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The Mining Cost Cutting Cycle and Ways to Avoid the Traps

Summary

TSV Mining has often witnessed thatnot all cost cutting methods transfer well from site to site, and

some may have hidden traps that become detrimental to the longer term sustainability of a mine.

Background

As the mining industry tightens its belt due to costs exceeding cooling prices for many operations,

the cost cutting machine has kicked in yet again. Having been through this same Groundhog Day a

few times and seen the transitions from the overspending to the cost cutting phase of the cycle, we

have noticed several traps that mining operations can get caught by.

Working with clients whilst they were expanding during the boom time, we were able to

demonstrate the non-linear relationship between throughput expansions and costs. Contrary to

expectations of economies of scale, costs were actually going up exponentially with the increase in

production, not linearly. There were a number of well-publicised reasons for this, such as:

A requirement to move into high stripping ratio areas that could be avoided at lower levels

of production

Additional major capital purchases including processing plant upgrades and expansions

during a period of elevated capital costs

Higher freight costs as long term contracts were reset and where further production

increases required substantial below rail capital investment due to capacity constraints

being approached

Increasing supplier costs with the higher demand

However, there were also some less than obvious causes for the exponential increase in costs. Mine

plans that were being produced were becoming more and more aggressive as companies tried to

take maximum advantage of the boom. Creating a plan on paper was seen as the value that could be

achieved, disconnected from the real value in the pit. Operations were being stretched to a higher

intensity than had been seen before. On paper these plans looked to be quite achievable compared

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to previous productivity levels, but when implemented, they tended to perform poorly.

When we were part of expansion projects, a difficult element was making sure the plans were

actually achievable. The difficulty was not in getting the plans to work per se, but rather convincing

the business that a lot of the methods used to increase production were merelyincreasing the risk of

the plan, and such risks would have negative consequences well beyond the value being chased on

paper.

We once calculated the odds of a more bullish productivity assumption being used for a medium

term plan: the change in assumption would allow a saving of approximately $10 million in order to

curb the rising expansion costs through a reduction in contractor stripping requirements. The odds

of a successful outcome were very low; less than 5% based on statistical analysis of previous

stripping output. We demonstrated that the downside of the attempted saving if the contractor

stripping was not able to be replaced by improved owner-operator performance would be a loss in

revenue in the order of $50 million that year (and site policies made it nearly impossible to prevent).

So there was a $50 million bet being made in order to achieve a $10 million windfall – in gambler’s

parlance it was a bet paying $1.20 the win, with less than 5% chance of paying off!

Another major cause of unmet planned targets was the implementation of new policies. Some of

these policies were required to ensure the sustainability of the operation; however many were

implemented for an isolated process without any analysis on the impact it was having on the overall

production system. The cost to the operation from the policies was not measured or understood.

The price and the demand for resources still remains strong historically, leading to companies

maintaining high levels of production, but needing to combat the unprecedented jump in costs.

Capital investment for a lot of mining operations has already been spent, and there still remains a

requirement to maximise the financial return on these.

5 Hidden Cost Cutting Traps

Some organisations are seeing the obvious benefits of cost cutting; it is hard to argue against cuts

such as renegotiating contracts with contractors and suppliers that were agreed during a “hotter”

market. Reducing overheads in areas that don’t add any value to the actual production chain is also

a clear winning move in the current environment. Improvements that clearly demonstrate increased

production out of equipment that allows for reduced contractor input or the ability to shut excessive

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capacity is something that will pay off in any part of the cycle. However, there are always some

things to watch out for when looking for other cost cutting targets.

1. Watch out for the perceived benefits on paper

A short term cut can lead to long term devastation. As in the example of chasing the $10 million

saving earlier, there can be a large downside if it is not fully thought through and done properly. A

lack of understanding in risk or consequence contained in the mine plancan cause a false sense of

benefits being achieved. In our experience we have found the usual static, deterministic plans that

we are all use to, to be incapable of giving any information onrisk or consequences. Even the token

effort of completing a tornado plot at the end of the planning process really adds nothing to the

understanding because it is still based on a static plan, it doesn’t account for the interactions that

occur between production processes or show how the variability contained in the system effects

throughput.

Pressure on performance does not have to come from the mine plan directly; aspirational targets

can still be used as an operational incentive without the need to generate overly bullish numbers in

the base mine plan. Keeping the plan as close to reality as possible will ensure that all processes are

likely to be resourced “correctly” and sets a clear baseline.

If risk is to be increased in the plan, take risks that have high reward with minimal downside and

make allowance for that risk within the plan through discounting.

