Finding cost effi ciencies in mining operations through effective value driver modellingAaron Carter, Brian Gillespie and Chris GilbertPerformance Improvement Group, Brisbane

February 2009

© 2009 PricewaterhouseCoopers. All rights reserved. “PricewaterhouseCoopers” refers to PricewaterhouseCoopers, a partnership formed in Australia or, as the context requires, the PricewaterhouseCoopers global network or other member fi rms of the network, each of which is a separate and independent legal entity.

Understanding the complex linkages between operational variables at a mine site and the fi nancial performance of that mine is now more critical than ever as operators deal with the slump in commodity prices.

Even before the downturn, many of Australia’s leading mining companies had started to implement a more structured approach to cost effective decision making across all areas of mine production.

This paper highlights Australian mining best practice in both operations cost management and production value maximisation through robust modelling of operational value drivers.

Introduction

Finding cost effi ciencies in mining operations through effective value-driver modelling2

For the fi ve years until mid 2008, most major mining companies in Australia emphasised cost effectiveness over cost effi ciency, particularly in the areas of maintenance and transportation. Mining the largest possible quantities of minerals as quickly as possible has been more important than minimising the cost of key maintenance or production activities due to the high prices available. Operational effi ciency had in effect been compromised to varying degrees in the quest for production volume to take advantage of high prices.

Large capital projects have the potential to destroy substantial shareholder value during extended periods of low prices. Anticipating an extended price slump, almost all of the major mining companies around the world took a critical look at their major capital expenditure plans towards the end of 2008, clearly demonstrated by a signifi cant lessening of lead times for many categories of major capital items. As commodity prices continue to slump and demand scales back from the growth markets of India and China, many mining companies have already deferred specifi c major development projects and many more have announced non specifi c scale back of aggregate capex projects over the next fi ve years (Aeppel 2008).

However, prior to the credit crunch, and probably as early as January 2008, some of the more forward thinking Australian mining companies were already preparing for an expected change in market conditions. At the mine site level, this involved a change in emphasis towards maximising the profi tability per tonne of product with an increasing focus on reducing costs rather than just maximising the total tonnage mined and shipped at any cost. This renewed focus on achieving acceptable return on investment per individual mining asset has now taken hold across the sector as mining companies once again begin to take a closer look at the cost of capital items and their operational and maintenance practices.

A. Return to cost effi ciency

Source: The Australian Financial Review and PricewaterhouseCoopers

Crushers

Grinding mills

0 5 10 15 20 25 30 35 40 45 50

Average delivery time Jan 2009

Average delivery time Jan 2008

Tyres

Wagons

Locomotives

Draglines

Power generators

Figure 1: Buying power is moving back to mining companies as demonstrated by the signifi cant decreases in asset lead times over the past 12 months

Finding cost effi ciencies in mining operations through effective value-driver modelling 3

Understanding how operational levers drive the fi nancial performance of an individual mine is the key to cost effi ciency and value optimisation. There are a number of barriers that will typically compromise the many different types of projects that in some way are geared towards improving cost effi ciency. Very often the fi rst symptoms of project failure occur when target operational metrics have been achieved with smaller than expected fi nancial improvement. Typically this situation will arise when the relationships between operational metrics and fi nancial results are not suffi ciently understood. The primary barriers to fi nding cost effi ciencies are that:

1. There is little understanding of the relationship between operational metrics and fi nancial results

2. There is a lack of tools available to assist this understanding

3. Operating performance and fi nancial results are not suffi ciently disaggregated

4. There is a lack of accountability for fi nancial performance below senior management level.

These barriers will be discussed further in the context of the Australian mining industry.

Little understanding of the relationship between operational metrics and fi nancial results

It is now almost 60 years since mining companies started using the same accounting principles that were used to measure the fi nancial performance of the company to help measure the performance of equipment and operational processes (Hoyt 1950). However, understandably, few mining companies expect their trained engineers to be able to apply cost accounting principles to each of the possibly hundreds of operational decisions they make every week.

