optimising extended mining operations through value driver … · 2015-06-03 · 4 optimising...

Optimising extended mining operations through value driver modelling

Fabio Buckeridge Brian GillespieStephen Loadsman

November 2010

The global fi nancial crisis brought cost management back into focus for many mining companies who for the previous few years had been chasing production volume to exploit the booming commodity prices. Over the past eighteen months, many mining companies have made radical changes to their mine plans as they reacted fi rstly to the initial slump in commodity prices in 2008, then almost as quickly had to lift their production targets when the slump in demand failed to materialise and prices started climbing again.

Many mining operators effectively fi x their mine plans and budgets on an annual basis and often there are poor linkages between the mine plan, the budget and the real drivers of cost and value. Such operations struggle to determine the cost and margin impact of signifi cant changes to production particularly when this occurs outside the annual planning process.

However, those mining companies that did understand the cost and value drivers were able to quickly respond to both the sudden price collapse and the recovery. Active management of the drivers of cost across all areas of mine production and processing allowed a few mining operations to increase margins throughout the boom-bust-boom cycle of 2008-2010. This paper seeks to demonstrate that robust modelling of operational cost and value drivers across the extended life of mining operation is a key requirement for maximising value, regardless of the economic cycle.

Introduction

4 Optimising extended mining operations through value driver modelling

The global fi nancial crisis highlighted the need for greater operational cost effi ciency for most mining companies. When commodity prices and demand collapsed in June 2008, many mining companies had to scramble to quickly reduce both production and unit costs following several years of prioritising operations to increase production volume. At many mining companies, operational effi ciency had been compromised in the quest for increased production volume to take advantage of spiralling prices. Most mining cost indices indicate that around the world, mining costs have indeed fallen since the commodity price collapse and have continued to fall while prices and volume demand have recovered.

PwC estimates that operating expenses decreased by 6% from 2008 to 2009 across the global mining industry. However, despite the signifi cant decrease in operating expenses, a step-change reduction in the cost base remains elusive for many. It can be diffi cult to fi nd quick cost reduction opportunities when investment in plant equipment and transport contracts are geared to a relatively narrow range of production volume. For many mining operations, reducing production volume may signifi cantly increase extraction, processing and transport costs per unit of product. The ability to fi nd short term cost savings through immediate production decreases is therefore often dependent on the operational ability to make a step change in equipment and transport utilisation accompanied by the same level of step change in cost. This requires both an appropriate set up at the mine and extended production chain coupled withthe ability of the mining company to properly cost the changes to that production volume.

Many leading mining companies understand that operational cost effi ciency is an area that requires focus over the longer term to maximise lifetime asset effi ciency and embed a culture of cost awareness that will survive throughout boom commodity cycles. One indicator of a strong focus on sustainable cost control is an established allocation of key performance indicators measuring cost rather than volume or margin. BHP Billiton’s philosophy of allocating cost, volume and safety accountability at the mine operations level through a standard set of performance measures focuses attention on long run operational effi ciency independent of prevailing or forecast commodity prices. BHP Billiton aggregates their marketing and sales function across all production and has an independent set of performance indicators geared towards meeting (or exceeding) spot prices. In this way, BHP Billiton maintains a long term focus on cost effective mining operations without any masking of performance by fl uctuations in commodity prices.

A. Return to cost effi ciency

Figure 1: An example of commodity price and mining cost

0

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3,000

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5,000

6,000

7,000

8,000

9,000

10,000

1997

1998

1999

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2001

2002

2003

2004

2005

2006

2007

2008

2009

80

90

100

110

120

130

140Copper Price US$/t (left)

US Mining Cost Index (right)

Optimising extended mining operations through value driver modelling 5

B. Linking Operations and Finance

Finding EBIT maximising opportunities

Most mining companies focus primarily on achieving operational and cost effi ciency improvements at the mine site level. Often the main metric used to quantify the success of any cost effi ciency initiative is to measure the nominal cost saving using the standard costs determined in the previous annual budgeting process. Sometimes it can be many months or even the fi nancial year end before the real fi nancial impact of operational effi ciency initiatives can be measured.

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 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 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. These models are called value driver models and a fuller description of the theory and practice of building a value driver model can be found in the PwC paper published by Carter, Gillespie and Gillbert (2009).

