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7/24/2019 20150428 SME Mining Finance Mining Productivity.pdf
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WORKING DRAFT
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
SMEMining Finance, New York, 27-28 April 2015Presentation document
7/24/2019 20150428 SME Mining Finance Mining Productivity.pdf
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McKinsey & Company | 1
Reversing the trend
Mining Productivity Trends
Causes and fixes
Whats it worth?
7/24/2019 20150428 SME Mining Finance Mining Productivity.pdf
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McKinsey & Company | 2
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
7/24/2019 20150428 SME Mining Finance Mining Productivity.pdf
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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
7/24/2019 20150428 SME Mining Finance Mining Productivity.pdf
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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
7/24/2019 20150428 SME Mining Finance Mining Productivity.pdf
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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
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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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McKinsey & Company | 7
The decline is evident across different commodities
MineLens Productivity Index
CAGR, 2009-2013
SOURCE: Company annual reports; McKinsey analysis
1Platinum group metals.
-1.7-1.6
-1.5
Copper PGMs1
Coal
-4.5
Iron ore
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McKinsey & Company | 8
as well as across most major mining geographies
MineLens Productivity IndexIndexed to 2004 = 100
SOURCE: Company annual reports; McKinsey analysis
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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McKinsey & Company | 9
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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McKinsey & Company | 10
Reversing the trend
Mining Productivity Trends
Causes and fixes
Whats it worth?
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McKinsey & Company | 11
The largest drivers of the decline in productivity have been escalatingcapital expenditure and operating costs
SOURCE: Company annual reports; McKinsey analysis
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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McKinsey & Company | 12
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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McKinsey & Company | 17
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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McKinsey & Company | 18
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=6870987/24/2019 20150428 SME Mining Finance Mining Productivity.pdf
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McKinsey & Company | 22
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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McKinsey & Company | 23
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/7/24/2019 20150428 SME Mining Finance Mining Productivity.pdf
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McKinsey & Company | 24
Contents
Mining Productivity Trends
Causes and fixes
Whats it worth?
Open-pit Autonomous haulage can potentially lower haulage costs by 15-
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McKinsey & Company | 25
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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McKinsey & Company | 26
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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McKinsey & Company | 27
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)
WORKING DRAFT
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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