20150428 SME Mining Finance Mining Productivity.pdf

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Productivity in mining operations:

Reversing the downward trend

CONFIDENTIAL AND PROPRIETARYAny use of this material without specific permission of McKinsey & Company is strictly prohibited

SMEMining Finance, New York, 27-28 April 2015Presentation document


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McKinsey & Company | 1

Reversing the trend

Mining Productivity Trends

Causes and fixes

Whats it worth?


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Shift in extractiveindustries labor

productivity

Productivity in Mining

SOURCE: US Bureau of Labor Statistics

US Labor productivity2

Indexed 1987 = 100

ChemicalsMining

Food manufacturing

Motor vehiclesOil and gas

80

100

120

140

160

180

200

220

240

260

280

300

1987 10200090 2013

1 For some items the CAGR was calculated to 2012 2 Real gross domestic product over number of hours worked


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Mining doesnt use equipment as productively as other heavy industries

SOURCE: MineLens, McKinsey experts, team analysis

Average Overall Equipment Efficiency

Percent



Productivity measures how efficiently material and resource inputs areused to generate outputs

Flow of a mining operation

Inputs

Labour

Capital

Energy

Water

Land

Otherinputs

OutputsMining operation

Planning Geotech

Businessdevelopment

Operations

SecuritySafety

Maintenance

Supplychain

?

Physical

outputs

Economicoutputs

Revenue Exports GDP

contribution



MPI - a better way to measure Mining Productivity

Accounts for productivity of use ofmining consumables. Costs areadjusted for mining cost inflationto eliminate the impact of priceincreases of consumables

MineLensProductivityIndex

Total Material

Mined (O)

Non-labor opex(C)

Asset value(K)

Employment (N)

Accounts for bothlabor and capital

productivity

SOURCE: McKinsey, MineLens

By using a physical measure of output

instead of economic output, the impactof commodity price swings is eliminated

By using total material mined instead offinal product volume, grade-and-stripratio effects are eliminated and the focusis placed on equipment efficiency



The MineLens Productivity Index reveals that mining productivity globallyhas declined 3.5 percent per annum over the past decade

SOURCE: Company annual reports; McKinsey analysis

MineLens ProductivityIndex, 2004 = 100

85

80

75

70

0

100

95

90 -6.0% p.a.

201312111009080706052004

-3.5% p.a.

-0.4% p.a.


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The decline is evident across different commodities

MineLens Productivity Index

CAGR, 2009-2013


1Platinum group metals.

-1.7-1.6

-1.5

Copper PGMs1

Coal

-4.5

Iron ore


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as well as across most major mining geographies

MineLens Productivity IndexIndexed to 2004 = 100


80

95

85

75

70

65

90

100

45

201309 12082004 0605 111007

North AmericaSub-Saharan Africa AustraliaLatin America

-4.11%

-4.82%

-4.2%

-4.8%

1 Latin America is for 2005 to 2012 2 North America is for 2006 to 2013

CAGR, 2004-2013%


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Mining companies are focusing on productivity improvements tonavigate the challenges

SOURCE: Press searches

Where we're really behind, shamefullybehind, is in the issue of productivity

Thom as Kel ler, CEO Codelco; A pri l2014, CRU World Co pper Conf erence,

Productivity and capital discipline really arevery close to my heart

We must get much sharper on operatingand capital productivity to expand marginsand increase returns, no matter where pricesgo

For us, every 1 per cent improvement inproductivity translates to a $170 millionsaving

And rew Mackenzie, CEO BHP Bil l i ton,

Feb 13

Declining productivity is now aproblemwe share

Paul Dowd , Director OZ Minerals

Mar 14

Productivity is a big challenge for theChilean mining industry

Diego Hernandez, CEO An tofagas ta;

Ap ri l 2015, CESCO, Chile


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Reversing the trend


Causes and fixes

Whats it worth?


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The largest drivers of the decline in productivity have been escalatingcapital expenditure and operating costs


72

100

2013

-3.5% p.a.

2004

MPIIndexed to 2004 = 100

345

100

178

100

1001,681

100

2013

446

2004

EmploymentnumberofworkersIndexed to 2004 = 100

Capexasset valueIndexed to 2004 = 100; real terms1

OpexexcludinglaborcostIndexed to 2004 = 100; real terms1

Productionmined volumeIndexed to 2004 = 100

CAGR, 2004 - 2013%

14.8%

6.6%

36.8%

18.1%

1Capex and Opex adjusted for mine cost inflation.