Mine Complex ($)Cost ($) 270,968,699 282,428,715

Variance 11,460,016 4.2%

Accountability Jason Stubbs

Processing Plant ($)Cost ($) 5,245,000 5,263,598

Variance 18,598 0.4%

Accountability Sarah Smith

Underground Mine Cost ($)Cost ($) 25,645,000 25,640,000

Variance (5,000.0) 0.0%

Accountability Darryl Keating

Opencut Mine ($)Cost ($) 237,515,554 248,961,217

Variance 11,445,663 4.8%

Accountability Geoff Price

Rail and Port ($)Cost ($) 2,563,145 2,563,900

Variance 755 0.0%

Accountability Dick Smith

Opencut Cost ($/ROM t)Cost ($/ROM t) 36.54 37.13

Variance 0.59 1.6%


Opencut Production (ROM t)ROM t 6,500,152 6,705,123

Variance 204,971 3.2%


Drill and Blast ($/ROM t)Cost ($/ROM t) 7.05 6.58

Variance (0.47) -6.7%

Accountability Darryl Keating

Truck and Shovel ($/ROM t)Cost ($/ROM t) 22.05 21.50

Variance (0.55) -2.5%


Dragline ($/ROM t)Cost ($/ROM t) 6.58 8.20

Variance 1.62 24.6%

Accountability Sarah Smith

Rehabilitation ($/ROM t)Cost ($/ROM t) 0.50 0.40

Variance (0.10) -20.0%


Other ($/ROM t)Cost ($/ROM t) 0.36 0.45

Variance 0.09 25.0%


+

+

+

x

+

+

+

+

Figure 2: Example of Value Driver Reporting

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.


Flexible value driver models can calculate the expected costs under different production levels and operating performance scenarios.

Value driver models can be used to report a combination of operational and fi nancial performance data covering all aspects of a mining operation. The key difference from more conventional reporting 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. A fl exible value driver model can calculate the expected costs under different production levels and operating performance scenarios. Value driver models can therefore prioritise potential EBIT improvements by considering variation of many different operational confi gurations.

Using value driver models within the budget cycle

Mining organisations typically have proven ‘Life of mine’ planning processes and well defi ned budgeting cycles which use the production life contained within the ‘Life of mine’ plan as a key input. However, the standard costs used in the budgeting process are typically developed once per year as part of the annual budgeting cycle and are usually applicable to a specifi c production plan and therefore valid over a small range in targeted production. Typically there is no fi nancial link between the annual budget and the ‘Life of mine’ plan. Signifi cant changes in target

production usually involve a lengthy process involving several iterations of information exchange between mine management who produce new asset confi gurations and the fi nance team who cost them. For most mining companies, this process has come under increasing pressure since the beginning of the global fi nancial crisis as mining companies seek a quick response to signifi cantly changing economic conditions. However, typically the adjustments to budgets and forecasts or the identifi cation of EBIT improvement initiatives require the budget process to be repeated to ensure all key assumptions are considered and the fi nancial impact of the proposed changes are reliable. Therefore it can take some time (often several weeks) for a fi nancial impact to be estimated.

A properly confi gured value driver model can determine the fi nancial implications of radical ‘what if’ scenarios very quickly, allowing senior management the ability to investigate signifi cant changes in production levels or wider operational confi guration without lengthy iterations between the mine managers and fi nance team. Production scenarios that can take weeks or months to cost can often be accomplished in a matter of hours. PwC have also found it to be the case that a value driver model once implemented, can typically provide the output used as the starting point for the next planning and budgeting cycle.

Flexibility around production targets has come under increasing pressure since the beginning of the global fi nancial crisis as mining companies seek a quick response to signifi cantly changing economic conditions.


C. Optimising mining operations across the production value chain

The challenge of end-to-end supply chain optimisation

For those mining companies that have managed to link operations and fi nance at the mine site level, the next challenge becomes that of extending those linkages across the production value chain. The production value chain is often described as the ‘pit-to-port operation’ but in reality this involves all aspects of the supply chain from the mineral extraction at the mine site through to the point at which the mineral is delivered to the customer.

