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

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 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 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

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

White Paper - Trends in Performance of Open Cut Mining Equipment

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


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


Take a look at the White paper here.


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

Productivity and Mine Planning - Part 3

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

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

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

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

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

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

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

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

Graham Lumley 

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

When to spend on innovation

I have introduced a number of broad-based productivity issues over the last few blogs.  We will get on to some more specific issues but in this piece I want to introduce another broad issue.  What is the best time to invest in productivity enhancement?

The two key areas to productivity enhancement are during the R&D / equipment selection phase and during the post-commissioning phase.  That is, get the right equipment and optimise its use.

To understand the interaction between mining and knowledge I will return to the presentation by Jari Kuusisto presented to the Smart Innovation Festival in Brisbane in May 2008.  Kuusisto presented the curve of ROR vs Product Life Cycle.  I have added the risk and cost benefit to this to provide the following plot.

The product life cycle can be described from the mine or the supplier’s perspective.  In the plot here it is viewed from the mine’s perspective.  The mine follows a process of Correct Selection – Order placement – Commissioning – Equipment Enhancement.

The rate of return on money invested is highest during the development / selection stage of the product and during the after-sales service / equipment enhancement phase.  However, the risk on the investment is highest early in the process and reduces further after the product has been delivered.   When these two plots are combined it can be easily seen that the cost-benefit (return / risk) is moderate at the start of the process (during R&D / selection) and highest after delivery/commissioning (during the process of “asset optimisation” or “capacity utilisation”).  It is no coincidence that the application of knowledge is needed most during these two sections.  It can therefore be deduced that the input of knowledge is related to cost benefit.  It is also interesting to note that the highest cost benefit occurs when the knowledge is applied in the after-delivery phase of equipment optimisation which is largely process related.

If one looks at this from the perspective of a supplier the product life cycle  becomes: R&D, Collect Orders – Commissioning – After Sales Service.  Interestingly, the plot follows exactly the same form.  For the equipment supplier their greatest return comes in after-sales service.  This is a really interesting observation because during the boom I had an almost impossible job getting suppliers to listen to anything to do with knowledge and after-sales support.  Suppliers apparently were able to sell all their equipment and the concept of using after-sales service and knowledge as a means of helping mines use their equipment better and as a point of strategic advantage wasn’t considered.  This has clearly changed.  I have had a number of companies approach us about using the data and knowledge as a key element in their marketing strategy.  During the boom nobody seemed to care that there was one brand of truck which was 84% more efficient than the worst.  Funny isn’t it?  During the boom if it had wheels and carried dirt it was good enough.  Now a lot of mining people don’t seem to want to take the risk that they will buy the worst truck and some suppliers seem motivated to use data to help them be as good as their equipment allows them to be on the mine sites.

It seems prudent for suppliers to understand the words of S. Downton on ecustomerworld.com,

Delivering high levels of customer satisfaction through a well-managed service operation can increase loyalty, and thereby sales, by as much as 8 times - greatly enhancing the value of the business. Successful manufactures increasingly focus on their customers' total lifecycle by investing in their service management business to maximise the value captured throughout the product lifecycle. This means that the product sale is only a small part of the overall value during the complete product lifecycle and is only the start of the customer relationship.

Support for my belief that the world of suppliers has changed came late last year when we found a bucket manual which I had written for a mine to optimise the performance of the bucket they had just purchased, had been blatantly plagiarised by the OEM and presented to other mines purchasing their product under their name and logo.  This supplier has seen the value of knowledge (particularly linking the knowledge to the company) and has seen it as providing strategic advantage for them.

Graham Lumley 

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

Knowledge Intensive Mining

I have addressed the issues in the previous couple of blogs about the poor use of knowledge and value adding through innovation by the Australian mining industry.  I have been quite negative about how the Australian mining industry is performing in this essential area.  So rather than always be negative, the aim of this discourse is to describe a process and a culture which will form the foundation of improved performance through knowledge-intensive mining. 

With some hesitation I return to University and 1^st Year Chemistry.  We consider a reaction with a desired result.  The chemical reaction requires reactants and a catalyst.  To achieve the desired reaction (adding value through innovation) we need the correct reactants (processes) and catalyst (culture)

If you knew that there was an M8050 dragline that achieved 21 MBCM annually (17% higher than the next best), would you want to know how? If you knew there was an EX5500 excavator which achieved 12% higher than the next best, would you want to know how?  Most people do and this type of broad information is the foundation of knowledge-intensive mining (but it doesn’t stop at the broad-based information).  The following definition is proposed for Knowledge-Intensive Mining:

Knowledge-intensive mining is the acquisition (from internal or external sources); absorption (through active understanding) and application (via systemic processes or one-off projects) of knowledge which improves the mining process. 

