Mines Must Operate More Efficiently or Die

The world claims that nobody predicted the Global Financial Crisis and the commodity price drop, rebound and subsequent drop which followed it. But this is untrue. You might think the GFC is over but with 18 first world countries facing debt defaults (which most are desperately trying to inflate themselves out of), it would be a brave person (or a fool) who believed that boom times were returning any time soon.

Back in 2007/08 there was almost universal support amongst Economics academics that the situation in the US was unsustainable. There was even one of the regulators, Brooksley Born, who spoke out and was quickly excised. Funny how the media is only bringing this to light now.

Quite apart from this, in August 2007 I gave a presentation where I said that the current resources boom could not go beyond 2011. I must confess I thought it was over in 2009 with the rapid drop during the GFC but most commodities rebounded strongly. In February 2008, Leigh Clifford was in the press saying we were at the start of a 50 year super cycle. At this point I knew the end was only a matter of time.

We all know that the mining and resources industry works on a boom-bust mentality. It is possible to track this back to 1846 when copper was officially discovered in South Australia. Australia’s first (and greatest) resources boom lasted to 1852/3 after the hoopla of the official discovery of gold in Australia in 1851 died down. We see subsequent resources booms starting in the 1870’s, 1890’s, 1920’s, 1950’s, 1970’s and 2000’s. These are not necessarily stock market booms but rather investment booms and not always mineral resources; in the 1950’s we had a wool boom. The average time between booms starting is 25 years and varies between 22 and 28 years. Of significant interest is the fact that they have never run more than 8 years (nor less than 6 years). Consequently, the most recent boom, which did seem to be a particularly strong boom after a really difficult time in the industry during the 1990’s, and which started around 2003, couldn’t go past 2011. In addition, the 2000’s boom closely mirrored the 1920’s boom which ended in the 1929-1933 stock market crash. In both cases a financial bubble was formed using creative financial products. Don’t kid yourselves here. In the 2000’s high commodity prices were driven by leverage from financial institutions; leverage for speculators to push and manipulate prices up and down, and leverage for US households to keep spending and push consumption through the roof. In the 1920’s high stock prices and resources speculation were driven by leverage provided by brokers with the support of bankers. The GFC was a shake-up to the system caused by a drop in house prices in the US. But to use of leverage to manipulate commodity markets is still very much in play.

What is my point? Don’t believe for one second that investment is going back to boom times in the short term. History says it won’t before 2025. Add to that the fundamentals which see the US basically bankrupt; a financial system which should deleverage but is strongly leveraging itself further; and there is insufficient demand to offset the increased output from the boom to drive commodity prices up. I believe the masses are being sucked into a financial con by the big (mostly US) financial institutions who are using government stimulus and printed money to create an illusion of recovery to drag Mums and Dads back into the market (many through superannuation) so they could further leverage the derivative products. It can’t end well.

We are therefore left in a “bust” until at least 2025 and possibly until 2031. I have said before, the last bust (1986 – 2003) addressed labour numbers in the mines. Workforces were slashed by 50% and more which increased an illusion of efficiency in terms of output per manyear. This bust has embarked on cutting the excess labour and this process is nearly finished. There is not much more blood left in that stone. The mining companies have at least 12 more years to survive until the next boom and will have to address equipment efficiency.

For many mines it will be a simple equation; operate more efficiently or die. The new coal and iron ore barons will die and/or be swallowed up by the big players or by Chinese companies. My estimate, again based on history is that at least 70% of current mine owners / companies will be gone by the start of the next boom. You have little choice but to improve efficiency. You might as well start the process now; the pain will be less later. When your company is losing money on every tonne of a commodity going out the gate what owner will allow their equipment assets to be 20%, 30%, 50% below their capability? They won’t. In the same way, Charles Copeman and Peko Wallsend addressed labour issues at Robe River in 1986 (followed by a raft of less advertised examples across most of the mining industry), this industry will, over the next 10-15 years, address equipment underperformance issues. For some mines which can’t or won’t that will mean closing.

