven’t been demonstrated as economically mineable open cut reserves because the inputs into defining them are extremely doubtful by industry standards.

Substantial underperformance is rife and it will continue to be a feature of our industry as long as mine processes fail to use the knowledge which is available.  This starts with mine planning.  Site planners and mining consultants don’t have the data / knowledge so they are happy to keep guessing.  Why do you think many operators treat mine plans as a joke?  Probably because they are.  Effort is needed to help these amazing, technologically-advanced tools produce exceptional plans by facilitating the acquisition, absorption and application of knowledge which is available and is being generated on a daily basis.  It is about the use of information; and in particular the conversion of that information to knowledge and most importantly – innovation (change) on the ground.

The purpose of this blog is to highlight an area where very simple but extremely useful data exists but many people are not using it. The mining plan requires estimates of productivity and costs which feed into the production plan and schedules.  It is the productivity and costs which are a real key to the DCF analysis but are normally done with minimal input from outside the potentially subjective opinion of the person doing the planning.  However, this information is available in great detail from around the world.  The question is posed, “Why do people not use the information available to improve the outcome?” 

I will expand more on this and provide more specific examples in my next blog.

Remember.......The right data.  No speculation.

Graham Lumley 

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

A Productivity Paradigm

I have spent some time looking at the issues relating to productivity and why working hard on a mining solution rather than a “Business Improvement” solution is really important.  You know at the end of the day that the most important strategic ability your mine has is to be able to implement value-adding change.  Also, there is that catch phrase “continuous improvement” which is really, really important when considering the most important strategic ability.  It is interesting when studying data from best practice operations to see the trend of their performance.  In the majority of cases it trends up.  It might be 2% one year, 3% the next and maybe some years it is 0% or down a bit, but the trend is unmistakable.

While Six Sigma, Lean, TOC, etc. are all useful systems, you have to be careful that the improvement you gain is in the removal of your commodity not just the understanding of the “correct” process of business improvement.

However, lets take a step back and assume you are not one of the 10% of mines that has best practice performance of your mining equipment.  Where do you start?  Well the first place you start is in the collection and use of data.  There are monitors for all equipment now and there is no reason not to have one on every piece of equipment.  We have just kicked off a project with Vale in Brazil and they have many, many small excavators and small trucks running around a number of their mines.  From a fleet of 5 m³ excavators and 40 tonne articulated trucks they have a monitor on everything and the data quality is as good as anything in the world.  This is a company which is trying to catch up technology and bring their mines into the 21^st century and they have monitors on everything.  You need to be the same.  It has often been said that if you don’t measure it then you can’t improve it and while this is true it is more than this.  If you don’t acquire it, analyse it and apply it then you can’t improve it.

OK, so we assume you have monitors (and even if you are still to get them) there is a very simple paradigm for improving your equipment.  That is; Fill it up and do it more often.  “Too easy”, I hear you say.  “We already do”, most will respond.  My response to this then is - why aren’t you achieving best practice?  If your P&H4100XPB shovel is moving less than 50 million tonnes per annum or your EX5500 Excavator is less than 24 million tonnes or your Cat 793 truck is less than 5 million tonnes per annum per truck then why aren’t you doing what best practice operations do?  The problem is twofold.  

Firstly, many mines find excuses not to “fill it up”.  These excuses range from, “I can't overload the machine” to “If I don’t fill it up then I can cycle quicker” to “We can’t handle the spillage” to …….  Any of these sound familiar?  Another issue for many mines is that operator have been taught to not fill it up.  The number one, most important thing you can tell an operator is to fill it up.  Irrespective of whether it is a truck, dragline, excavator, front end loader or electric rope shovel the singular directive should be given to the operator; “Fill it up”.  This sounds simple but an operator must be taught what full is and then how to achieve it consistently.  It is surprising how many different definitions there are of “full”.  I will return to this issue of full in future posts as it is a really important concept which many miss.  

Secondly, you must treat every second of time as being important.  This is another one of those attitude issues.  Do you operate your truck for 5,000 hours per year or 6,000?  Think about it this way.  If you could save 15 minutes a day simply by being more efficient in how you park your equipment and how you start it up again (this is one we have actually studied and we reckon 15 minutes a day is the average most mines could achieve per piece of equipment) you would save 90 hours per year.  The key here is attitude towards time.  The average hours worked for a Komatsu 830e truck is 5,159 per annum while best practice mines operate them for 5,675 hours per annum.  Interesting isn’t it.  Now right here I can hear the excuses, weather, height above sea level, hauling profile, etc. but the underlying issue is attitude.  If you found yourself making excuses as soon as you saw those numbers then you need to have a think about your attitude.