Only a few mining companies have managed to implement a planning process that optimises operations across the full production chain from the mine site to the customer. Most mining companies focus primarily on achieving operational and cost effi ciency improvements at the mine site level. Often the main metric used to quantify the success of any cost reduction is to measure the nominal cost saving using the standard costs from the previous annual budgeting

process. However, the effects on the extended supply chain are often ignored or crude fi nancial estimates are made by extending the nominal cost saving over other parts of the value chain. Sometimes it can be many months or the end of the fi nancial year before the real fi nancial impact of operational effi ciency programs can be measured.

End-to-end supply chain optimisation is well established in many other industries so it may seem strange that there are few mining companies that have managed to implement robust optimisation planning tools across their full production supply chain. Although the typical mining supply chain looks to have only a few major components, each component has signifi cant operational complexity and has massive potential for variation on both capital items and operational expenses.

Mining companies are dealing with hugely fl uctuating prices for the commodities they produce and also for many of their input costs. This

produces discontinuities in the cost and revenue functions of any supply chain model that tries to deal with step changes in many areas of the production supply chain.

The logical mining supply chain gives no indication of the complexity that can arise within each component of the supply chain. For example, the rail transport process can involve sharing infrastructure with transport providers and other mining companies and also involve complex scheduling which is further complicated if the system is suffering capacity constraints.

Most mining companies prioritise the optimisation of those parts of their supply chain that seem to have the largest operational cost, typically in isolation from the rest of the supply chain. However, optimising the operational effi ciency of just one component of the supply chain may have little or no effect on overall cost effi ciency, particularly if the constraints of the supply chain are to be found elsewhere.

Figure 3: Simplifi ed Mining Supply Chain

OreMineral

ExtractionCrushing Blending

LandTransport

Stock-piling Sea

Transport

Waste Domestic Customers

ExportCustomers


An example of isolated decision making

For example, take the situation where a mine manager makes the decision to slow down development and longwall speed to meet a reduction in production targets. For this particular mine and extended value chain, slowing down development units may have little impact to overall production costs due to such things as fi xed conveyor capacity, a longwall already operating at low utilisation or contracted rail capacity that must be paid for regardless of the tonnage carried.

In this case, although the standard costs in the mine plan and budget indicate substantial cost savings, the reality is that little or no cost saving may occur. In this case, stockpiling excess production may have been a better interim option while major asset reconfi guration options or haulage contracts were investigated. It may even be the case that this decision actually was a negative driver of value due to the lost tonnage combined with zero cost saving. Clearly in this case, the mining company may have benefi tted if the mine manager had been able to quantify the real cost impact of the slowdown in development and longwall speeds.

Building a value driver model across the full production value chain

Understanding the linkages between cost and operational confi guration in each component of the production supply chain are the building blocks of a value driver model that can be applied across the production process. The most useful operational models are those that replicate the full structure of operations and process logic across the extended production and supply chain operation. This is not an easy task and involves comprehensive mapping of operational activities and their cost components as a fi rst stage.

Identify fixed and variable costs

Upload trialbalance

Production Assets Other

Map activitiesfor mine

Operational Financial

Define accountlist for mine

Define/capturesundry drivers

Identify keyassets and

metrics

Capture valuesof assetmetrics

Build the production

schedule basedon life of

mine plans

Build value driver model

Map trial balanceto mine activities

Map variablescosts to drivers

Figure 4 : The value driver model build process

One company that has successfully developed extended value driver models is Xstrata, who have implemented value driver models across all their coal operations from mine to loading point in Queensland, Australia. Xstrata Coal use value driver models to assist both short term development and production decisions and their longer term mine planning and annual budgeting. Mine managers now have the ability to understand the full fi nancial impact of production volume decisions taken at the mine site. Potential operational decisions can also be discussed easily with senior members of the fi nance team who are also able to quickly undertake scenario analysis to understand the fi nancial impact of different scenarios.


Obtaining a full production value chain perspective of each mining operation is the fi rst step in identifying the value drivers across the entire portfolio. To obtain a robust portfolio view of all operations, any shared processes or infrastructure must be properly linked in the value driver model. A portfolio view allows senior executives to understand the different options available to them to achieve target production levels across the full asset portfolio. This provides divisional managers or mine general managers the capability to understand the fi nancial impact of their operational decisions on the entire company portfolio. This is particularly important where the complexity of the supply

chain makes it increasingly diffi cult to estimate the aggregation effect towards the end of the supply chain. Small increases in production over short periods at different mine sites for example, can aggregate to exceed crushing capacity, stockpile capacity or contracted rail capacity leading to signifi cant additional cost.

