Alan Dormer - CSIRO as given by Dr Andreas Ernst - Improving reliability and capacity in heavy haul operations – Technology trends in supply chain optimisation

Improving reliability and capacity in heavy haul operations – Technology trends in supply chain optimisation

DIGITAL PRODUCTIVITY AND SERVICES FLAGSHIP

Alan Dormer, CSIRO

0459 801269

Analytics... what?

Techniques applicable to Mining SCs and BMH, from the mature to the bleeding edge

Opportunities for the development of new analytics methods and applications to support decisions in Iron Ore logistics

Agenda

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Data-driven fact-based decision making

Data can be observations of events (e.g., ship arrivals) or properties of things (e.g., grade) or abstract concepts (e.g., freight rates)

Data can be forecasts (e.g., demand) or generated outputs by analytics techniques (e.g., simulation results)

Encompasses optimisation, simulation, financial mathematics, statistics, data mining, mathematical modelling (and so on!)

Analytics

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Descriptive analytics

Prepares and analyzes historical data

Identifies patterns from samples for reporting of trends

Predictive analytics

Predicts future probabilities and trends

Finds relationships in data that may not be readily apparent with descriptive analysis

Prescriptive analytics

Evaluates and determines new ways to operate

Targets business objectives

Balances all constraints

INFORMS Analytics Section says:

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Analytics for BMH Projects and Logistics

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SD Design and Infrastructure

Planning

Tactical Supply Planning

Operations Planning

Life Cycle Analysis Real Options Analysis

EIA / EIS

Maintenance Needs Analysis

Life of Mine

Price and Rates Forecasting

Day of Operations

Contract Alignment

Licence to Operate

Sampling and Variability

FIFO Planning

Sensors and Real-Time Monitoring

Mass and Grade Accounting

Execution Control

Cost of Complexity

Mainstream logistics analytics

Project Mgmnt

Particulate Flow

Summary of state-of-the-art and trends

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Technique Applications Maturity Challenges / Trends

Discrete optimisation Infrastructure/SC planning, operations management

Mature Bigger, stochastic and non-linear models

Discrete event simulation BMH planning, operating policy development

Mature Incorporating decision making

Analysis of variability and throughput

Finding bottlenecks, capacity loss, mass loss, grade variation

Mature Automation and embedding of methods

Large scale, integrated optimisation

End-to-end SC planning Developing Data integration, business process change

Real time big data Analysing data streams for indicators and anomalies

Developing Data QA/QC, integrating with operations

Decision making under uncertainty

Robust planning, risk analysis

Developing Capturing realistic levels of complexity


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High-level system decisions

Arriving trains

Dump stations

Berths

Storage pads

Simulating port operations and undertaking analyses of berth and shipping channel capacities

Port Simulation

High-level SC capacity planning

Optimisation approach to determine best infrastructure expansion

Represent the system in terms of:

Decision variables: what can be changed? eg: decide on number of additional trains to be put into the system

Constraints: what are the limitations? Physical constraints: eg maximum number of trains that can be serviced by a load

point.

Business constraints: eg ships must be serviced in a first-come-first-served order

Objective: what is to be achieved? Maximise throughput

Minimise costs

May include soft constraints: outcomes that should be avoided but may be necessary given constraints and other objectives.

HVCC Capacity Planning - Inputs

Shipping demand – scenario including variability over ~6 months

Existing infrastructure – rates and efficiency/utilisation factors

Relative costs of upgrades

HVCC Capacity Planning (cont’d)

Decisions

Increasing train loading rates at any of the loadpoints

Increasing junction capacities

Additional wagons/trains

New dump stations at any of the terminals

Additional stackers or reclaimers at any of the yards

Increasing stockpile space at the terminal yards

Ship loading infrastructure

Using stockpiles & short shipping delays to smooth demand

Outputs

Lowest cost expansion to meet the demand.

Operational usage – daily allocation of infrastructure capacity to best meet demand

Trade-off with shipping delay (controllable via input parameters)

HVCC Capacity Planning Model in Practice

Used in conjunction with existing simulation model

Good agreement between simulation & optimisation models

Optimisation guides selection of scenarios to analyse in more detail with simulation

Useful insight into combination of expansions that is most cost-effective for dealing with significantly increased throughput.

On-going use in HVCCC as various predictions of future demand growth are considered.

IFAP Freight

A freight network design and analysis system utilised to decide on freight routes, transport and processing capacities

Road, rail, sea, pipelines, conveyors

Determine “where, when and how much” in capacity improvement plans that can span 25 years into the future

Developed with Queensland Transport and Main Roads for regional freight infrastructure planning

Specialized for regional transport planning, minerals and bulk materials supply chains

Can be applied to whole supply chains or to specific areas

Incorporates modules for the detailed study of ports

Optimally selecting, configuring and deploying transport infrastructure over multiple years in order to fulfil evolving freight demand for a region, port or supply-chain.