Graham Lumley 

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

A Productivity Attitude

The primary aim of these articles is to get members of the mining community to think about productivity.  Productivity is about attitude.  Much can be learnt about the theory behind operating different pieces of equipment and improving productivity but if the mine does not have a ‘culture of productivity’ then achieving best practice is virtually impossible.  Being innovative helps but just doing the simple  things well is a really good start.

The profitability of many mines is highly leveraged against the productivity of the major earthmoving equipment and thus significant management effort should be focussed on getting the most out of this equipment.  Unfortunately exactly what this entails is not always well understood and often other activities are given preference sometimes to the detriment of equipment productivity.  The actions of mine planning, blasting, scheduling, maintenance and man management all play a significant role in production but need to have a common productivity focus or else they can negatively impact the equipment performance.

Figure 1

Figure 1 is the way many mines are run.  The processes in running the mine and the requirements of the corporate entity simply work against getting optimal performance.  In addition, people with an innovative attitude soon get put in their place and drowned within the bureaucracy.  People on these mines are too concerned with ticking career boxes and making sure the processes are all in place, but when it comes to doing something there is always a good reason not to.

Figure 2

The productive mine (Figure 2) shows a different flow of “impacts”.  We now make the equipment productivity central to the mine’s performance, (which is exactly where it should be…surely).  People and personalities become less important and the requirement for equipment productivity becomes of primary importance.

The equipment productivity is now “driving” other aspects of the mine operation.  It is no longer acceptable for mine planning to impact productivity negatively; they know what is expected of the equipment and they produce plans which help the equipment achieve it.  Blasting, scheduling, maintenance, management, etc. are all the same.  The mine has an expectation of performance (which I believe should be dictated by what best practice machines achieve) and every role within the mine should be singularly focussed on helping the mine achieve the required productivity.  We have inevitably found that this is the way which mines achieving best practice operate.

There is a saying along the lines, “the best things in life are free”.  I find it hard to forget as I had to debate this in Year 10 English.  I now prefer to say that the best productivity improvements are free (or nearly free).  Productivity is about people and attitude and it costs no extra for a mine to have a “productivity attitude”.

I have referred previously to Robe River Mine and the upheavals which took place in 1986 under the guidance of Charles Copeman.  At the end of the resources boom which commenced in 1977/78, mining companies were starting to tighten their belts.  Unfortunately this belt-tightening was resisted by workforces which had become accustomed to getting things their own way.  This attitude was promoted by management which made money despite themselves.  Robe River was the first to face the prospects of an extended “difficult” period by attempting to change the attitude of the mine.  I suspect Charles Copeman knew where it would lead as changing a culture is not an easy thing to achieve.  When change did not come Copeman sacked the management team and installed “his” team of people with the attitude he wanted.  Copeman recognised that change had to start at the top and work its way down.  Sure, it did eventually work its way through and the workforce was sacked and then selectively reemployed some on significantly different working conditions, but the important lesson to learn here is that the change started with management.

This was the start of the depressed period I call the “Downsizing Period”.  It ran from about 1986 – 2001.  I remember one day going on to a mine site (1996 I think) I often visited which had a big sign out the front.  Employee numbers usually ranged from 380 – 400.  This day, the number was 196.  I had to look at it a couple of times but it made an indelible impression on me.

The mining industry has now entered the next difficult period.  Forget the super-cycle or a quick rebound.  The largest economy in the world is bankrupt as is the Eurozone and demand for goods will remain depressed so demand for commodities will remain depressed.  I believe this period will run at least 12 more years (probably longer).  Most mines are now like the proverbial stone which has had the blood ringed from it when it comes to people.  You just can’t keep cutting people and keep the mine going.  Once Executive Management and Boards of Directors realise that prices are coming down they will have no option if they want to stay in business but to chase improvements in equipment productivity.  I wonder if they will follow Charles Copeman’s lead and start with mine managers who accept mediocre or average performance (in this case of their equipment)?  Most operators’ jobs are safe because most of them actually want to do a better job and just need management to help them achieve it.

Graham Lumley 

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