Having operations and their interdependencies incorporated into one model allows management to quickly estimate the impact of one variable change on the entire portfolio. This capability is extended to operational, capital and strategic decision making. Having a portfolio view of operations allows a mining

company to align production capacity to the level of overall demand at an optimum cost. A portfolio level view of operations and respective operating costs, gives management the tools to make portfolio decisions about production levels. This is particularly important when a new target production is signifi cantly above or below budgeted production which often renders budgeted cost assumptions meaningless for costing new production levels. This allows management to answer questions like: “What should our production portfolio look like if prices rise by 20%?” or “where should we reduce production if our customer demand falls by 35%?”

D. Optimisation across extended mining operations

Figure 5: Example output from value driver model across an extended mining operation

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

Sensitivity analysis

EBIT impact of + 5% change in operational value driver

EBIT impact of -5% change in operational value driver

Value Drivers

Rail Load Out Facility Capacity

Processing Plant Shut Downs

Longwall Operating Delays

Dragline Maintenance Delays

Excavator Fleet Idle Time

Longwall Change Out Time

Truck Fleet Idle Time


LoadOutBin

Tailings

Mine 1 ProductionROM t 3,560,005 3,560,005


Mine 1 ROM StockpileOpening Bal. 10,000 10,000

Closing Bal. 10,000 10,000

Truck Vol -

Mine 1ROM

Mine 2ROM


Closing Bal. - -

Truck Vol -

Opening and closing Run ofMine stockpile balances

Mine 3ROM



Closing Bal. - -

Truck Vol -

Mine 4ROM



Closing Bal. - -

Truck Vol -

Rejects from crusher

Crusher 1 RejectsTonnes 168,549 168,549

RAW 1

RAW 1 StockpileOpening Bal. 20,000 20,000

Closing Bal. - -Rejects

Rejects

Crusher 1

Crusher 1 ProductionRAW t out 4,705,459 4,705,459

RAW 2

Push to Raw 2 StockpileROM t 68,668 68,668

RAW 2 StockpileOpening Bal. - -

Closing Bal. - -

Push to Raw 2 StockpileRAW t - -

Crusher 2 RejectsTonnes 169,744 169,744

Crusher 2 ProductionRAW t 7,362,897 7,362,897

RAW 3

RAW 3 StockpileOpening Bal. 327,107 327,107

Closing Bal. - -

Opening and closing Raw stockpile balances and

dozer push between stockpiles

CHPP 1Rejects from CHPP 1

tonnes 838,725 838,725

Tailings from CHPP 1tonnes 209,681 209,681

CHPP 1 ProductionProduct t 2,576,244 2,576,244

CHPP 1Product

Rail WeighbridgeProduct RailedProduct 1 - -

Product 2 3,091,306 3,091,306

Product 3 4,908,694 4,908,694

Total 8,000,000 8,000,000

CHPP 2Product

CHPP 2 Product StockpileOpening Bal. 83,908 83,908

Closing Bal. - -

CHPP 2 ProductionProduct t 5,916,770 5,916,770

CHPP 2 Rejects from CHPP 2tonnes 1,865,999 1,865,999

Tailings from CHPP 2tonnes 466,500 466,500

Tailings

Run of mine feed to crusher

Monthly production from coal mines

Rejects

Crusher 2

Rejects and tailingsfrom plant

Conveyor feed to processing plant

Product railed by commodity type

Rejects

Rejects

Product produced from plant to

produce stockpile

Figure 6: Value driver model of extended mining operations

In Queensland, Australia, Xstrata Coal have implemented value driver models for all their mining operations and then linked those parts of each model that are also linked in the physical world, such as shared conveyors, preparation plants and stockpiles. Xstrata has been able to analyse the entire value chain of a particular mine complex involving several mines and shared preparation plants and conveyors.


A tool for rapidly building value driver models

It can take a signifi cant period of time to build a complete value driver hierarchy for all assets and activities in any particular mine and then allocate all fi xed and variable costs to those assets and activities. Building a value driver model from a zero base for a new mine site can take approximately 6-12 weeks, longer for extended mining operations or when dealing with a new accounting system.

