TSV Mining: Variation Analysis in Mining Part 1 - Precision and Accuracy
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TSV Mining Pty Ltd [email protected] M: +61 438 886 473 www.tsvmining.com.au Presented by: Christopher Braund Director Total System Value VARIATION IN MINING PART 1: PRECISION AND ACCURACY
description
Variation is one of the largest value destroyers in the mining industry. This presentation shows how to reduce and manage variation using the definitions of Precision and Accuracy. The ability to remove floating bottlenecks in an operation is linked to the precision of processes, and to the demand placed on them. Often in mining, the cost associated with removing processes from being an occasional bottleneck is higher than the gain in throughput. Process capacity and inventory requirements are heavily linked to precision, which is rarely reviewed by any mining operations.
Transcript of TSV Mining: Variation Analysis in Mining Part 1 - Precision and Accuracy
TSV Mining Pty Ltd [email protected]
M: +61 438 886 473 www.tsvmining.com.au
Presented by:
Christopher BraundDirector
Total System Value
VARIATION IN MINING PART 1: PRECISION AND ACCURACY
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Why focus on variation? Variability in a mining operation is a large value destroyer It erodes value due to:
Out of specification product Lost product in processing Lost product in recovering Lost people and equipment utilisation Lost production Rework
There are two main factors at play with variation in a system:1. Precision
2. Accuracy
Lets look at a simple definition for each
Precision and accuracy Precision is the ability to
replicate previous. Consistency
Accuracy is the ability for results to reflect the true value, or plan
Precise but inaccurate Accurate but imprecise
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Example of precision and accuracy An example of precision and accuracy in a mining operation can
be illustrated in the following preparation plant scenario:
If the target ash for a coal product is 10%, and the plant is checked 6 times during the shift: All 6 results are exactly 15% ash, the plant would be precise but not
accurate The 6 results are: 7%, 12%, 14%, 5%,13%, 9% = 10% average, the
plant would be accurate but not precise
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How to measure precision and accuracy
Precision can be represented as the coefficient of variation of a process: Coefficient of Variation = Standard Deviation / Mean
Accuracy of a process: The comparison of the planned mean to the actual mean of a
process
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Some precision and accuracy in the mining industry Operating ability
Process control, execution, and some policies effect the precision of a process
Planning and some policies effect the accuracy of a process
Six Sigma variation Common Cause Variation effects the precision of a process Special Cause Variation effects the accuracy of a process
Process resourcing Demand effects the precision Overall capacity effects the accuracy
And many more
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Process resourcing precision and accuracyLets look at the definition of precision and accuracy in the context of process resourcing:
To keep it simple, we have used a deterministic process capacity. As we can see, this process capacity is well below its requirements
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Accurate process resourcingNow let’s make the capacity match the mean of the process demand:
We can see that the capacity of the process is accurate with the demand, however with the above distribution the imprecision of the demand will cause the capacity to be under resourced for a large portion of the time, this will cause it to be a floating bottleneck
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Accurate process resourcing and precision Having the process capacity accurate with the expected demand can
lead to the process being under resourced The imprecision in the process caused by varying demand needs to
be measured and understood in order to achieve the highest process value
Some of the options in dealing with imprecision include: Having excess capacity (sprint in the process) in order to cater for the
imprecision Increase the precision in the process demand Both increase the precision and have excess capacity
All options need to be understood and valued in the system
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Be inaccurate by adding additional capacityLets add enough process capacity in order to ensure we can cater for all process demand:
Excess process capacity has been used, well past the mean of the process demand. If this process is never to be a bottleneck in the system, the capacity must always be greater then the demand
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Increase the precisionWhat happens if we increase the precision in the process and still have no excess capacity:
Here we have increased the precision and have significantly reduced the amount of time we are under resourced, however we are still under resourced for a large part of the time in the above distribution.
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Increase the precision and then add capacityFor most processes the best option is to increase the precision and have excess capacity:
Most processes will find that the highest value proposition will come from combining the two.