This lack of understanding can take a number of forms. At the most basic level, process controllers with minimal understanding of component costs may be given the task of optimising a part of the production process. Even when the cost components of the production process are well understood over a certain range of variability, a lack of understanding of the various inter-relationships between cost components will usually lead to simplistic assumptions about the drivers of value. Most diffi cult of all is that each mine site is unique in its combination of factors such as plant layout, mineral ore body and proximity to rail, road or port. Even if the cost drivers of a particular process

have been determined for one mine site, the cost drivers for the same process may vary considerably at another. Even the simplest mine operations will have unique aspects of their operation that must be taken into account when estimating costs.

Lack of tools

Another barrier to fi nding cost effi ciencies is that the mining industry traditionally has underinvested in tools to quickly and reliably assess the fi nancial impact of potential mine improvement ideas. Although many mines have mine planning, process optimisation and fi nancial modelling tools, they tend to be non integrated or receive limited data feeds from each other and are therefore used in isolation for scenario modelling purposes. Often the sheer quantity of operational data and the linkages between data contained in these stand-alone tools can give a false sense of reliance on the output even when the hierarchy of operational effect and fi nancial result is incomplete.

Figure 2: An integrated approach to operational modelling links all key aspects of a mine

B. Barriers to fi nding cost effi ciencies

Geology

Financial

MiningOperations

Supply

RefiningOperations

Maintenance& Availability

Finding cost effi ciencies in mining operations through effective value-driver modelling4

Many major mining companies have also attempted to use data provided by their fi nancial reporting system for mine planning purposes and the large Enterprise Resource Planning (ERP) vendors now all offer mine planning modules to help integrate fi nancial information at the mine site. The advantage of major ERP systems can be the depth of the fi nancial data and the ability to provide simultaneous multi-user capability. Many mining companies are using their historical fi nancial data from an ERP system to assist annual budgeting and annual production planning. However the standardisation required from fi nancial reporting systems is a signifi cant limitation when it comes to modelling the uniqueness of the operational set up at a mine within the ERP system.

This creates a problem for mining companies seeking to prioritise cost saving initiatives from a portfolio of possible projects. Many opportunities are not explored properly and are accepted or rejected on the basis of weak logic based fi nancial modelling. Inevitably many such cost reduction initiatives fail to deliver the expected fi nancial results due to the impact of other parts of the mining operation not being adequately taken into account. Even more worrying is that some value optimisation activities are never considered due to the time and effort required to evaluate them properly.

Reported operating performance is not suffi ciently disaggregated

In some mining companies, another impediment to suffi ciently understanding costs is that fi nancial performance is often only reported at a consolidated level, or at a lower level based on the cost hierarchy and cost elements as defi ned in the chart of accounts. In many instances, the cost hierarchy does not suffi ciently disaggregate costs in a manner capable of accurate cost reporting across operational processes, ie by activity. Even if there is a close alignment between operations and cost centre reporting, this reporting often provides little consideration to the level of value created from incurring these costs.

In addition to poor disaggregation of fi nancial information, often operational drivers and fi nancial outcomes will not be included within the same reporting framework, or if they are, the linkages will not be clear. Without appropriate reporting of both fi nancial and operational information, it can be diffi cult to understand why performance has been tracked in a particular manner. In many cases, operators and superintendents do not have the ability to report which operational metrics drove throughput and fi nancial performance. Without obtaining a full and accurate understanding of operational performance and the resulting fi nancial implications from lower level staff, it is very diffi cult for senior management to effect cost reduction initiatives within an operation with any degree of certainty.

Lack of accountability for performance

The fi nal impediment to achieving cost effi ciency is a lack of appropriate accountability for those operational metrics that have the greatest impact on fi nancial outcomes. A lack of visibility of the linkages between operational metrics and a signifi cant negative or positive fi nancial variance means that an operator will not be required to provide an explanation of the variance appropriate to the level of fi nancial impact. Accordingly, the operational staff that may have the ability to heavily infl uence fi nancial outcomes through the way they conduct their day-to-day operations, are not being measured in a manner likely to change behaviour to improve fi nancial performance.