The steps to gaining the tangible improvements, whether they be due to a change in the machine or mining process, must be preceded by a number of steps of gaining the intangible knowledge. Each individual needs to be accountable for their own attitudes and actions, regardless of their position.  Not everyone keeps detailed records of everything he/she does, recognise some form of sub-optimal result, does something different, etc.  What is needed is people doing business improvement on a “micro scale”.  What that means is when a person sees something happening which is sub-optimal they immediately do something to change it.  For an operator an example might be a half full bucket or poor positioning on a block.  Improving this doesn’t take a BI program but if you look at it, a very similar (undocumented) quality / six sigma / lean process is taking place.  To achieve these gains you don’t need a BI program, you need a focussed and motivated workforce / team.  To get this you need the processes and the culture.  Each person up the management line, Operator, Foreman, Supt, Manager, General Manager, etc. needs to take this micro approach to business improvement and it appears clear that many are not.  All too often the upper level manager is too concerned with “ticking the boxes” and / or not making a mistake to worry about really using knowledge to achieve innovation.  After all, their performance is normally judged on how many mistakes they have made, not how innovatively they have acted.

Whether work is in coal mining, hard rock mining, infrastructure, environment, or wherever, the messages are the same:  Firstly, the idea that only tangible things add value must be changed.  We must value knowledge.  We must actively acquire knowledge, absorb it and apply it to add value through modifying processes.  Remember, processes are the innovation reactants.  They are the aspects which combine to produce productivity. 

Changes to them are sometimes hard to grasp or understand but they are none-the-less the fabric of performance.    Secondly, culture is the innovation catalyst.  Not change for the sake of change but rather change which is targeted at the bottom line.

So what do we do about culture? This is the more difficult question at all levels of the mine but if we look at management there are two key issues to do with culture.  Firstly, the attitude of rewarding people who don’t “stuff up” must be changed.  If you aren’t allowed to be wrong then your employer won’t ever achieve anything.  Companies must reward people who are prepared to take measured risks even if those risks fail.  Anecdotally, it is smaller companies which encourage innovation but they don’t always respond well to failure so their support of innovation is not always useful.  If you are rewarded for not “stuffing up” or if you work for a company which describes itself as a “fast follower” then find another company which encourages innovation.  Secondly, you must believe you have a right to be wrong.  If you as an individual aren’t prepared to be wrong then you won’t ever achieve anything.  Unfortunately our education system, which I admire greatly (I am married to a teacher who I met in a small town in the middle of nowhere), encourages people to be right.  There is little encouragement to be innovative and get it wrong.

It is these attitudes (or lack of them) which is strangling the advancement of the Australian mining industry.  

Graham Lumley 

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

GBI Dragline Dictionary V2 eBook - Sample

GBI Dragline Dictionary V2 eBook - Sample

Top 20 Dragline Best Practices Manual Sample eBook

Top 20 Dragline Best Practices Manual Sample eBook

From knowledge to innovation

In my previous blog I discussed the creation of knowledge and adding value through change (innovativeness). The big step forward which is needed for the mining industry is a better understanding of the link between knowledge and innovation.  The innovation process has four characteristics.

1.       Being part of the global world.  Knowledge is everywhere and there is good work being done around the globe.  For example, Europe is not renowned for mining knowledge (although maybe Russia and several of the former Russian States may be exceptions).  Many of the European countries fall in the top quartile for innovativeness and as such frequently have developments of interest here in Australia.  In addition to a number of large equipment companies from Germany which are doing some good work, there are real technology advancements coming out of Europe.  The Vienna Test System which comes from Austria has tremendous application in the Australian mines for operator selection; significant electrical advancements are being made in Germany and tested on draglines in Estonia; etc, etc.  Mines should be grasping knowledge and/or developments from anywhere they might come.  As a primary consideration they should be benchmarking wherever possible.