But surely our mines aren’t this bad. Surely, this was also addressed in the previous busts? Well, no it wasn’t. In the 1980’s we didn’t know how badly most of us operated our equipment. We had a feeling that it could be done better but it is only with the advent of complex monitoring systems and the data-warehousing of worldwide data that we now know how inefficient most of the industry is.

If you as an individual and company haven’t developed the most important strategic skill – value-adding change, chances are you won’t survive in this industry to see the next boom.

GBIData.com Loading Unit Full Sample Report

GBIData.com Loading Unit Full Sample Report

Syndicated Project for Rope Shovel Benchmarking in Russia

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

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.

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

Cost and Mine Planning

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

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

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

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

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

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

Graham Lumley 

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

White Paper - Mine Planners Lie with Numbers

White Paper - Mine Planners Lie With Numbers

White Paper - Trends in Performance of Open Cut Mining Equipment

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


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


Take a look at the White paper here.


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

Productivity and Mine Planning - Part 3

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

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

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

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

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

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

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

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

Graham Lumley 

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

Productivity and Mine Planning - Part 2

Before Christmas I started writing about the issue of mine planning.  This entry follows that same theme.  This week, I will again visit the area of mine plans not delivering quality information for appropriate decision making.  Why haven’t shareholders and the stock exchanges held Boards of Directors accountable for their poor decisions on how to proceed with mining a particular resource or whether to proceed at all.  When shareholders and stock exchanges start holding mining companies accountable for the decisions they make, the people doing the planning might need to start explaining themselves.  Development engineers may need to dust off their CV’s and mine planning consultants might need to become acquainted with their Professional Indemnity insurers. 

The scheduling side of Mine Planning Tools is letting the industry down badly.  As new logging and monitoring technologies are being developed, the amount of and complexity of the data is becoming overwhelming and decisions are often made based on only a small portion of the available data.  In most cases production estimates are higher than what is achieved.  Is it a human trait to be optimistic or is it pressure to produce results which are good enough to gain shareholder or Executive Management approval?  We work in an industry which is wildly optimistic about what could be achieved and then prepared to accept mediocrity in what is delivered.

Most of the inputs are based on ‘ideal’ values or values that are given by OEM’s or third party experts.   The biggest mistake made by planners and their managers is not linking the detail of the plan to the requirement of the stage of planning.  A good example is a long term plan which plans a shovel or excavator down to payload, wait on truck, fill time, swing time, etc. and then ties it all together in some impressive-looking Monte Carlo simulation.  Surely for a long term plan you should use realistic annual production numbers.  Detailed analysis follows.  Then we have the problems with planners not using enough detail for short term plans.  Now this is a fine line.  We can collect data ad nauseum but then something changes in the pit so the ongoing optimisation of the plan becomes a balance between collecting and using data and the dynamics of the pit.

The following are actual reasons why overly ambitious production rates have been used from personal experience;

• Dig depths and face heights not considered, 
• Variation in seam dip not considered,
• Planning done in 2D and then merged to 3D,
• Scheduling using maximum potential rate for KPI’s and productivity rather than what can be achieved over a longer period,
• Scaling performance from equipment of different capacity,
• Overestimating hours of work,
• Not considering fleet interactions,
• Not understanding operational limitations, eg. Double side loading vs single side loading
• Not understanding densities and bucket fill

While most mining executives have encountered plans which have gone pear shaped they haven’t always understood why.  Well the answer is in many occasions the poor use of realistic production rates.  The following are actual examples from our work in the past for loading units and trucks:

• Truck fleet actual operating hours 21% below plan.
• Electric rope shovel actual annual output up to 33% below plan. 
• BER (payload / bucket capacity) 25% below plan
• Payload for trucks being assumed at design load, when on-site performance was 14% underloading (limited by tray volume)
• Dragline swing time used in plan was 14 seconds when actual time was 22 seconds.
• In 2008 the average shortfall in dragline coal uncovered was one million tonnes per dragline, (for 20% of draglines there was a shortfall of over two millions of coal)

In each of these cases (partly the reason I chose them) the actual performance is not vastly different from the worldwide average for the make and model. 

You can’t plan effectively without accurate inputs.  You can’t make good decisions without good planning.  You don’t have to accept inaccurate inputs.  Just use the data available.  Benchmark against industry standards.  It seems too obvious.

