7 Mine Scheduling – Why It Doesn’t Work!
Mark Bowater
? Leaving Mine Planning In A Better Place Than I Found It ? Developing Mining Engineers ? Improving Mine Planning Systems
This article is the seventh in a series of articles on various issues and topics relating to mine scheduling. If you find this article beneficial, you can always go back to the first article here and read through the series in order.
Let’s start by zooming out and looking at the typical mine scheduling process used at most mine sites. We’re given a geological model to use for mining designs, from which we export quantities such as volumes, areas and thicknesses, and a range of qualities into a scheduling model. We then use a scheduling tool and build in assumptions for the following:
·??????Equipment operating hours
·??????Equipment production rates
·??????Parameters for turning geological and design volumes into schedule quantities, e.g. converting geological modeled coal volume to a product tonne, prime waste volume to a dragline total volume, etc.
But, the one area significantly undervalued in our mine scheduling processes and that I rarely see built into standard mine scheduling, is the inherent variability that exists in mining operations.
As an example, let’s look at just one activity in the mining process, say truck and shovel operations, here are just some of the variables that occur in this process:
·??????Actual dig volume
·??????Material density
·??????A range of equipment lost time events that are dependent on the activity or other variables, such as:
o??Unscheduled maintenance
o??Wait on blast
o??Wait on dozer
o??Wait on other equipment
o??Wait on access
o??Dust
o??Shovel hang time
o??Truck queue time
o??Positioning
o??Deadheading
o??Idle
·??????A range of equipment lost time events that are not dependent on the activity or other variables, but are still variable within themselves, such as:
o??Meal breaks
o??Shift change
o??Scheduled maintenance
o??Refuelling
o??Crew communications
o??Wet weather
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o??Operator checks
·??????Note the above delays will be different for each equipment item and typically be very different between loading units and trucks
·??????Shovel bucket payload
·??????Truck payload
·??????Bucket cycle time
·??????Truck spot time, travel time, and dump time
·??????The number of operational trucks
·??????Double-sided, single-sided, or top loading
·??????Face height and face width
This is just one of numerous activities within a mine schedule and every other activity, such as drilling, blasting, dragline, coal mining, and coal washing, all have a large range of inherent variables. Typical mine scheduling involves consolidating all of those variables into a single assumed productivity rate multiplied by operating hours that come from a calendar, with single point assumptions for a range of non-operating events.
So we take a mining operation with hundreds (or more likely thousands) of variables and condense it down to a single snapshot in time that we call a “mine schedule” and think that is representative of the mine! We expect an execution team to implement that plan, but then to exacerbate the issue even further, we invest resources into trying to hold the execution team accountable, by measuring “compliance to plan”.
Who are we kidding?!!
Is there a bigger waste of time than trying to measure compliance to plan, when that plan woefully under-represents the complexity and variability of the mining operation?
Let’s use a very, very simple example to highlight this. I’m using a simple example so I can calculate the range of outputs using maths, rather than creating a simulation model. However, the real scenario at a mine site is significantly more complex than this example for many reasons, including that mine sites carry inventories, variabilities are not as simple as normal distributions, and mines involve countless interactions between large numbers of dependent activities.
I want to determine the range of total time taken to uncover a block of coal and then rail it to the port. This example involves just one sequence of activities as follows:
·??????Drill
·??????Blast
·??????Waste Excavation
·??????Coal Mining
·??????Coal Washing
·??????Railing
There are no inventories, all activities start when the previous activity finishes and they all have the same production parameters. Each activity has an average execution time of 10 days, with that execution time being normally distributed with a standard deviation that is 30% of the mean, so a standard deviation of 3 days.
In this scenario, the average total time for those tasks in sequence would be 60 days and the standard deviation would be 7.3 days. This results in a 41% probability that the total time will be wrong by more than 10% (so out by +/-6 days) and a 17% probability of it being wrong by more than 16% (so +/-10 days). It is not uncommon for mine sites to run with inventories of 5-7 days of production, so very simplistically speaking, in that case, there is a 40% probability of a scheduling issue arising.