2.       Innovative individuals and communities.  The mining industry needs innovative people.  I have mentioned it before but the perfect example is the Australian Coal Association Research Program which distributes over $10M annually of the industry’s money for coal mine research.  This program draws some of the smartest and most innovative thinkers into the coal industry research and development arena.  To ACARP’s credit, they do get the whole concept of knowledge development and the link to innovation and have a clear focus on adding value.  AMIRA also plays a vital role for the broader mining industry.  Mines need to build an innovation culture where change is not done for the sake of change but rather to add value.

3.       Systemic Nature.  Being innovative is not something which can be turned on and off.  It is the culture; the way the people think and act.  Some people believe it is difficult being innovative within a large mining company.  This is because they are thinking on too large a scale.  Too often we think that multi-million dollar projects such as Universal Dig and Dump, equipment automation, etc. are required to be innovative.  However, knowledge intensive mining and being innovative can be done on a micro-scale.  Each person can take responsibility for themselves and can follow the path of acquiring, absorbing and applying.  On a micro-scale the operator who, having difficulty loading one bucket ends up with half a load, actively changes their digging for the next cycle and the one after that has applied knowledge. As a summary, each individual being innovative relies on how they are acquiring, absorbing and applying the knowledge which is available.

4.       Customer and user-centric.  This is what I call “bottom line” service.  From the provider’s perspective, knowledge and service provision must be focused on what the user / mine needs.  All too often the mining industry funds work by research groups and consultants, which focuses on the process and how smart the process and people are.  For knowledge to be valuable and to facilitate the innovation process it must be value-based, ie. it must provide bottom-line / profitability improvements for the mines.  The key to this is the person pulling the levers or turning the steering wheel.  This person has the ultimate control over what output is achieved.  Therefore the mine must engage the operator / driver in the optimisation process. 

In the European Innovation Survey, Australia fell in the third quartile.  We are below average in innovativeness, and by industry standards the mining industry is very conservative.  In fact, I could name quite easily those mines in Australia which I consider to be genuinely innovative.  The reasons for this are quite clear and I will address them in my next blog.

Graham Lumley 

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

Improving Equipment Performance – Knowledge & Innovation

The two steps in business improvement for any process, including equipment performance, are to gain knowledge (about gaps in performance) and to do something with the knowledge (innovation). 

There are a number of generators of knowledge.

• R&D which needs policy to support R&D and money to do the R&D.  The money attracts smart people to do the R&D.  A really good example of this has been the ACARP program in the Australian coal industry.  $10M+ of funding is available per year and some of the smartest researchers have been attracted to this money.
• Experience from time on the piece of equipment.  The knowledge is gained from the interactions between the people and their environment.  
• Training which is defined in terms of the content and the delivery / instruction generates knowledge for the trainee.
• Information, which is generated from data, becomes knowledge when it is meaningful to the recipient.

Experience happens, data is collected and benchmarks done, training is provided and research is done by various organisations, however the transition to knowledge is not always done well.  Many have said, "If you don’t measure it you can’t improve it", but it is more than this.  If you don’t actively acquire it, absorb it and apply it, you can’t improve it.  Internal knowledge resides in the people and the captured data.  External sources may include trainers, researchers, consultants, market intelligence, etc.    

The effective generation and use of knowledge is being stifled at the majority of Australian mines.  Good management doesn’t just put red lines through a whole heap of budget items.  Good management is about cost optimisation in the short, medium and long term; not necessarily short-term cost minimisation.  Cost optimisation always allows a budget cost (usually relatively small) to become smarter and practice real continuous improvement.  If an organisation wants to stay operating during the difficult times ahead they really need to spend some money to save more.

The expansion of knowledge and the use of knowledge has attracted the attention of many key mine people, however, the further one looks up through the corporate ranks the less appreciation for the value of knowledge is apparent.  Many people in decision-making positions, struggle with grasping something which is not tangible. 

Most Australian mines fail to take the steps to innovation.  Getting a benchmark or a consultant’s report or a mine plan demonstrates that the manager is doing something.  But really, if something practical isn’t done with it, all he/she has done is waste the company’s money in an attempt to make themselves look good and tick their career boxes.  Without taking the step to innovation / change, nothing of value is achieved for the mine.  A culture has developed whereby not taking risks is rewarded.  "If you want to get ahead don’t stuff up".  Add to this the personal issues many Australians have to being wrong and you can see why innovation is so difficult for some mines. 