2. Are site inventories being reduced, making the site more vulnerable?

Just about all processes at an operation will have some form of inventory. In the production area

there can be:

Topsoil cleared

Drilled inventory

Blasted inventory (both waste and broken stocks)

Prestrip inventory

Coal/ore uncovered inventory

Pre-processing (raw coal/ore) inventory

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Product/concentrate inventory at site and in the logistics chain

If there is an ability to reduce these inventories (and yes, a consistent increase in performance of a

process which is stable both statistically and relative to plan, can allow for significant reduction in

these), this can be a successful cost reduction method through savings in working capital. However,

reducing inventories to reduce costs without addressing variation can lead to unwanted

consequences.

Inventories protect the system throughput and are critical in handling normal variability in output as

well as negative unplanned events (and all unplanned events are negative, when was the last

unplanned event in your production system that was a boost to the for throughput?). Events tend to

be unplanned as individuallythey are infrequent; however the likelihood of one or more of these

events occurring is almost certain in a year. Inventories enable an operation to handle these events

and sustain throughput. Any cost saving measure through reducing inventories should have a very

good handle on the variability of different processes and the potential for negative events to impact

the plan such as wet weather, major equipment failures, geotechnical incidents, or an error in

design.

Any operation will incur negativeunplanned eventsat least once a year. It is not economically viable

to significantly lower the chance of these events occurring.

3. Some changes will happen gradually and may be missed

We’ve all heard the boiling frog analogy; if you put a frog in boiling water it will leap out right away

to escape danger but if you gradually heat the water from a cool temperature, the frog will not

notice until it is too late.

The open cut mining environment tends not to detect change well because the production cycle is

much longer term compared to the manufacturing industry, resulting in gradual change that is not

easily noticeable. If the main key performance indicator tracked is final throughput, and only

minimal focus is put on leading indicators such as inventories (and not just final product inventory),

then there may be no warning that a process is about to become a constraint for the

system.Inventories will always go up and down as they smooth out the inevitable fluctuations in the

system; it is what they are there for. What they should not do is continue downwards, unplanned,

for an extended period of time. When this occurs, a new process will eventually start to hold up the

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system. At first the occasional hold up will occur, gradually increasing until it becomes a constant

issue. If inventories have decreased in a number of processes, the situation may become even worse

with the constraint floating across the system, making it even harder to find where best to apply

limited resources to remove the constraint and lift overall throughput and a production system that

is impossible to get under control because of its unpredictability.

Changes to constraints will not happen overnight. Observing unplanned, consistently dropping

inventory is a lead indicator that the current production is unsustainable with the current application

of resources.

4. Processes in the operation previously considered minor and therefore unscheduled have

become more crucial and are very sensitive to resource changes

With the higher intensity being included in mine plans, potential issues can be missed. One major

issue we have seen at mining operations (and it is becoming more and more common) is that some

processes are no longer needing to be resourced based on the size of the combined tasks over time,

but rather the time that will be available for the process to be performed for each individual task. As

production increases in the same footprint, the time available for supporting tasks such as drill prep,

drilling, blasting, etc actually reduces exponentially, not linearly (if you don’t believe this, picture a

single digging unit that moves across four dig areas; now consider two diggers over the same four

dig areas and compare the amount of time that a digger is away from an area to allow time for

support activities: production doubles but time for support activities reduces by two-thirds, not by

half). This can cause untracked and unscheduled processes to become constraints with very little

warning. It is only obvious when the schedule is run with production targets aligned with actual

performance and with all processes that consume time in the operation being scheduled, no matter

how minor they might have previously seemed. These supporting tasks may need substantial sprint

capacity (anathema to the cost cutting drive) if they are to complete tasks within the time available

and maintain plan stability.

Supporting taskscan stop throughput just as easily as major production tasks, however it is relatively

cheap to prevent. At most operations they can actually be responsible for some of the lowest

marginal cost throughput on site.

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5. Using utilisation to understand resource requirements can be dangerous

There may be a poor relationship between process utilisation and resourcing requirements.

Utilisation works well as a short term indicator when working on a task; however it can be a poor

method for calculating long term resourcing requirements. This is the case when tasks need to be

completed in a narrow period of time and there is standby time between these task windows.

Generally this is the case for most minor tasks on site, such as small excavation tasks, drilling

preparation, drilling, grade control, blasting and pumping.

A simple method of demonstrating the potential size of delays when long term demand is used

rather than focusing on short term peak loads is to use Queueing Theory, first developed by Agner

Erlang.