An assumed standard deviation of 30% of the mean is potentially on the low side, analysing a range of real data for draglines and shovels, led to standard deviations closer to 50% of the mean. In the example above, if we changed the standard deviation from 3 days to 5 days, then there is now a 62% probability of the schedule being wrong by more than 10% and a 41% probability of it being wrong by more than 16%.
Given the system we are scheduling has a huge number of inherent variables, why are we not incorporating variability and running stochastic models as a standard process for our mine schedules?? We’re never going to create “better” mine schedules while we continue to run mine schedules on a deterministic basis, that is they have no variability in the inputs and so produce a single output.
If you found this article of value, then click here to read the next article in the series. Or if you want to start back at the beginning of the series of articles, click here.
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Digital Operational Planning Specialist
3 年Well written, I can't agree more. In my view, process digital twins (date linked simulation models) are the solution. I might be biased, though. We have built multiple simulation models trying to tackle this problem and provide realistic and achievable targets. The biggest issue we are still facing is quickly getting all of the data into the model, and mines don't capture all of the required inputs (or manually capture) to model a mine to its full complexity.
Principal Mining Engineer at Stanmore
4 年Exactly, how do we stop stretch targets being put into mine schedules when all business are so convinced they are critical to success? They just end up making a schedule pointless and a mine inefficient, which ultimately impacts culture and personnel performance. Fundamental strict scheduling rules need to be established from the top down in a business and enforced to stop manipulation of variables to gain a goal seeked outcome. The challenge is when prior commitments and expectations on mine output have been set, it’s a hard pill to swallow as a real schedule will always show downside....so back in goes the stretch.
Underground General Supervisor at Agnico Eagle Mines Limited
4 年This is a fantastic article. In my opinion it's a common one due to the disconnect between operations and engineering. Operations over committs and engineering underestimates. A mine is fluid, evolving and dynamics. This is often where experience in management can be essential to bridge forecast gaps between the two.
Director Mining Intelligence and Benchmarking at PricewaterhouseCoopers Australia
4 年Gday Mark. Thanks for the time and effort you put into this article. You know I am not a mine planning engineer and you know I have serious issues with how mine planning is done. You and I have talked about it many times. The use of unrealistic metrics / forecasts in mine planning is a cancer in this industry. The three main reasons for this are; 1. Unjustified Optimism, 2. Ignorance, and 3. Strategic Misrepresentation. It extends to the JORC and VALMIN codes where the codes require inputs based on actual performance metrics. Despite the codes requiring it, very, very few geologists and engineers use real performance metrics (presumably measured on other sites or their site) when producing reserves statements and valuations. The annoying part about this is that the respective groups are not holding the practitioners accountable for not complying with their own codes. On an individual mine basis, the frustrating thing is that most mines have data for their current equipment performance and with a small amount of analysis provides very accurate inputs. Even the much maligned Monte Carlo simulation can provide extremely accurate forecasts if real inputs are used. One problem I suspect is partly that most mines do not know how to extract the metrics and measure the variability (and use it appropriately) from the equipment performance databases. The other problem is that many managers don't want a mine plan based on real inputs as it emphasises how inefficiently they are operating the mine. Another issue is that Mine Planning Engineers (and their managers) love detail. I suppose it helps them prove how smart they are. I have a sarcastic comment around the detail in a mine plan (provided it is accompanied by pretty colour pictures) correlates with the intelligence of the planning engineer. The simple fact is that due to the variability you describe most detail is noise and is wasted effort, time and investment. Catch you soon.
Sr. Director, Supply Chain, Mfg & Operations
4 年Great article and series on scheduling or Mine and scheduling generally. An interesting question is robustness and how long a schedule is valid before a reschedule is required. In some discreet mfg plants its about 1-2 minute. Have not seen for mining. Many thanks to @ Simon Thompson for commenting, otherwise would have missed a great article!