Conversely, senior personnel that are being measured against fi nancial outcomes may have little infl uence on (or suffi cient understanding of) how their operational staff can assist them to improve performance. If the inter-relationships and linkages between lower level operational metrics and higher level fi nancial indicators were clearer, then the organisation would have a better chance of developing a set of useful metrics for all staff to reward successful fi nancial performance.

Finding cost effi ciencies in mining operations through effective value-driver modelling 5

C. Linking operations and fi nance

Extracting minerals from the ground and then selling those minerals in a global market may be a simple business model, but the cost components of that business are huge, complex and inter-related. Additional value adding processes such as even basic refi ning further confuse the components of cost.

Many mining engineers still believe that the process of extracting minerals from the ground is straightforward and that the fundamental quality of the mine determines that mine’s position on the cost curve.

This assumption fails to understand the scope for optimisation in even the most basic of mine operations. For example, even fi nancially aware senior operations staff will struggle to optimise single large basic cost components such as the effective manpower cost of a changing shift pattern. How then can they be expected to minimise the combined production unit cost of hundreds of equipment assets over an extended time period in a dynamic production environment?

Mining companies must have a solid understanding of the operational levers that drive fi nancial performance if they want to be able to quickly and cost effectively confi gure for required production. Building an accurate operational model where all components of that model link to the predicted production cost is the most straightforward way to combine operations and fi nance.

The most useful operational models are those that replicate the full structure of operations and process logic at a mine site, or extended operation. The best models provide a cascading top down view of operations, linking high-level fi nancial outputs to the key operational drivers of those outputs such as production performance metrics and the disaggregated operating costs of each major process or asset.

Figure 3: High-level value driver logic for development activities at an underground coal operation

Development($/metre)

Development ($)

Shuttle Cars ($)

Breaker Feeders ($)

Labour ($)

+

Development (metres)

Production Speed(metres/hr)

Calendar Hours (hours)Unscheduled Time

(hours)

Idle Time (hours)

+

+

+

x

x

x

/

Calendar Time (hours)

-

-

Production Time (hours)

Scheduled Time (hours)

/

Financial

Operational

Calendar Time (hours)

General Consumables ($)

+

Electrical Parts ($)

Calendar Availability (%)

Scheduled Time (hours)

Mechanical Parts ($)

Other ($)

Utilisation (%)

Continuous Miners ($)

+

+

+

Finding cost effi ciencies in mining operations through effective value-driver modelling6

These tools are primarily implemented to provide an accurate and reliable insight into the key elements of value creation at the mine being modelled. They are used for predictive modelling, sensitivity analysis and variance reporting purposes. Over the longer term, they will start to educate, then infl uence management thinking and encourage a sharp focus on the key metrics that have the biggest impact on the performance of the mine.

The power of predictive models

Predictive value driver models are focused on evaluating the impact that alternative operating scenarios will have on performance and modelling the core value drivers of a mining operation. They show how changes in capacity, leverage points and process inputs can infl uence operational and fi nancial results. The design of a predictive value driver model must allow the model, when populated, to replicate the true factors underpinning the economics of a mine. Production constraints, mine geology, mine planning data, and the operating performance and maintenance constraints of key assets are combined with precise fi nancial data to create a model capable of mirroring mine performance. They differ to traditional mine planning, scheduling and optimisation tools due to the emphasis placed on the fi nancial implications of different operational scenarios.

Predictive value driver models can be used to assess the likely benefi t of proposed operational improvement and cost reduction opportunities, or predict the level of

return that capital expenditure will yield through improving operating performance. Proposed cost and productivity improvements are entered into the model for comparison against a baseline operating scenario. Based on these inputs, the value driver model can calculate expected operating performance under different scenarios, and can highlight the source of key performance variances.

Predictive value driver models can become a valuable tool for the quick evaluation and prioritisation of improvement opportunities. For example, questions that are often tested through a predictive value driver model include:

• Which capital investment options will have the biggest impact on operational performance?