Let’s use an example of a blast crew:

As other processes release jobs they become flagged for the blast crew to complete. When

resourcing a blast crew based on the overall tonnages they are required to complete over a

sustained period (such as a budget year), the assumption is made that they can level the tasks – ie.

as long as their capacity is higher than the overall requirements, then the blast crew should not hold

up any process.

If we assume that on average two blasts a week are released with an average 400t per blast, and the

blast crew has a loading capacity of 1,000t per week including two tie and fires a week. What will

this look like?

Case A: D/D/1 (deterministic task arrival / deterministic task service / 1 blast crew)

Average blasting requirement = 400t

Average blasting task released = 2 per week (or arrival rate of 0.286 blasts per day)

Blast crew capacity = 1,000t / week (or a service rate of 0.357 blasts per day)

Blast crew capacity utilisation = 80%

Average time released tasks wait for blast crew

But we know that tasks will not be released at equal intervals each week. Let’s give the release an

exponential distribution around the arrival of these tasks:

Case B: M/D/1 (random task arrival / deterministic task service / 1 blast crew)

Average blasting requirement = 400t

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Average blasting task released = 2 per week (or arrival rate of 0.286 per day, on average but random)

Blast crew capacity = 1,000t / week (or a service rate of 0.357 per day)

Blast crew capacity utilisation = 80%

Average time released tasks wait for blast crew

So blasting tasks on average are now waiting 5.6 days on average before the blast crew will get

around to them.

Next, let’s give the blasting process a distribution. Both the blast crews and the size of the tasks will

have a distribution, for simplicity we will use a normal distribution around the service rate of the

blast crew.

Case C: M/G/1 (random task arrival / random task service / 1 blast crew)

Average blasting requirement = 400t

Average blasting task released = 2 per week (or arrival rate of 0.286 per day, on average but random)

Blast crew capacity = 1,000t / week (or a service rate of 0.357 per day, on average but random)

Blast crew capacity utilisation = 80%

Average time released tasks wait for blast crew

NOTE: Though we have used an exponential distribution for arrival time and normal distribution for

the blast crew/task size – this is just a simple example to demonstrate that utilisation of a process is

not always right. Distributions would differ site to site.

So something like the blast crew can be sensitive to an operation, particularly one working on a time

size requirement, as a blast crew at 80% capacity utilisation has jobs waiting 11.2 days on average to

be processed. For a high intensity operation that is trying to achieve quick strip/block turnaround,

this could cause a significant loss in throughput.

Say if this blast crew was cut back due to high costs, and the expectation was to get 90% utilisation

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from the remaining crew. In this case using the same calculations as previous, the average wait time

for a blast will end up being 28.4 days, not much of a saving, but a potential large loss in throughput

and revenue.

The blast delay here is just an average. During peak blast demand times the wait could be

substantially higher. It is easy to see how these delays could affect throughput if the blast window is

narrow, severely affecting downstream processes such as stripping. In order to correctly resource all

processes in the operation, all time consuming activities must be scheduled dynamically.

5 Sustainable Solutions when Cost Cutting

There are numerous methods to cutting costs that are sustainable, such as:

Renegotiating contract rates to a lower price

Removing non-value adding activities and resources

Delaying expansion projects

Increasing process productivity

However we thought we would give some different solutions rather the more traditional ones:

1. Check policies and improvements for individual processes and see if they truly benefit the

system

Localised improvements for a process can quite often be to the detriment of the system. If the value

is only measured at the process without analysing upstream and downstream processes, it may not

be aligned with the revenue of the operation. Operating standards/requirements also tend to

accumulate over time.

An example of this is a localised improvement that reduces the number of trucks needed to haul coal

by breaking up the long hauls into shorter segments, thereby reducing the peak loads of haulage.

This could result in a saving of $5 million per year. However, undertaking a system-wide approach to

improvements would reveal that more coal rehandling increases fines generation, creating an

overload in the fines circuit of the CHPP and dropping the coal yield from these areas by 8%,

reducing revenue by $17 million per year. In addition to the loss in coal produced, there could be

other downstream issues, such as reducing handleability in the logistics chain or client concerns with

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the increased fines.

All process improvements and policy changes should undertake a system-wide analysis to ensure

that all unintended consequences are found and examined.

2. Look at opportunities to reduce the variation in the processes

At TSV Mining we analyse six main sources of variation that are typically found within the

commodity value chain of the open cut mining environment. Reducing variation in the system is

always possible. Interrelationships between variation sources have exponential effects on any

operation. Some reduction can be relatively cheap and provides for the sustainable cost reduction in

many areas through a reduction in the required sprint capacity or the inventory.