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Beyond managements control Within managements control

Several of the causes of the productivity decline are withinmanagements control

NOT EXHAUSTIVE

Declining ore grades Deeper UG deposits Higher strip ratios in OP Difficult mineralogy (e.g., more

impurities; shift towards oxideores vs. sulphides in Ni, Cu)

Escalating factor costs acrossall categories (althoughconsumption levers and someaspects of price are withinmanagement control)

Deposits located in more remote,less accessible locations withhigher base infrastructurerequirements

More onerous safety,environment, and communityrequirements

Lengthier permitting processesincreasing delays

Lean operations and Asset

Productivity practices not reachingfull potential Lack of capabilities to achieve

world-class levels of waste andvariability reduction

Lack of shared metrics andobjectives (e.g., poor collaboration

between Maintenance/Ops/Techservices; upstream vs. downstream)

Risk aversion to adopting or tryingnew technologies

Mindset that mining is a basic digit and ship it industry

Productivity improvement not anexplicit goal for mining companiesthe focus is typically on growth andcosts

Lack of business mindset

Geologicaldegradation

Regulatorychanges

Costs

Infra-structurechallenges

Lack offocus onproductivity

Capabilitiesto improve

Silo-

behaviors

Slowtechnologyadoption

SOURCE: McKinsey


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Mining companies can pursue 4 levers to improve underlying productivity

SOURCE: McKinsey

Capability buildingUpgrade individual andorganizational capabilities todeliver the above

Operations excellenceRelentless focus on eliminatingwaste and variability, andimproving productivity of assetsthrough advanced reliability andmaintenance approaches.

Innovation Usage of Big Data &Advanced Analytics todevelop insights on currentoperations (e.g., preventativemaintenance)

Separate man from machine(e.g., autonomous haulage,teleremote operations)

Embed effective ManagementOperating systemsFree people and resources toprioritize productivity andoperational excellence, driverobust performancemanagement, working acrosssilos and data-driven decision

making

Levers for improvingproductivity

1 2

3 4


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Massive increase in variety, volume, and velocity of data available

One zetabyterepresents more than 4,000,000times theInformation stored in the US Library of Congress

7.8

5.3

3.8

2.8

1.8

1.10.7

0.50.40.20.1

2015201220062005 20132007 2011 2014201020092008

More data available in digital form for businesses to use, Zetabytes

d i d l ti ti h l d th b i t


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and increased real-time computing power have lowered the barriers tooperating with the data of the present

exaFLOP

petaFLOP

teraFLOP

gigaFLOP

megaFLOP

kiloFLOP

1 x 1016

1 x 1015

1 x 1012

1 x 108

1 x 106

1 x 103

1960 1970 1980 1990 2000 2010 2020

High performance computing milestones, Floating point operations per second1

1 Terms FLOPS and MFLOPS (megaFLOPS) were invented to compare the so-called supercomputers of the day by the number of floating-point calculations they performed per second.This was much better than using the prevalent MIPS to compare computers as this statistic usually had little bearing on the arithmetic capability of the machine - Source Wiki Article

SOURCE: AMD

A h i h t f h thi ki i i i


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A comprehensive approach to fresh thinking in mining usesmultiple available digitization levers across the value chain

SOURCE: McKinsey Mining Digital Transformation Service Line

Optimize

operations(currentoperating

model)

Exploration data

analytics

Redesign(new operating

model)

Tele-remote/autonomousdrill rigs

Exploration anddevelopment

Mining

Granularbenchmarking

Processing

Yieldoptimization

Data-drivensourcing

Supply chainand logistics

2

Example tools and approaches

Remote Operating Centers tooptimize operations

Process Excellence Centers toproductivity

Tele-remote

andautonomousequipment

Advancedfleetmanagement

d

Asset Productivity with Big Data

c Theory ofconstraints forbottleneckoptimization

a b

MineLens is a proprietary mining benchmarking tool that leverages


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MineLens is a proprietary mining benchmarking tool that leveragesall available mine data to make the right performance comparisons

Compare operating, labor, and cost performance

benchmarks to a global set of peersbenchmarks

Utilize rigorous benchmarking methodology based onproprietary normalization algorithms and analytics

Identify opportunities to improve productivity whilereducing costsand capital expenditures

Establish common metrics to align entire organizationand set appropriate and achievable performance targets

Opportunities for ROIC improvements of 6-13% through:

Increased throughput of 12-25%

Reduced cash costs of 10-20%

Decreased capex on mining equipment of 10-20%

Measure performance improvement over time to ensurevalue creation is fully realized, and sustained