• How will improving the reliability and availability of key plant items impact the performance of the mine?

• What operational improvement initiatives will have the biggest fi nancial impact?

Another common use for predictive value driver models is to conduct comprehensive sensitivity analysis. The sensitivity of fi nancial performance and mine production volumes to each driver in the model is calculated and prioritised to highlight those elements of the mine that create and destroy value.

This knowledge empowers management and staff to focus their time and resources on ‘where the money is’ to improve the performance of their mining operation.

Figure 4: Example sensitivity analysis highlighting the key operational drivers of fi nancial performance

-1.00% +1.00% -0.50% +0.50% 0%

% change to EBIT

EBIT impact of + 5% change in operational value driver

EBIT impact of -5% change in operational value driver

Value Drivers

Longwall Idle Time

Longwall Operating Delays

Conveyor Maintenance Delays

Longwall Change - Out Time

Development Idle Time

Development Unit Cut Rate

CPP Unscheduled Time

Finding cost effi ciencies in mining operations through effective value-driver modelling 7

Jointly reporting fi nance and operations

Value driver models can also be used to report a combination of historical operational and resulting fi nancial performance data covering all aspects of a mining operation. The key point of difference, from conventional reporting mechanisms, is that the value driver model can be used to present operating performance in a logical cascading model structure, disaggregating high-level reported fi nancial performance into the lower level operational elements driving that performance.

Reporting in this way can enhance the level of control that management has over operations by providing transparency of the key drivers of monthly results. Managers can understand exactly which elements of the mine have positively and negatively impacted reported results, and the extent of this impact.

Developing an accountability framework

Key measures in the regular reporting pack can be assigned to appropriate personnel to create accountability for performance. Management level personnel, such as superintendents, are typically made accountable for performance metrics higher up on the value driver model, such as plant or major asset availability.

Operators can be held directly accountable for the specifi c metrics particular to their part of the process, such as

operating delays and unplanned maintenance of key assets representing the lower operational levels of the value driver model. A well constructed value driver model can be used as the basis of an accountability framework that can embed key performance metrics across an organisation.

One of Australia’s largest mining companies has implemented such frameworks in a number of its mining and refi nery assets in Western Australia and Queensland, linking value driver models to business intelligence. Senior management meets with plant superintendents on a monthly basis to examine a variance report, which requires input from all key areas of the operation. A negative variance on the model can be tracked to its source operational driver(s).

Personnel accountable for negative variances must provide an explanation and rectifi cation plan for variances below certain tolerances. There are two clear benefi ts to this approach: fi rst, operators and superintendents clearly understand the economic impacts of their operational area, and second, this granular level of visibility can be used to motivate individual operators to improve the priority operational metrics that they control.

Figure 5: Example value driver reporting tool and accountability framework

Mine Development ($)Cost ($) 2,141,219 2,160,346

Variance 19,127 0.9%

Accountability Jason Stubbs

Breaker Feeders ($)Cost ($) 243,202 236,299

Variance (6,903) -2.8%

Accountability Sarah Smith

Continuous Miners ($)Cost ($) 754,201 738,722

Variance (15,479) -2.1%

Accountability Darryl Keating

Shuttle Cars ($)Cost ($) 420,715 487,002

Variance 66,287 15.8%

Accountability Geoff Price

Labour ($)Cost ($) 723,101 698,323

Variance (24,778) -3.4%

Accountability Jason Stubbs

Shuttle Cars ($/ROM t)Cost ($) 1.885 2.174

Variance 0.289 15.3%

Accountability Jason Stubbs

Development Production ($/ROM t)ROM t 223,191 224,012

Variance 821 0.4%

Accountability Jason Stubbs

Mechanical Parts ($/ROM t)Cost ($/ROM t) 0.762 0.754

Variance (0.008) -1.0%

Accountability Mike Stapleton

Electrical Parts ($/ROM t)Cost ($/ROM t) 0.459 0.443

Variance (0.016) -3.5%

Accountability Daneil Cotters

General Consumables ($/ROM t)Cost ($/ROM t) 0.192 0.422

Variance 0.230 119.8%

Accountability David Stanton

Lubricants ($/ROM t)Cost ($/ROM t) 0.218 0.212

Variance (0.006) -2.8%

Accountability David Stanton

Other ($/ROM t)Cost ($/ROM t) 0.254 0.343

Variance 0.089 35.0%

Accountability Geoff Price

+

+

+

x

+

+

+

+

Finding cost effi ciencies in mining operations through effective value-driver modelling8