Data for a region, input

using a GIS platform

Optimal freight flows and

infrastructure plans for

each year

IFAP Freight Network Analysis

In this scenario, the Flinders Hwy from

Cloncurry to Mt Isa is highly utilised,

partly by Ernest Henry mine outputs


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Analytics / investigative data analysis

Understand and estimate the effective capacity of bulk materials logistics system elements

Understand variability patterns, sources, transmission and management through supply chains

Understand uncertainty, including analysing predicted versus actual data for uncertainty quantification and causal insights

Statistical analysis can determine factors that have a significant effect on the performance of the system or some component.

Detection of anomalies and outliers potentially requiring attention to improve efficiency

Approach:

1. Analyse data on variability of physical processes

2. Analyse data on information provided by customers, planning and decision-making processes

3. Model system using different operating rules

Purpose:

Understand what are the main factors affecting delays to shipping

Find strategies to reduce delays

Tools: statistics packages data mining scheduling methods simulation

Example: Port Waratah Coal Services

Example: Analysing Historical Data for DBCT

Inloader (dumpstation) variability:

Supply chain variability:

• Understanding difference

between nominal and actual

behaviour of system

• Identify major causes of

variability

• Analyse propagation of

variability through the supply

chain.

Variability by Mine

• Quantify variability in train unloading

times

• Estimate effect of various causes of

uncertainty

• Fit model

• Evaluate effect of possible changes

Sources of variation and uncertainty

Physical processes When was material delivered to terminal relative to ship arrival times? Time from entry to commencement of ship loading Time to load Time from completion of loading to sailing How long are the gaps between sailing of one ship and entry of the next? How long are train travel times? How long are train dumping times?

Planning and decision-making processes How much departure is there from ships being served in order of arrival? What types of ships are sent to which terminal? Does the average number of contract versions vary between coal companies? When were contracts submitted? When were contracts changed? How reliable are estimated stockpile availability dates? How reliable are ship ETAs? How reliable are estimated terminal/berth assignments? How useful is consideration of tides within the planning process?


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Operations management

Planning and operations management Coordinate operations and resolve resource conflicts

Mining Production plans

Loading capacities

Live/bulk stockpiles

Maintenance

Road and rail Fleet capacity, cycle time

Network capacity

Stockyards and ports Dumper use and maintenance

Live/bulk stockpiles

Stockpile sampling, geometry and grade modelling, optimised blending

Ports:

•Eastern Intercourse Island,

Parker Point, Cape Lampert

•Total outloading capacity of

240mt pa

•4 car dumpers and avg 25

trains per day

•Combined bulk and live

yard space of ~20mt

•7 shipped products each

with different grade

requirements

Background: RTIO

Mines:

•12 existing mines and

several planned mines

•~ 3-4 train per day

•~5mt of live and bulk yard

space in each mine. Some

mines have no yard space.

•Most of the mines produce

lumps and fines of variable

grades.

•To maintain a good quality

certain ratio of lump and

fine needs to be delivered

Emu Galah Gecko Gull Ibis Koala Pelican

DugiteDoveDingoBrolga

7Mile

Mesa J

PPt

CD

CD3

CD5

2Mile

Caliwingina(2025/35)

EII

CD2

CD+1

InstalledFundedFuture rail adds

Mine name(possible start dates)

Legend

Maitland

Murray Camp

Green Pool

Harding

Western

Creek

Arches

Mesa A (2010)

Churdy Pool

CLA CD1 CD3

CD2

CLB CD4

CD5

CD6

CD7

Bungaroo(2015/16)

Yard

Yard

adds

Beasley River(2028)

Mesa G(2013)

Mount Region(2020/26)

Metawandy(2020/33)

Jimma(2050/53)

Lizard Lyre Possum

Nammuldi new(2012/23)

Silvergrass(20??)

Brockman

Refuge

Brockman 2Nammuldi

Tom Price Paraburdoo

Turee Syncline (2013/24)

MalleeBanksiaWombat JN

West Angelas

Cockatoo

Spoonbill

Marandoo

Yandi

QuailOsprey

Jabiru

Juna

DownsHawkFinchFalcon

Marandoo

Turnoff

Eagle

Rosella

Wombat

Mulga

Bell

Bird

HD1

HD4(2012/13)

Governor

Hancock

Junction

Teal

Brockman 4(2010/11)

Cassowary

Dog Flats

Western Turner(2011/23)

Giles(2017/26)

Koodaideri(2015/30)

No NameMarandoo BWT(2012/14)

HD2(2022/23)

HD3(2022/23)

Bakers(2032/43)

Rhodes Ridge(2030/48)

Cabbage

Gum Creek(2039/52)

Crest

Wonmunna(203??3)

Juna(203??3)

(21.4)

(44)

(76)

(77)

(94)

(190)

(248)

(288)(277)

(291)

(277) Approx dist to port

(387)

(416)

(406)

(462)

(362)

(449)

(299)

(302)

(274)

currently ~1400 km of track, 30 + 5 consists

(436)

S Hill

(417)

(377)

(410)

(310)