Graham Lumley 

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

Productivity and Mine Planning - Part 1

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

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

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

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

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

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

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

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

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

Graham Lumley 

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

Productivity prediction – fact or fantasy 2?

It is no wonder our mines struggle with efficiency.  Whose fault is it that equipment routinely falls short of predicted performance?  Mine schedules or new development's mine plans are often not worth the paper they are written on.

Last blog I introduced a spreadsheet provided by a supplier with a prediction of performance of a 62.7 CuM shovel.  

Is it really the supplier’s job to tell you how well the particular piece of equipment will perform on your minesite?  Well….. yes and no.  You would expect them to know how it performs on other sites and this would be valuable input for you to use and relate to your own minesite idiosyncrasies.  Right or wrong they simply do not know how their equipment performs (and the fact that we do know is a major threat to them).  As I said last week, in a perfect world our suppliers would take an interest in after-sales performance but over the last ten years most haven’t.  So long as it is running it is doing OK.

The productivity forecast by the shovel OEM was sent to the mine presumably for planning purposes and I wanted to run through this to show why mines routinely miss production targets.  Last week I looked at the truck capacity and the dipper payloads.  This week I want to look at hours and overall productivity.

The annual hours is an area where you would expect the supplier to have a good idea on performance and I suspect they do.  The problem is that in many cases the hours worked are so low the supplier is probably embarrassed to say what they know.  You see, if there are two suppliers in a tender for a loading tool and one decides to be honest and tell the mine what they really know then they will probably lose the tender.  This is a simple fact. Most mines don’t check information supplied by OEM’s and just simply believe the lies and or guesses.  The end result is that the mine receives two sets of fictitious performance predictions.  Mines only have themselves to blame for this situation.  The data exists and there are people around who do know how to analyse it.

Average work hours around the world for the particular model shovel are 4,599 per annum. The OEM predicted 5,098 (Op hrs * Job Efficiency * Truck Presentation).  They either don’t know (which questions their competence) or they are providing numbers they know are wrong.  500 hours in a year is a lot.  I will look into the reasons why these hours are so low in a future article.

Given the poor performance the supplier is predicting for payload (although given what is happening elsewhere with truck loads being well below the nominated capacity, the average may need to be lower still) and the high hours (relative to other shovels) the end result of 21.8 MBCM places this shovel in the 83rd percentile of performance for this make and model normalised to 62.7 CuM.  Now this is fine and I am sure the mine would love to use this number in their mine planning but if they plan for it and don’t get it the repercussions may be significant.  I understand that the OEM has not provided a guarantee but the mine really needs to know (with some degree of authority) whether the OEM thinks this shovel, working at the particular mine, loading the nominated trucks can perform consistently in the 83rd percentile. Interestingly enough best practice (approx 95th percentile) for this model in the geographic area they are is only 18 MBCM so you work out for yourself if they will get 21.8 MBCM.

Following on from this I revisited another OEM’s calculations for a dragline bucket’s performance this week and saw a much more professional approach to giving the mine something to work with.  In this case the supplier had been given copious data by the mine.  However, the supplier’s understanding of minesite operational issues and a specific data issue still resulted in them arriving at the wrong answer for recommended bucket capacity.  Now this doesn’t seem too bad, except if the mine accepted the recommendation they would have purchased a bucket which was more than 10% too big for the machine.

I can’t believe how difficult this is for the mines!!!  It doesn't need to be.  In this case we had told the supplier that the payloads from the monitor were flawed!!  This is the main reason why we are encouraging mines to not just give their data out to anyone.  You need someone who knows the data and the issues with it.  You really want to come up with the right answers.  We encourage mines to tell the suppliers to contract an independent third party to do the analysis.  At least then the mine can have confidence in it.

Next week I am going to expand further on the issue about mine plans being wrong due to not using real data as their inputs.

Graham Lumley 

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

Productivity prediction – fact or fantasy?

Last column I introduced the productivity paradigm; fill it up and do it more often, and it was my intention to expand on this further.  However, between writing these two blogs a document came across my desk which has caused me to diverge as there are some important data and productivity issues tied up in this document.  I will address a number of these issues in this and the next couple of blogs.

