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

GBIData.com - Loading Unit Tutorial

GBIData.com - Dragline Tutorial

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.

More excellent feedback from our Mine Operating Standards Best Practice Course

Best Practice Standards Series - Top 10 Mine Operating Standards Feedback

Again really positive feedback from all participants on Day 2, with numerous in-depth discussions generated as a result of the topics presented.

"Very Interesting and informative"

"The Top 10 Best Practices were really interesting"

"I will encourage other people at my mine to come to this in the future. Would be good for maintenance people to come to this."

Achieved an overall course ranking of 4.57 out of 5. Where 1 is poor and 5 is excellent.

Ranking for Content = 4.37  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.

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

Truck and Loader Matching - Part 1

For many mines the issue of matching truck capacity to loader capacity is problematic and more often than not results in substantial inefficiency.  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.  The goal of getting the majority of trucks +/- 5% of the rated capacity just doesn’t happen.  Clearly an innovative process is needed.  The first stage in innovative thinking is to benchmark (use data) what is currently being done.

The word benchmark stirs more emotion amongst open cut mining fraternity than any other issue.  It is a polarising issue which people either seem to love or hate.  We, of course, are biased and love it because we have the data.  However, the data teaches us a lot and we think we know what benchmarking equipment can and can’t be used for.  Benchmarking is a widely accepted business tool to identify position and performance against previous performance and the rest of the world.  It is the process of seeking out and studying the best practices that produce superior performance.  Benchmarking identifies your strengths and weaknesses, and to determine strategic areas for improvement opportunity. It shows what can, and is being achieved, (best practice).  The two phases to benchmarking are; determining best practice and how your equipment compares, and secondly,  identifying and learning from leading practitioners?

While we are thinking about truck and loader matching it is worth considering the truck.  Can you accurately benchmark mining trucks? When trucks can work on the surface or lift 400 metres or more; aren’t the differences just too great to gain a useful result.  The simple answer is that so long as you understand the mining scenario and the data you can gain useful information from truck benchmarking.  The total output from a truck (measured as rate multiplied by digging hours) is an important component in the overall productivity equation for a mine.  Then digging hours and the different components of it can be broken out.  The dig rate can be broken into load and cycle time.  Each of these can be broken down further.  The analysis may be as broad or as specific as required.  The key to benchmarking trucks and loaders is to take the “glass half-full” attitude.  What can I learn about areas for improvement?  What are others achieving which I should be able to do?  Many mines are shocked by first time benchmark results and justify it through “But my operation is different”.  These mines are consigned to mediocrity.  Those mines that say “What can I do to improve?” inevitably do improve through the intangible process of simply focussing on performance.  Process improvements come on top of attitude-based improvements.

At the end of a benchmarking exercise a mine will get specific data about their trucks and loaders and surely that can’t be a bad thing.  Remember, your data is your most important strategic resource; so get some return from it.

Compounding the problem of truck and loader matches is the variation in truck and loader performance. It is a simple fact that different makes and models work better than others.  In fact performance varies between makes and models of truck by up to 81%.  This means that the average performance of one model moves 81% more than the average of another model.  (You would sure want to make sure you didn’t buy the bottom one – which is still available!!!)  Clearly a hard rock mine which is 400 metres deep is going to have lower truck productivity than a coal mine where the trucks are being used in prestrip.  However, it should be noted that the difference in average performance for excavator models is up to 66% and that is not dictated by the geometry of the pit where they are working.

Look at it this way.  If you knew your RH340 was moving 13 Mt per annum you might think you were doing OK.  This puts you in the 78^th percentile.  However if you also knew that best practice (~95^th percentile) is 22.8 Mt then you can find plenty of potential.  Surely that knowledge is valuable.

It has been known since the 1990’s payload is the key for dragline productivity.  This has been determined from the strength of the relationship between payload and annual output.  With trucks and loaders there is a much greater dependence on the number of hours the equipment is scheduled to operate.  It is a little perplexing that mines can spend many millions of dollars on equipment and then not schedule to use it.  The best practice mines use their equipment.  They don’t have it sitting around idle.  Consequently, when the piece of equipment is operating, payload is again the key to productivity.

Over the next few weeks I want to investigate this phenomena where trucks inevitably take 2.5 or 3.5 or 4.5 passes to fill.  Equipment selection is still being done very badly and it doesn’t have to be.  More on why truck and loader matching is such a problem next time.

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

Complexity and Productivity

If you were to ask a mining executive why their mines’ equipment performance has reduced over time, apart from spluttered expressions of disbelief from some you would certainly get the issue of mining complexity fairly high in the excuses.  This is because site people use this excuse almost universally when asked why their performance has reduced.  It seems logical that mines dig the easiest / most profitable areas first and conditions do generally become more difficult over time.

When executive management starts holding site people accountable for the equipment performance it is interesting to see what happens.  It usually goes something like this;

1. Dry up the source of the bad news – ie. stop benchmarking.  “We know we are 40% below best practice so why keep telling Executive Management”.
2. Advise management that reducing performance is a function of complexity of the mine. “We know it is getting worse and we know it must be the increasingly complex mine we are running.”
3. Create a picture of how complexity reduces digging hours or increases cycle times, etc.

However, should equipment achieve less output as the mine becomes more complex?  This really is a perfect example of not letting the truth get in the way of a good story.  We have looked at this issue from multiple angles and we can’t find any evidence to support this notion that complexity reduces the performance of a particular piece of equipment.  Even for trucks if you use an appropriate measure of truck performance there is no consistent reduction in performance.  Of course as a mine gets deeper and more complex, more equipment may be needed.  This is a completely different issue.

So let’s look at the truth. 

