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