Beyond the major differences between open cut and underground mining operations, the mining value chain employs a fairly standard set of assets and processes. PwC has developed a Microsoft Excel based software tool named Archimedes FACX which contains a library of mining activities, assets and chart of accounts that can be used to build a base model of any mine site and asset portfolios. Archimedes FACX follows structured value driver model build process and runs from a graphical user interface allowing rapid construction of a prototype value driver model.

Customisation of any element of the mine operation that differ from standard mining processes can be undertaken in minutes.

Archimedes FACX also has the capability to download the general ledger for the mining operations being modelled and undertake predictive mapping of all cost accounts to accelerate the process of cost mapping. Typically over 80% of all cost components can be predetermined using text based recognition of cost centres and accounts. This feature signifi cantly reduces the time demands on the fi nance team and mining operations team which can often be the main constraining factor. Manual allocation of remaining cost components and manual verifi cation of automatic mappings however are required to ensure that the value driver model is complete. Archimedes FACX has reduced the time taken to produce a value driver model from 6-12 weeks down to 1-4 weeks. This signifi cantly reduces the time taken to produce the fi rst results, often providing immediate cost saving opportunities.

E. Rapid build of value driver models for mining operations

Typically over 80% of all cost components can be predetermined using text based recognition of cost centres and accounts.


Figure 7: Building a value driver model using Archimedes FACX

Identify fixed and variable costs

Upload trialbalance

Production Assets Other

Map activitiesfor mine

Operational Financial

Define accountlist for mine

Define/capturesundry drivers

Identify keyassets and

metrics

Capture valuesof assetmetrics

Build the production

schedule basedon life of

mine plans

Build value driver model

Map trial balanceto mine activities

Map variablescosts to drivers

Mine Activity Mapping

Move Node

Mine Type

Coal Open cutCoal UndergroundCoal Preparation Plant

DrillBlast

Production

Rehabiliatation

Support Mining

Make it a child of selected node

Move it above selected node

Move it below selected node

> >

Add

Del

Move

Save

OK Cancel

Coal Open cut New Coal Mine

Name

Site Preparation

DrillBlast

Drilling

Production

Rehabilitation

Reshaping

Top Soil Placement

Support Mining

New Coal Mine

Site Preparation

DrillBlast

Drilling

Blasting

Production

Rehabilitation

Support Mining

New Coal Mine

Asset Productivities

Name

Longwall Units

Longwall Unit 1

General

Operational Time= Available Time

– Positioning Time

Asset CapacityLongwall Unit 2

– Planned Maintenance

– Reactive Maintena...

– Operational Delays

UoM Baseline Alternate Variance

Set all Alternate = BaselineLongwall Units

= Working Time

= Calendar Time

– Unscheduled Ti...

– Idle Time

-

5,169.22

5,169.22

5,169.22

8,760.00

1,568.55

1,146.23

-

-

-

-

-

-

5,169.22

5,169.22

5,169.22

8,760.00

1,568.55

1,146.23

-

-

-

-

-

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

Value Driver Tree

Name

= Development= Development

= Continuous Miners

= Variable Operating Costs

= Variable Operating Cost

UoM Baseline Alternate Variance

Mechanical Parts

+ Development Production

+ Fixed Costs

+ Shuttle Cars

= Variable Maintenance Costs

= Breaker Feeders

÷ Electrical Parts

– General Consumables

+ Small Tools

+ Ground Engaging T...

+ Pipes, Valves and F...

+ Lubricants

x Other

-0.6%-0.6%

-13.5%-18.9%-18.9%-39.8%

0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%0.0%

1,763.1322,007,942.52

857,368.10577,386.22

1.420.500.420.020.070.070.280.06

-405,916.44279,981.88

-1,180,674.90

134,929.52

1,773.8822,142,150.39

991,575.97711,594.08

1.750.830.420.020.070.070.280.06

-405,916.44279,981.88

-1,180,674.90

134,929.52

$/m$$$

$/ROM t$/ROM t$/ROM t$/ROM t$/ROM t$/ROM t$/ROM t$/ROM t$/ROM t

ROM t$

$$

Pit-top costs value driver model

Cost to Activity Mapping

Financial information to be mapped

Cost centre #

Allocate cost to :