The absolute key to the performance of any piece of equipment is payload.  I can’t find any logical explanation as to why complexity should consistently impact payload.  The only possible impact could be in bench heights and/or pit layout.  However, if superintendents and engineers do their job there is rarely a reason not to set the pit up to ensure optimised payload.  The differences in payload (eg. The difference between dragline best practice and average is 17% and other equipment is similar) are inevitably caused by other factors.  The most common and most distressing is mines telling operators not to fill up the bucket or truck body and kicking the operator when they do!!!  For heaven’s sake the operator’s job is to fill up the bucket and he/she should be encouraged to do this to the best of their ability every time.  If it is overloaded then don’t blame the operator; this is a management failure.

OK so it can’t be payload.  Is digging time related to complexity?  The key area that gets blamed is operational delays and most specifically waiting on equipment or blast.  We have tracked operational delays and we know that when productivity drops, about 40% of the drop can be linked to operational delays but only about 6% is linked to waiting on something.  So really it has little to do with waiting on equipment or blast.  Yes there is a relationship between complexity and operational delays but the major loss in productivity is found elsewhere.

Often the major contributor to a loss in productivity over time is availability.  What happens is that there are two key relationships.  Complexity increases with time and availability tends to reduce with time.  The truth is the two relationships are only linked in a very minor way.  So is it equipment getting older and harder to keep going?  Maybe, but old equipment does get replaced and the trend does continue.

It is my theory that the corporatisation of the mine site is to blame for the increase in operating delays; the reduction in availability; and consequent reduction in productivity.  It is the focus on process and not the result which is primarily to blame.  Managers are often judged on how they do their job, not the end result, and a declining result can be hidden behind exceptional processes.  Because most managers have little real 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.

A bit of a wake-up call here.  Commodity prices (maybe with the exception of silver and 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

Recognising Innovation

Australians on the whole are not overly innovative and regularly fall below average in measures of innovativeness across countries around the world.  There is little doubt that this contributes to poor equipment performance.  I noted a little while back where Dr Peter Lilley of CSIRO was lamenting the lack of “transformational” R&D.  I was staggered (although maybe I shouldn’t have been) that the Minerals Down Under group has a budget of $100+ million per year for R&D.  Think about that for a minute.  Over $100 million per year and they can’t come up with some workable transformational ideas?  You have got to be kidding.

A project which my company undertook was one of the outstanding engineering projects which won Engineers Australia State awards and competed for National Awards in Canberra recently.  What a privilege to be amongst some truly transformational engineering.  Our project – Optidrag, had a budget of $276,000 (thank-you to ACARP).  Now Optidrag really is transformational and is being embraced by a number of the major mining companies.

I am sure this industry suffers a serious case of Myopia when it comes to innovation.  Here you have a project which is one of the outstanding engineering projects in Australia in 2009, as judged by Engineers Australia, and the Australasian Institute of Mining and Metallurgy rejected it as being unsuitable for one of their Mining Conferences.  Quite apart from the fact that it is my project and I was prepared to fly across the country to present it in Perth, how can a project recognised by the pre-eminent professional engineers association in Australia as one of the outstanding engineering outcomes in 2009 be not recognised by my esteemed colleagues in the mining industry? 

Sour grapes?  You are joking.  I got to sit in Parliament House in Canberra with the engineers who were recognised as having the most outstanding projects in Australia in 2009.  I happily saved my money and did not attend the conference in Perth but I am distressed for the industry I work in.  I side with Dr Peter Lilley in so far as believing this industry needs transformational change.  However, I believe it is needed in R&D, technology and attitudes.

The biggest problem with research and development in Australia is they are too focussed on the process rather than the outcome.  Tick the boxes, get your government money and if it costs more than budget or you don’t get an outcome then so be it.  Move on to the next project.  Compare that with the private sector.  We are currently developing a new product.  Exciting and terrifying at the same time.  We went to Westpac, cap in hand and asked them to finance a shoestring budget.  They took mortgages over our properties, a fixed and floating charge over the business, personal guarantees by the owners of the company (my wife and I) and security on our souls in case we decide to depart this world (watch out - banks have contacts in high and low places, although not too many above).  If we can’t produce a product when the money runs out we are screwed.  If the product fails to sell we are screwed.  Despite our patent protection, if a big company steals the idea, I can’t afford to fight it for 10 years in the courts – we are screwed.  If a Rio or BHP fund it they will rightly tie it up so not only does nobody else get it, we also can’t do any further work on it.  The research organisations haven’t delivered and small people have incentive not to be innovative.

Transformational changes in technology don’t come along too often.  You can think about draglines, hydraulic shovels, etc as being major advances but they are few and far between.  The thing which concerns me is that sometimes ideas are not advanced for the wrong reasons.  Politics in our large mining companies and our research institutions ensure some truly transformational ideas will never see the light of day.  Consider the following.  After presenting Rio Tinto's automation work to the Austmine conference in Brisbane last May, Rio Tinto's head of Innovation, John McGagh, was asked how we, as small, dynamic innovators could get our products in front of Rio Tinto.  His response was distressing. "Rio have people and resources working in this area.  If you have something of value to us, we will find you."  I really don't know where to go with that.  I suppose it is the golden rule; He who has the gold makes the rules.

I have said much in recent weeks about transformational changes in attitudes towards productivity.  Productivity is largely about attitude.  I fear for Rio's investment in automation for this very reason.  Attitude is the key input into the differences between best practice operations and the other 90%.  Some have given up and accept mediocrity or pay contractors to be mediocre or make huge investments in technology.  Some mines and contractors have grabbed the opportunity and have moved to fill the gap between average and best practice performance.  They are the companies you really want to work for and with.

Graham Lumley 

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