Real direction and focus on areas of big opportunity

a

Over 150 mines,including all major

commoditiesglobally -

granularity ensures

comparability

The MineLens database contains information on over 150 mines and


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The MineLens database contains information on over 150 mines andtheir associated equipment

Open Pit

Shearer

15

Underground

Equipment numbers

Iron ore (41)

Coal (60)

Copper (10)

Gold (23)

Uranium (1)

Oil sands (1)

Zinc (1)

Titanium (1)

Diamond (5)

Phosphate (2)

Nickel (1)

Manganese (1)

Salt (2)

Industrial mineral (1)

Commodities Regions

North America (43)

South America (18)

Africa (25)

Europe (16)

Asia (9)

Oceania (39)

Drill

547

FEL

423

Shuttle cars

282

Shovel

795

Truck

4826

CM

108

Comminution

Mill109

Crusher

93

SOURCE: MineLens (April 2015)

Rock Drill

65

LHD

106

1 Shotcreter (21); Explosive truck (17); Transmixer (7); Roadheader (6)

Bolter

56

Other UG1

52

LPDT

100

Dragline

86

Database content overview

a

MineLens continue to assist a diamond producer in monitoring and


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McKinsey & Company | 1919SOURCE: MineLens

MineLens continue to assist a diamond producer in monitoring andincreasing its mining performance compared to global benchmarks

Value identified - 2014

61%31%

4%96%

2%98%

2%

2014

100%

Year on year improvement

-39%USD/tonne moved

Mining costafterthrough-putincrease

Reducedmain-tenancecost

Reducedoperatingcost

Potentialminingcost

Mining costafter OEEimprovement

Total cost USD Mn 178.5 181.6 177.7 113.4

Diagnostic details

8 week timeline Included client feedback to:

add qualitative insights syndicate analysis with site management understand site challenges and context

ShovelsMTBF,Hours

Drills

MTBF,Hours

Tire life

Hours

Diesel costUSD/tonne.km

UtilizationReliability Consumption

+22%+16%

+46%

+42%

+100%

+70%-4% -2%

201420132012

2012

107%

2013

118%

Identified opportunities

2012 : ~USD 57 Mn 14 % of additional Load & Haul capacity 34 % reduction of mining cost

2013 : ~USD 96 Mn 29 % of additional Load & Haul capacity 49 % reduction of mining cost Significant improvement observed in

equipment availability truck queuing time

2014 : ~USD 68 Mn

4 % of additional Load & Haul capacity 39 % reduction of mining cost Significant improvement observed in

equipment reliability utilization energy consumables cost

a

Rapid Yield Boost leverages Big Data in processing plants to identifyb


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Rapid Yield Boost leverages Big Data in processing plants to identifythe main drivers of yield at a granular level

Leverage limited plant employee time andresources6-8 weeks to gather data, analyse, anddevelop leversseize impact in 3-4 months

Translate very complex analyses into concrete,actionable recommendations that can beimmediately tested in the plant

Optimize 2ndand 3rdlevel parameters and modelcomplex processes through advanced analytics/

big data capabilities (e.g., neural network)

b

Analyze the system holisticallyto optimize the

process end-to-endfocusing on drivers ofprofitability (profit per hour)

Yield optimisation, leveraging Big Data, can drive improvements inb


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Impact

Yield improvement potential(concentrator and smelter), %

Optimal

96%

3%more

Observed

93%

Profit per hour , 000 $/hour

OptimizedBaseline

3538

5-10%more

Yield Improvement , %

Optimized

75

Baseline

50

Plus23%

Yield optimisation, leveraging Big Data, can drive improvements incomplex processing

SOURCE: McKinsey

Situation

Nickel mine

Large nickel mine facing rising costs andreduced marginsfrom market downturn

Complex process with hundreds of variables perstep

Multiple blind spots in understanding drivers ofyield (e.g., in electrode set point, reagentconsumption)

Mine experiencing declining grade over time,requiring increase in throughput to maintaingold output levels - increased costs

Plant was focusing on grade and throughput askey parameters that drive performancelimiteddiscipline on 2nd and 3rd level parameters (e.g.,dissolved oxygen)

Africangold mine

Mine with 5 rod mills in the phosphate processingplant

Rod Mill D had lower yield compared to theothers by 23% - receives coarser than averagefeedbut all parameters in line with other 4 mills

Plant did not fully understand drivers of thedifference (e.g., mill entry point)

