GBIData.com Loading Unit Full Sample Report

GBIData.com Loading Unit Full Sample Report

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 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