Coal Open cut

Additional detail

Account #

Addtional detail

Mapping target

Cost type

Process

Activity

Update Cancel2 of 3 Skip

DTR091

OPERATORS - ORDINARY TIME

Labour - Operators

Support Mining

Water Trucks

2011

Labour - Operators

Mapping selections

Cost type :

Site Preparation

DrillBlast

Production

Rehabilitation

Support Mining

Grader

Pit Water Management

Readway Maintenance

Light Vehicles

Support Trucks

Pit Pump

Loaders

Lighting Plant

Other Support Assets

Mine Services

Mine Overheads

Maintenance Workshop

Water Cart 777D TR91

Archimedes FACX


Benchmarking operational and fi nancial performance

Once key value drivers have been identifi ed, benchmarking can also be undertaken across a company’s other mining operations and also against comparable performance of other mining operations. PwC has now developed a library of operational and fi nancial metrics for large mining operations that can be used for comparative external benchmarking. These benchmarks have been collated over several years of operational improvement projects with top tier mining companies in Australia. These operational and fi nancial benchmarks are also contained within the Archimedes FACX software tool.

Figure 8 : Benchmarks contained in Archimedes FACX

Figure 9: Example benchmarking output in Archimedes FACX

0

Production (’000 tonnes per annum)

Dev

elo

pm

ent

Co

st($

per

met

re)

Benchmarks

Mine being modelled

0

1 2 3 4 5 6 7 8 9 10

140

120

100

80

60

40

20

Benchmark report

Benchmark Library Selected

Save Discard Close

Open Cut benchmarks Benchmark - Operations

Site Preparation

Overburden Removal

Unit cost per hectare cleared

Unit cost per bcm of top soil re

ROM tonnes mined per FTE

Dragline cost per gross bcm

Dragline swing time

Dragline productivity

Dragline rehandle

Excavator average productivity

Excavator average availablility

Excavator average utilisation

Travel average utilisation posi

Open Cut benchmarks

Site Preparation

Overburden Removal

Drill and Blast

Unit cost per hectare cleared

Unit cost per bcm of top soil re

ROM tonnes mined per FTE

> >

Add

Del

Move

Benchmark - Operations


F. Conclusion

In response to a new era of rapidly fl uctuating prices and demand, mining companies now require to be considerably more fl exible in their approach to cost optimisation of mining operations. Robust modelling of cost and value across the extended operations of a mining company is a key requirement for maximising value regardless of the economic cycle. Producing value driver models for extended mining operations is one method to quickly investigate potential cost reduction or EBIT maximising opportunities across the extended mining supply chain.

The time taken to build value driver models for extended mining operations can also be improved signifi cantly through the development of libraries of mining assets, processes and cost elements which allow rapid confi guration of models of new mining operations. A portfolio view of extended mining operations provides senior management with the capability to understand the fi nancial impact of their operational decisions on the entire company portfolio.

Acknowledgements

This paper has been developed following insights gained by PwC while working on operational improvement projects with Anglo American, BHP Billiton, Xstrata Coal and Xstrata Copper. Sean Miller of Xstrata Copper, Rebecca Phillips of Anglo American Metallurgical Coal and Mark MacManus of Xstrata Coal all took the time to provide us with valuable insights to the realities of their mining operations.

Our special thanks to Xstrata Coal who engaged us to develop value driver models for their coal operations in Queensland and have allowed us to reproduce some material developed while working on their projects.

Thanks also to our PwC colleagues; Aaron Carter (Toronto offi ce), Chris Melck and Xing Lui (both Brisbane) who worked with Fabio on the development of the Archimedes FACX software tool and contributed many of the ideas contained in this paper.

References

Aaron Carter, Brian Gillespie and Chris Gilbert, February 2009, Finding cost effi ciencies in mining operations through effective value driver modelling

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

PricewaterhouseCoopers, 2010. “Mine: Back to the Boom Review of global trends in the mining industry” Global Energy, Utilities and Mining.

PricewaterhouseCoopers, 2009. “Finding cost effi ciencies in mining operations through effective value driver modelling” Global Energy, Utilities and Mining.

© 2010 PricewaterhouseCoopers. All rights reserved. In this document, “PwC” refers to PricewaterhouseCoopers, which is a member fi rm of PricewaterhouseCoopers International Limited, each member fi rm of which is a separate legal entity.