Some examples of reduction in system variation include:

Normalising the length of stripping circuits by planning hauls and dump locations more

thoroughly, ensuring that the operation is hauling “long dirt” to the short dumps and “short

dirt” to the long dumps

Improving grade control practices

Upskilling of frontline supervision to a consistent standard

Improving water management capacity to reduce the effects of future weather events

Balancing block sizes so that coal/ore is released on a more consistent basis

Improving quality control in the processing area so that penalty components are closer to

the customer specification while still not getting penalised

Understanding the sources of variation and how the systems reacts to changing variation is one of

the largest opportunities to reducing costs in most operations.

3. Look at getting the right inventories in the right locations

There is a huge amount of working capital tied up in inventories. Getting inventory levels and

locations right ensures the system throughput, but getting them wrong is simply tying up working

capital or reducing the potential throughput of the operation. Often when working with clients we

find that risk mitigation strategies have inventories in low value locations (usually because these are

the areas where inventory can be held more easily, regardless of whether it adds value), when

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alternative locations can reduce costs significantly and improve the risk mitigation.

It is not just overall quantities of inventories that need to be monitored; the right quantities must be

in theright locationsin order to be cost effective.

4. Customise the cost reduction for each operation

Anchoring is a cognitive bias that affects us all, where we tend to overly focus on what has worked

previously, both in our personal experience and what we have seen work for others. However, no

two operations are the same, and taking a solution from one operation to another may have a

detrimental effect if it isn’t considered whether the reasons for the solution working elsewhere are

still applicable in the new environment.

Operations can have a sustainable cut in costs in the right areas. But assuming that all sites are the

same, and using the same technique across the board can be fraught with danger and actually cause

costs to increase in certain situations.

Process improvement resources are always in scarce supply, no more so than in the current

environment. Therefore it is crucial that a “boil the ocean” approach is not taken. Time should be

taken to identify the constraining process that is holding back overall throughput and then

commitment made to not make cuts in this area. All process improvement resources should then be

targeted at the one constraining process with a laser-like focus.

Cost-cutting can then be applied in other areas where there is excessive capacity (be sure to

understand what excessive is), with a scalpel, not a broad brush. However, each cut should consider

the effect on the rest of the operation. Decisions should be made considering the operation as a

complete system, not as a group of siloed processes.

Targeting areas to cut costs where they will not impact throughput is of high importance, and may

not be where you would like to cut. We have seen damaging cuts made to the productive capacity of

a process that was the main capacity constraint of an operation just because 50% of the site’s costs

were in that process.

Always customise the cost cutting for each operation as small differences between two operations

can cause big changes in where the focal areas need to be to improve throughput and reduce costs.

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5. Look at incremental, not overall process costs

When there are multiple processes required to move material, with cost analysis changing the

process from one to the next, the costs need to be analysed on anincremental basis. Whether it is

moving from dozer push to excavator, increasing cast, choosing the dragline horizon with prestrip, or

adding equipment to an excavator circuit, overall process costs will not give a clear picture.

As with most things discussed here, incremental costs are not linear; they again go up exponentially

as productivity deteriorates exponentially: raising the dragline horizon will go through larger step

changes in rehandle (increasing rehandle exponentially), dozer push productivity will decrease with

longer and steeper uphill pushes (productivity deteriorating exponentially), the benefits from

increasing powder factor to achieve a higher cast or improve diggability will decay exponentially,

adding more trucks to a circuit will have a exponentially decaying benefit in additional throughput.

Overall process costs hide exponential cost increases.

Bringing it all together

Short term cost reduction benefits can have many hidden traps. There are many aspects of an

operation where sustainable cost reduction can be undertaken. In this paper there are several

underlying themes:

Always look at how the system is reacting to change, not just the individual processes

Inventories will hide potential throughput issues for a period of time.

Any inventory contained within the system that constantly deteriorates is a lead indicator

that current production levels with current application of resources is unsustainable

Most elements in mining do not change in a linear fashion, they tend to change

exponentially

Traditional methods of measuring resource requirements may no longer work

Keeping these themes in mind when looking for opportunities to cut costs and increase throughput

will have you well-armed in avoiding many of the traps and pitfalls we have mentioned here.

TSV Mining has many years of applying systems thinking principles to the open cut mining industry

using simulation modelling and statistical analysis, amongst other techniques. If you would like to

discuss how these or dozens of other methods could be applied at your site to improve cash flow,

please contact cbraund@tsvmining.com.auor visit our website at www.tsvmining.com.au

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