Check out figure 1. 

Figure 1

We are working with one of the major mining companies in the lead-up to them taking delivery of a 62.7 CuM electric rope shovel and a number of 327 tonne trucks.  I will leave the OEM’s names out.  The mining company was sent a productivity forecast by the shovel OEM and I wanted to run through this to show why mines almost religiously miss production targets.  You see many mines will look to everyone except within, to determine the likely performance of new equipment.  Many often turn to the supplier and blindingly accept whatever they are told.  Down the track when forecasts aren’t met there are a multitude of excuses the supplier can use as to why and what has changed since the assumptions were made.  I know because I have helped suppliers get out of trouble for overly-optimistic predictions (and in some cases guarantees) on production rates.  This is not an attack on a particular supplier as they are mostly the same; why would you expect them to know about productivity?  They are equipment manufacturers.  Sure, in a perfect world our suppliers would take an interest in after-sales performance but many don’t.  So long as it is running it is doing OK.  Many mines compound the problem by doing everything possible to operate the equipment as inefficiently as is possible.

Returning to the productivity analysis. The first point to notice is the fudge factors used to arrive at the answer that the client requires.  In this case the required answer for truck payload is 327 tonnes.  So the Dipper Capacity (heaped), swell factor, dipper compaction and fill factor are all variables in the Excel spreadsheet which can be varied to arrive at the target payload of 109 tonnes. 

Here we raise two really important points.  Firstly, does the truck having a nominal capacity of 327 tonnes mean anything and secondly will the 62.7 CuM dipper carry 109 tonnes on average.  Answering these questions in turn.

Does 327 tonnes nominal truck capacity mean anything?  Well yes it does.  It means it is going to carry a lot of something.  But is it going to carry 327 tonnes of something?  Probably not on average.  Average payload for 327 tonne trucks around the world is 288 tonnes (88% fill) while best practice is 299 tonnes (91.4% fill).  This is an issue in itself which I will write on at a later date but we wonder why anyone including a shovel supplier would use 100% of the nominal capacity of trucks when it just doesn’t happen on average.  The simple answer is that while there is plenty of gossip and innuendo people just don’t know.  Maybe you can start seeing the value of data.  The information is available and you don’t have to plan blindly.  OK enough advertising.

The second question; will the 62.7 CuM dipper achieve 109 tonnes on average.  Back in 2001 we reshaped a 44 CuM dipper to become a 48.4 CuM dipper and it carried 111 tonnes on average so a 62.7 CuM dipper can easily carry 109 tonnes, but will a 62.7 CuM dipper of the supplier’s design carry this payload?  Average in-dipper density (payload / capacity) for this OEM’s dippers is around 1.85 t/CuM which would provide a payload in a 62.7 CuM dipper of 116 tonnes.  So in this case they are predicting below average performance achieved by the new shovel?  Why??  Are they recommending the operators won’t need to fill the dipper up fully to average 109 tonnes per load? What happens if the trucks do only carry say 290 tonnes (97 tonnes per pass)?  Again the supplier doesn’t know and is making guesses.  There guesses are as good as most mines' guesses.  But you don’t need to guess.  The data and information is available and you need to use it. 

A further point to this question.  Another supplier’s shovel dippers perform much better.  This other supplier’s average in-dipper density is around 2.05 t/CuM.  So to move the 109 tonne average payload probably needs a 6-8 CuM smaller dipper which weighs say 8-12 tonnes less.  Maybe they could have purchased a smaller shovel or used smaller gears or motors or whatever.   Don’t ever forget that you are using energy to move your spoil and commodity.  Like it or not but the community’s attitude to using energy is not getting better so you can’t ignore efficiency.  The data is available.  You can make informed decisions.  I will return to this specific case of predicted shovel productivity in the next column.

Graham Lumley 

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

A Productivity Paradigm

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

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

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

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

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

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

Graham Lumley 

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

A Productivity Attitude

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

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

Figure 1

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

Figure 2

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

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

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

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

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

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

Graham Lumley 

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