D. Modelling cost reduction opportunities in turbulent times

In the current market conditions, many companies are undertaking urgent cost reduction programs to counter signifi cant shortfalls in revenue due to price slumps and slackening demand. Mines have already been closed in Queensland and Western Australia where the cost of extracting the reserves signifi cantly exceeds revenue available under the forecasted commodity price.

For many more mines in Australia, there will still be a lag between the drop in the market price available for near term production and the input costs of that production. For some operators, there will be a transition period lasting well into 2009 of considerable reductions in revenue with little drop in input costs under existing contracts. When presented with shrinking or even negative margins, the options of implementing immediate measures such as turning off production, reducing headcount or delaying major capital expenditure must be considered.

Whilst these measures are clearly necessary for some mining operations, for other mines it will be important to understand which levers will have the most impact on cost

reduction during a period of continued low commodity prices. A true cost improvement program for reduced production levels requires sustainable cost reduction over a longer period. This is particularly the case where production levels may be substantially reduced for an extended period requiring a signifi cantly altered cost structure for the operation.

Modelling scenarios of signifi cantly lower production levels than recent levels is not straightforward. Production constraints can change signifi cantly when the requirement for the number of major capital plant items such as power generators, draglines or crushers is reduced in number but give rise to signifi cantly higher asset utilisation. A fl exible value driver model can calculate expected costs under different production level and operating performance scenarios, even when historical cost data is not available for the particular mine capacity confi guration being considered. Predictive value driver models can become signifi cantly more valuable than mine planning tools using ERP cost data in such circumstances.

Finding cost effi ciencies in mining operations through effective value-driver modelling 9

E. Conclusion

Understanding the complex linkages between operational variables and the fi nancial performance of a mine site is now more critical than ever as operators deal with the slump in commodity prices. This paper has sought to highlight the importance of fi nding greater cost effi ciencies by modelling the operational drivers of fi nancial performance.

There are currently four barriers to fi nding greater cost effi ciencies through use of such initiatives.

1. Little understanding of the relationships between operational metrics and fi nancial results.

2. Lack of tools available.

3. Operating performance and fi nancial results are not suffi ciently disaggregated.

4. Lack of accountability for fi nancial performance below senior management level.

Several leading Australian mining companies have implemented value driver models linking operations and fi nance. Value driver models provide mining companies with four signifi cant capabilities:

1. An understanding of the operational levers that drive fi nancial performance.

2. The ability to jointly report on fi nancial results and the operational drivers of those results.

3. The ability to identify and prioritise cost reduction opportunities.

4. An accountability framework to drive fi nancial performance.

Acknowledgements

This paper has been developed following insights gained by PricewaterhouseCoopers while working on operational improvement projects with Anglo Coal Australia, BHP Billiton, Newcrest, Rio Tinto and Xstrata Coal.

Our special thanks to Xstrata Coal, Newcrest and BHP Billiton who recently engaged PricewaterhouseCoopers to work with them to develop value driver models at mine sites and refi neries in the Australian states of Queensland, New South Wales and Western Australia.