“PwC” is the brand under which member fi rms of PricewaterhouseCoopers International Limited (PwCIL) operate and provide services. Together, these fi rms form the PwC network. Each fi rm in the network is a separate legal entity and does not act as agent of PwCIL or any other member fi rm. PwCIL does not provide any services to clients. PwCIL is not responsible or liable for the acts or omissions of any of its member fi rms nor can it control the exercise of their professional judgment or bind them in any way.

About the authors

Brian Gillespie Partner Tel: +61 7 3257 5656 E: [email protected]

Brian is a Partner with PwC in Australia leading Strategy and Operational Improvement assignments for Resources companies. In recent years Brian has worked on a diverse range of projects across the mining, oil and gas sectors such as hedging strategies, logistics and supply chain optimisation, credit risk, safety, major feasibility strategies, organisational re-structuring and process improvement.

Brian has led large projects with Anglo American, BHP Billiton, Rio Tinto, Xstrata Coal, British Gas, Santos, Origin Energy, Dalrymple Bay Coal Terminal and Queensland Rail Coal Division.

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

Stephen LoadsmanDirectorT: +61 7 3257 8304E: [email protected]

Stephen is a Director in PwC in Australia leading projects in the Resources sector where he specialises in corporate performance management, fi nance effectiveness and cost reduction. Stephen has led various value driver modelling, KPI defi nition, management reporting, capital effi ciency, and operational planning and forecasting assignments across Australia, UK, Asia, Canada, South America, Fiji and PNG.

In recent years Stephen has led assignments with BHP Billiton, Xstrata Copper, Xstrata Coal, Anglo American, Macarthur Coal, Newcrest, YanCoal, Santos, and Queensland Rail.

Stephen holds a Bachelor of Business (Accountancy) from Queensland University of Technology and is a Chartered Accountant, with the Institute of Chartered Accountants of Australia.

Fabio BuckeridgeSenior ManagerT: +61 7 3257 8354E: [email protected]

Fabio is a Senior Manager in the PwC Consulting practice in Brisbane. He specialises in operational improvement and cost reduction and has led multiple value driver modelling assignments. Fabio has 8 years of international Resources sector experience and has knowledge of electrical engineering, logistics, mining, accounting and fi nance. He has worked in South America, USA, UK and Australia.

In recent years Fabio has managed large operational improvement projects for Anglo American, BHP Billiton and Xstrata. Fabio has experience across several commodities and has recently managed the implementation of value driver models across all Xstrata Coal mine sites in Queensland, Australia. Fabio holds a Bachelor of Finance and Accounting (fi rst class distinction) from the Queensland University of Technology and is a Chartered Accountant, with the Institute of Chartered Accountants of Australia.

Australian Energy and Resources Industry Leader Michael Happell, MelbourneTelephone: +61 3 8603 6016Email: [email protected]

Australian and Global Mining LeaderTim Goldsmith, MelbourneTelephone: +61 3 8603 2016Email: [email protected]

Russia and Central and Eastern EuropeJohn C. Campbell, MoscowTelephone: +7 495 967 6279Email: [email protected]

CanadaPaul Murphy, TorontoTelephone: +1 416 941 8242Email: [email protected]

United KingdomJason Burkitt, LondonTelephone: +44 0 20 7213 2515Email: [email protected]

United StatesSteve Ralbovsky, PhoenixTelephone: +1 602 364 8193Email: [email protected]

Latin AmericaColin Becker, SantiagoTelephone: +56 2 940 0016Email: [email protected]

South AfricaHugh Cameron, JohannesburgTelephone: +27 11 797 4292Email: [email protected]

ChinaDerrick Ryley, BeijingTelephone: +86 10 6533 2207Email: [email protected]

Rita Li, BeijingTelephone: +86 10 6533 2365Email: [email protected]

IndiaKameswara Rao, HyderabadTelephone: +91 40 6624 6688Email: [email protected]

PricewaterhouseCoopers, Riverside Centre, 123 Eagle Street, Brisbane QLD 4000GPO Box 150, Brisbane QLD 4001, AustraliaOffi ce: +61 7 3257 8995Facsimile: +61 7 3023 0936Website: www.pwc.com.au

Contacting PwC

optimising extended mining operations through value driver … · 2015-06-03 · 4 optimising...

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