Phosphatemine

b

Asset Productivity leveraging advanced analytics allows for predictivec
http://www.freepik.com/index.php?goto=27&opciondownload=19&id=aHR0cDovL3N0b2NrdmF1bHQubmV0L3Bob3RvLzEyODg4Ni9idWxsZG96ZXItb24td29ya3NpdGU=&fileid=599298http://www.freepik.com/index.php?goto=27&url_download=aHR0cDovL3BpeGFiYXkuY29t&opciondownload=121&id=aHR0cDovL3BpeGFiYXkuY29tL2VuL3JvYWQtdmVoaWNsZS12aWNlLXdlc3RlcndhbGQtdHJ1Y2stOTA1NjIv&fileid=687098


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Asset Productivity leveraging advanced analytics allows for predictivemaintenance to anticipate equipment failures and lower cost

Standard Wald

Error Chi-Square

Intercept 1 -4.6889 0.2091 502.6971


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a y g co pa es a e a g st des o a d o t eDigital Transformation journey

d

Rio Tinto remotecenter at Perth

Rio Tinto connected its

mines with satellites so

that workers 800 miles

away can remotely drive

drilling rigs, load cargo

and even use robots to

place explosives to blast

away rock and earth

BHP remotecenter in Perth

Remote centre brings

improved productivity

through improved

volume flows through

our existing sets of

equipment by

improving availability,

utilization and rate

Boliden remotecenter at Aitik

Although the

proportion of metal

found at the Aitik

copper mine is low,

less than 0.3%, it is

a highly profitable

mine because it is

run so efficiently

LKAB remotecenter at Malmberget

LKAB uses its remote

operating center to track

and manage the

performance of an

automated drill rigs fleet

that are in continuous

operation increasing

productivity and safety

Contents
http://www.lkab.com/en/


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Contents


Causes and fixes

Whats it worth?

Open-pit Autonomous haulage can potentially lower haulage costs by 15-


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p p g p y g y20% and make smaller trucks more attractive

SOURCE: Team analysis

0.30

0.35

0.40

0.45

0.50

0.55

0.60

0.65

0.70

150 200 250 300 350 400

$/Tonne

Truck nominal payload capacity, Metric Tonnes

Fleet TCO for manual versus autonomous haulage

Manual haulage Autonomous haulage

15%21%

PRELIMINARY

Underground mines implementing tele-remote technology can


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g p g gyincrease output by up to ~160%

14.11

2.67Increase tramming tempo

New production

Increase from hours 6.04

Original production 5.40

+161%

Increased output from tele-remote LHDs1

Material moved, metric tonnes (million) per year

ILLUSTRATIVE

SOURCE: VCP team; Team analysis

1 Assumes that all material movement is carried out by LHDs, rather than underground haul trucks and loaders

Potential direct economic impact of $88 388 billion per year in 2025


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Potential direct economic impact of $88-388 billion per year in 2025

SOURCE: McKinsey Global Institute analysis

NOTE: Estimates of potential economic impact are for some applications only and are not comprehensive estimates of total potential impact. Estimates include consumer surplus and cannotbe related to potential company revenue, market size, or GDP impact. We do not size possible surplus shifts among companies and industries, or between companies andconsumers. These estimates are not risk- or probability-adjusted. Numbers may not sum due to rounding

PRELIMINARY

56.5-257.0Operations management-Mining

Total 87.5-387.8

Human Productivity-Augmented Reality -Mining

0.1-0.2

Sales analytics-Mining

Health and safety-Mining 2.1-15.3

0.1-0.3

Improved equipmentmaintenance-Mining

26.9-109.8

IoT enabled R&D-Mining

0.4-1.3

Human Productivity-Activity monitoring-Mining

Human Productivity-HR redesign-Mining

0.7-1.6

0.7-2.2

Estimated scope in 2025

Potential economicimpact of sizedapplications in 2025$ billion, annuallySized applications

$2.6T in global mining revenue, $2T in global mining costs

$2.6T in global mining revenue, $2T in global mining costs

$69T in in annual accident and insurance costs

$260B in annual equipment costs

Total wages of mobile workers ($0.15 trillion)

$260B in annual equipment costs

Total wages of knowledge workers ($0.09 trillion)

Total wages of technical workers ($0.02 trillion)

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Last Modified 4/24/2015 9:25 AM W. Europe Standard Time

Printed 26/02/2015 6:01 PM Eastern Standard Time

Productivity in mining operations:Reversing the downward trend

CONFIDENTIAL AND PROPRIETARYAny use of this material without specific permission of McKinsey & Company is strictly prohibited

Documents

20150428 SME Mining Finance Mining Productivity.pdf