•Pooled fleet train ~25kt 233 wagon trains

•Deepdale 18kt 160 wagon trains

•cycle times between 20 to 40 hours

Rapid growth in response to demand

Photos courtesy of Rio Tinto

Objective: Simplify the planning process Reduce the current planning time Allow for “what-if” analysis

Optimal number of trains needed to maximise throughput while observing Port and rail maintenance requirements

Production plans at various mines

Fleet capacities

Dumping and loading capacities available at ports and mines

Grade quality at ports and mines

Optimize over whole of system, rather than stage-by-stage “Gantt Chart” approaches

Planning Tool


Verification and simulation

0500

100015002000250030003500400045005000

Number of Trains

# t

rain

s

P1

P2

P3

P4

P6

P5

Plan S1 S2 S3 S4 S5 S60

50000

100000

150000

200000

250000

300000

350000

400000

Shipped Tonnes

Port1

Port2

Port3

kt

Sh

ipp

ed

Plan S1 S2 S3 S4 S5 S6

-500

0

500

1000

1500

2000

2500

3000

3500

4000

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47

Net Remaining Train Hours

Plan

Tool

-1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47

Tactical Planning Version

Since September 2011, RTIO has stopped the manual process and uses only the analytics-based tool to create plans for its 240mt p.a. operation.

“... the scheduling tool has been consistently producing plans with higher iron ore throughput than the manual approach, to the extent that the company’s planners now rely solely on the software ...”

-IFORS News 2012


Hunter Valley Coal Chain Coordinator

Courtesy HVCCC http://www.hvccc.com.au/AboutUs/Pages/MapOfOperations.aspx

http://www.hvccc.com.au/AboutUs/Pages/MapOfOperations.aspx

Hunter Valley Coal Chain Rail Scheduling Operational planning ~ 2 day horizon

Inputs: Demand for railing

Availability of trains, track, load points etc

Train paths

Maintenance requirements for trains

(Un)loading rates

Aim: Maximise throughput

Match railing to shipping priorities

Maximise train utilisation

Reduce the planning time (~15 hours)

Output: Schedule for trains

Currently in use by HVCCC Planning time reduced to 30 mins

Quick execution time allows for “what-if” analysis

Other OR models

Maintenance Alignment When to schedule planned maintenance to minimise lost capacity for the

whole system?

Stockpile Planning Optimisation Where to locate stockpiles in the stockyard

Contract Alignment Optimisation Medium term planning to ensure all users (mining companies) get their fair

share of the capacity while maximising throughput

Major Outage Recovery Optimisation How to bring the system back to it’s normal state of operating after a major

outage.

Annual capacity planning models


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Integration – How Airlines Do It

Maintenance

Planning

Allocate maintenance schedule on aircraft to maintenance

facilities

Allocate the right aircraft to routes ‘rotations’

Allocate duty tours to resource groups

Fill vacancies on duty tours with real staff

Asset

Allocation

Crew

Pairing

Crew Rostering

Disruption

management

Replanning and rescheduling on the day

Minimise Overall Maintenance Cost

Availability 100%

Cost

Of maintenance Capital

Cost of

Additional

Assets

Minimum

Cost


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1. Trade off efficiency with immunity to disruption

2. Make decisions when necessary, not all up-front

3. Leave freedom to fix up

Requires

1. Good understanding of risk

2. Integration of data and models

3. Real-time decision support

Principles for Robust Planning

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The objective is to find the value of the optimal strategy (an optimal sequence of operation strategies during the time horizon) that maximises the profitability of the whole multi-year operation.

Modelling Uncertainty

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0

5000

10000

15000

20000

25000

30000

1/01/2011 15/05/2012 27/09/2013 9/02/2015 23/06/2016 5/11/2017 20/03/2019 1/08/2020 14/12/2021

Simulated paths: Nickel

Results are an expected value, and the result of simulating a very large number of realisations (a Monte Carlo style method).

By providing system alternatives, the value of these alternatives can be estimated

Third party acceptance of the techniques, as an investment valuation, is not assured – yet in financial sector applications the techniques are considered valid and used for trading worldwide

Results

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Strategy type Strategy value AU$

Long-term profit optimising strategy (real

options)

$1855 million

Constant feed $1713 million

Local (annual) profit optimising strategy $1734 million

Summary

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Analytics for BMH Projects and Logistics

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SD Design and Infrastructure

Planning

Tactical Supply Planning

Operations Planning

Life Cycle Analysis Real Options Analysis

EIA / EIS

Maintenance Needs Analysis

Life of Mine

Price and Rates Forecasting

Day of Operations

Contract Alignment

Licence to Operate

Sampling and Variability

FIFO Planning

Sensors and Real-Time Monitoring

Mass and Grade Accounting

Execution Control

Cost of Complexity

Project Mgmnt

Particulate Flow


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Accessing Analytics

Who does it: Engineering and economics consultancies

Specialized consulting firms

Research institutions and organisations

Nationally and internationally

More Information: https://www.informs.org/Community/Analytics

Australian Society of Operations Research

IFORS

ANZIAM

csiro.au

https://www.informs.org/Community/Analytics