References

Aeppel, Timothy, December 2008. “Miners Cut Spending in Half” Wall Street Journal Vol. 252

Charlton, S, May 2007. “Mining sector has to formalise processes and systems to improve productivity” Mining Weekly Vol. 142

Fordham, P, Jan 2004. “Mining Company Performance Improvement Programs and Results — Summary of Benchmarking Study” Plant Operators Forum 2004, Colorado

Hoyt, Charles D, September 1950. “Time Studies and Cost Accounting increase effi ciency at Titania” Mining Engineering Vol. 187

PricewaterhouseCoopers, 2008. “Aussie Mine* Reaping the rewards. A review of trends in the Australian mid-tier mining industry” Global Energy, Utilities and Mining

PricewaterhouseCoopers, 2008. “Global Mine* Bulletin — May 2008: Cascading KPIs” Global Energy Utilities and Mining

PricewaterhouseCoopers, 2008. “Mine* as good as it gets? Review of global trends in the mining industry” Global Energy, Utilities and Mining

Brian Gillespie PartnerPerformance Improvement BrisbaneT: +61 7 3257 5656E: brian.gillespie@au.pwc.com

Brian is a Partner with the Performance Improvement Group in Brisbane, leading Strategy and Operation Improvement assignments. In recent years, he has worked on large projects with organisations such as Anglo Coal Australia, BHP Mitsubishi Alliance, Rio Tinto, Queensland Resources Council, the Queensland Rail Coal Division, Dalrymple Bay Coal Terminal and Xstrata Coal.

Brian holds the degrees of BSc and MBA and is a Chartered Engineer with the Institute of Engineering and Technology in the UK.

He also sits on the Advisory Board of the Brisbane Graduate School of Management at the Queensland University of Technology and on the National Executive of the Chartered Institute of Logistics and Transport, Australia.

Chris GilbertDirectorPerformance Improvement BrisbaneT: +61 7 3257 8126E: chris.gilbert@au.pwc.com

Chris is a Director with the Performance Improvement Group in Brisbane. He specialises in operational improvement and cost reduction and has led multiple value driver modelling assignments. In recent years Chris has played a lead role on assignments with Anglo Coal, BHP Billiton, Dalrymple Bay Coal Terminal, Queensland Rail Coal, Bulk and General Freight Divisions, Queensland Resources Council, Rio Tinto and Xstrata Coal. He has experience in coal, aluminium (bauxite mining and alumina refi ning), copper and iron ore.

Chris holds a Bachelor of Mechanical Engineering from the University of Queensland and an MBA from the Australian Graduate School of Management, which he completed on exchange at the University of Chicago.

Aaron CarterSenior ConsultantPerformance Improvement BrisbaneT: +61 7 3257 8679 E: aaron.carter@au.pwc.com

Aaron is a Senior Consultant in the Brisbane Performance Improvement Group. He has experience across a number of industries including resources, transport and logistics and utilities, with a specifi c focus on operational modelling, cost and revenue analysis and operational improvement.

Aaron has recently been heavily involved in a number of operational modelling projects, with his experience including a value driver modelling engagement with Xstrata Coal and the development of fi nancial forecasting and retail pricing models for Queensland Rail. He has also recently delivered a project to identify the core drivers of cost and value in the newly formed South East Queensland bulk water sector.

Aaron holds a Bachelor of Accounting and Bachelor of Business (Information Systems) from Central Queensland University where he was awarded the Business and Law Faculty Medal on graduation.

About the authors

Australian Resources TeamResources Industry Leader Michael Happell, MelbourneT: +61 3 8603 6016E: michael.happell@au.pwc.com

New South WalesMarc Upcroft, SydneyT: +61 2 8266 1333E: marc.upcroft@au.pwc.com

QueenslandBrian Gillespie, BrisbaneT: +61 7 3257 5656E: brian.gillespie@au.pwc.com

South AustraliaAndrew Forman, AdelaideT: +61 8 8218 7401E: andrew.forman@au.pwc.com

Western AustraliaMark Bosnich, PerthT: +61 8 9238 3376E: mark.bosnich@au.pwc.com

VictoriaTim Goldsmith, MelbourneT: +61 3 8603 2016E: tim.goldsmith@au.pwc.com

PricewaterhouseCoopers, Riverside Centre, 123 Eagle Street, Brisbane QLD 4000GPO Box 150, Brisbane QLD 4001Australia

Offi ce: +61 7 3257 8995Facsimile: +61 7 3023 0936Website: www.pwc.com.au