The easiest way to use knowledge and to add value is through using data to evaluate and understand what is currently happening and to change based on the knowledge of what others around the world are doing.  It is not about the creation of a simple one-page report from the monitor because chances are that it has been written by an IT person with limited knowledge of what is meaningful.  It is about the active creation of meaningful reports and a program of helping the recipients understand and plan to be better.

Gaining Competitive advantage from Data

In this discussion I intend to discuss how the vast amounts of data which are generated on mining equipment can be turned into productivity, profitability and ultimately competitive advantage.   This is what I call “bottom line” services. 

There is a wealth of valuable mining data being produced around the world every day, however, mines are failing to benefit from it as they don’t have the ability to capture and meaningfully apply it.  Through poor management of available data and the loss of personnel, the continuity of information acquired and knowledge applied to run mines efficiently is being broken. If not remedied, this will prove very costly in the long term.

Data by itself is just a mass of numbers - it needs to be analysed and assessed to extract value. However, mines must be wary of subjective analysis which is done for the benefit of another party rather than the mine itself. All too often the mining industry funds work by research groups and consultants which focuses on the process and how smart the process and people are.  For knowledge to be valuable and to facilitate the innovation process it must be value-based, ie. it must provide bottom-line / profitability improvements for the mines.  The key to this is the person pulling the levers or turning the steering wheel.  This person has the ultimate control over what output is achieved.  Therefore the mine must engage the operator / driver in the optimisation process.  To do this they must have an intimate understanding of the information being provided through reporting of performance

The best way to explain this is through a case study. While most mining equipment has loggers generating data (and if your’s don’t then they should) the loggers on draglines produce the most comprehensive data.  A dragline with production and maintenance loggers will have over 2,000,000,000 signals processed into nearly 20,000,000 pieces of data, stored in databases every year for post processing.  (Is it any wonder that mines find themselves swamped by data? )

In this case study, the dragline is real and the results are real.  Most importantly, the lessons to be learnt can be applied to any operation and any piece of equipment.  This dragline historically operated at a production rate better than average.  When the maintenance logger was installed, a program of improving productivity and reducing damage was initiated.  The demand for change came from a range of areas, including, the workforce, technology, economics, competition,  etc.  The Mine Manager assumed the role of “change agent” and sought the support of a range of internal and external people who he perceived could help him.  Not unexpectedly, resistance to change came from individual and organisational sources.  In overcoming the resistance to change, the site focused on education, communication, participation, facilitation, support, and negotiation. 

A key part of this program was delving into the masses of data to provide specific and targeted reports to a range of people across the site.  Remember the word “meaningful”.  This is the key to operators and drivers understanding and changing their actions.  Data must be presented in a meaningful way. Reports included benchmarking, monthly production reports, operator comparisons, individual operator reports, and bucket reports, all of which included comprehensive productivity and maintenance information.  They included tables of data, line graphs, bar graphs, pie charts, and anything else the mine requested to help them understand what they were doing which impacted productivity or maintenance.  Of critical importance was the fact that these reports were followed up with visits from dragline “experts” and trainers who helped all levels on the mine site interpret the reports and develop plans for “change”.  In addition, all operators and supervisors attended off site courses which focused on team and individual understanding of the job they were doing.

The data was used to determine which digging techniques increased damage both from a global and an individual basis (comparisons were made both internally and externally).  Effort was made to identify which operators needed help with productivity or maintenance or both.  During the first 223 days of the program dig rate increased by 15% and boom stress decreased by 25%.  In conclusion, the data and the analysis of it were not the reason improvements were made.  Data was an integral part and improvements would not have been as significant without it.  However, the real impact was the organisational culture which was created. The dragline was changed into a “learning group” with the following characteristics;

·         A shared vision,

·         Old ideas were discarded,

·         The dragline operation was seen as a system of interrelationships,

·         People actually communicated with each other, and

·         Personal interest was less important than organisation interest.

Competitive Advantage for a mine or organisation comes from operating at a higher productivity and lower cost than others.  It should be seen as originating from doing a whole range of actions better than your competition.  On this definition, the dragline studied here has definitely assisted this mine in achieving competitive advantage.    The data was not the reason competitive advantage was gained but rather the most important strategic resource which itself was mined to extract the value.  Change was the mine's most valuable strategic ability and enabled the knowledge from the data to add value to the mine.

Graham Lumley  - CEO of GBI Mining Intelligence

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