Lets look at the relationship between costs and adjusting accuracy and precision
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The cost of increasing precision and adding capacity Typically operations want both precision and accuracy – it is seen
as having the best of both worlds What operations should be looking for is the right amount of
precision and capacity in order to maximize value The simple way to look at a process is to always compare the cost
of the options:
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The cost of increasing precision and adding capacity For some processes increasing precision can be very expensive,
while increasing capacity can be quite cheap, others can be the opposite
As changing either has a monetary effect, there can be limits on where the additional costs do not justify removing the process from occasionally holding up the system – ie it will be a floating bottleneck
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Sometimes it is higher value to have a floating bottleneck
In this example the next cost effective choice is to increase capacity (instead of improving precision). What is shown here is that the cost of adding more capacity will not be justified by the increased throughput, it is better left as an occasional bottleneck
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Example of where the better option is to have a floating bottleneckLet’s look at a prestrip circuit trucking requirement:
The trucking requirement distribution is optimised, and the digger is the logical planned constraint. To move from 4 to 5 trucks would not be justified in the increased throughput. The best option in this situation would be to lose potential throughput in the system and occasionally have the trucks as the bottleneck.
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Changes in precision of process demand The throughput of the system, and the capacity requirements are
heavily affected by the precision (in this case process demand).
Let’s look at some of the things that can effect the precision of process demand: Task balance Task size Intensity Inventory
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Task balance and precision
There are 3 pits: A, B and C. A dragline works from left to right, spending 70 days in Pit A, 170
days in Pit B and 120 Days in Pit C
Strip 1
Strip 2
Pit A Pit CPit B
After completing Strip 1 the dragline continues onto Strip 2 Once the dragline is finished in a pit, other processes need to take
place in that pit and be finished before the dragline returns in the next strip (say coal mining, drill and blast, partings, more coal mining etc)
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Task balance and precision
From the information we can calculate the time the dragline spends in a pit, and the time it spends away from that pit for other processes to take place:
Strip 1
Strip 2
Pit A Pit CPit B
Precision here is calculated using the coefficient of variation Lets see what happens when we move the boundary between Pits
A and B in order to improve the balance and precision
Pit A Pit B Pit C Average St. Dev. Precision (CoV)Time in pit 70 170 120 120 41 34%
Time away from pit 290 190 240 240 41 17%
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Task balance and precision
Strip 1
Strip 2
Pit A Pit CPit B
The averages remain unchanged, however we have created precise task demand
The new pit design will be far more stable in an operation with demands being stable for the dragline as well as other processes. (This also goes to show that some operations will naturally be more stable due to resource location and layout)
Previous Pits Pit A Pit B Pit C Average St. Dev. Precision (CoV)Time in pit 70 170 120 120 41 34%
Time away from pit 290 190 240 240 41 17%New Pits Pit A Pit B Pit C Average St. Dev. Precision (CoV)
Time in pit 120 120 120 120 0 0%Time away from pit 240 240 240 240 0 0%
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Task size and precision We can’t always move pit boundaries in order to improve precision,
however we may be able to redesign a pit to operate as two separate areas
Let’s use the same pit we had previously:
Strip 1
Strip 2
Pit A Pit CPit B
Pit B is a large task for the dragline. It also has the least time away for other processes to take place (and would likely have the largest amount of work to do in the area)
Lets reduce the size of Pit B and split it in half
Pit A Pit B Pit C Average St. Dev. Precision (CoV)Time in pit 70 170 120 120 41 34%
Time away from pit 290 190 240 240 41 17%
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Task size and precision
Strip 1
Strip 2
Pit A Pit CPit B1 Pit B2
Here the average of the pits has chanced, as we have essentially added a new pit. The average amount of time in the pit is now reduced significantly, and we have increased our average time away (NOTE: reducing the size of tasks also has a big impact on high intensity operations by improving time inventory)
Again we have managed to improve our precision in demand placed on processes
Previous Pits Pit A Pit C Average St. Dev. Precision (CoV)Time in pit 70 120 120 41 34%
Time away from pit 290 240 240 41 17%New Pits Pit A Pit B1 Pit B2 Pit C Average St. Dev. Precision (CoV)
Time in pit 70 85 85 120 90 18 20%Time away from pit 290 275 275 240 270 18 7%
Pit B170190
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Intensity and precision Let’s use the same pit again with the same results:
Strip 1
Strip 2
Pit A Pit CPit B
For this example we will double the production out of the area by adding a second dragline
The two draglines cannot work in the same pit at the same time, and again Strip 1 must be finished in a pit before Strip 2 can start
Pit A Pit B Pit C Average St. Dev. Precision (CoV)Time in pit 70 170 120 120 41 34%
Time away from pit 290 190 240 240 41 17%
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Intensity and precision
As can be seen from the results, both the average dragline task time and precision have not changed, however the time away from the pits have deteriorated to less than a quarter* from what it was, and it is 3 times less precise
Strip 1
Strip 2
Pit A Pit CPit B
Dragline 1 works Pit A Strip 1, Pit C Strip 1 then Pit B Strip 2 Dragline 2 works Pit B Strip 1, Pit A Strip 2 then Pit C strip 2, and so on
Previous Pits Pit A Pit B Pit C Average St. Dev. Precision (CoV)Time in pit 70 170 120 120 41 34%
Time away from pit 290 190 240 240 41 17%New Pits Pit A Pit B Pit C Average St. Dev. Precision (CoV)
Time in pit 70 170 120 120 41 34%Time away from pit 100 20 50 57 33 58%
* TSV Mining’s Time Away Theory demonstrated that doubling production out of an area will deteriorate time away by two thirds, however that is for a best case scenario
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Inventory and precision Inventory in a system allows for greater precision in process
demand. It does this by allowing for tasks to be moved If all tasks are on a critical path, the process is stuck with having to
accept tasks in a particular order
Let’s look at a process that has to look after 3 pits A, B and C The task size has been determined by a random number generator
between 1 and 10 As the process in all pits are on a critical path, they have to be taken
in order – whichever month they fall in they must have the task done that month
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Inventory and precision
The precision is 40%. There are plenty of peak demands and quiet months Lets now give Pit A a 2 month inventory, allowing us to move tasks within a
2 month window
Pit A Pit B Pit C Total ProcessMonth Fixed Fixed Fixed Demand
1 5 7 1 132 1 3 1 53 9 3 4 164 9 5 8 225 1 2 3 66 10 5 4 197 9 10 8 278 5 6 5 169 9 2 8 1910 6 5 1 1211 7 8 2 1712 1 2 8 11
15.36.1
40%
AverageStandard Deviation
Coefficient of Variation
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Inventory and precision
The inventory in Pit A has allowed us to smooth the demand to a closer mean. The precision is now down to 14%, allowing us to better utilise our process and reduce peak demands
Pit A Pit B Pit C Total ProcessMonth Movable Fixed Fixed Demand
1 5 7 1 132 9 3 1 133 9 3 4 164 1 5 8 145 10 2 3 156 9 5 4 187 1 10 8 198 5 6 5 169 6 2 8 1610 9 5 1 1511 7 8 2 1712 1 2 8 11
15.32.214%
AverageStandard Deviation
Coefficient of Variation
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Where mining operations can go wrong Analysing the capacity requirements based on previous experience
at other operations Only planning, monitoring and reconciling overall accuracy of a
process, but not the precision Taking a business improvement solution from one operation to
another, and expecting the same results Using capacity requirements to calculate inventory requirements
(inventory requirements will be driven by precision) Increasing intensity in an area without understanding the affect it
has on precision Cutting the capacity of a process based on overall requirements Using past successes in improving precision and expecting the
value to continue with more improvements in the area Not understanding how changing a policy will effect precision and
accuracy
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In conclusion Variation is one of the largest value destroyers in any operation,
especially in a highly variable environment such as mining Expectations that are based on previous experience and success
using a particular method can be damaging when used in a different environment. Some examples can be to take a successful project from one operation to another, or to take a successful process from one industry to another
Understanding what variation is to be addressed, and how the system will react to the changes, will determine what process improvement methodology will be best suited
Improvements in an area will have deteriorating value – always check that things have not deteriorated to a point where the value can no longer justify the effort
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Always customised – no two sites are the same
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