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

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Alan Dormer delivered the presentation at the 2014 Heavy Haul Rail Conference. …

Alan Dormer delivered the presentation at the 2014 Heavy Haul Rail Conference.

The 2014 Heavy Haul Rail Conference had a focus on driving efficiency with smarter technology. Australasia’s only heavy haul rail event is the annual meeting place for professionals interested in the latest projects, technologies and innovation in this dynamic sector.

For more information about the event, please visit: http://bit.ly/hhroz14

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  • 1. Improving reliability and capacity in heavy haul operations – Technology trends in supply chain optimisation Alan Dormer, CSIRO 0459 801269 DIGITAL PRODUCTIVITY AND SERVICES FLAGSHIP
  • 2. Agenda 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 CSIRO | Page 2
  • 3. Analytics 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!) CSIRO | Page 3
  • 4. INFORMS Analytics Section says: 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 CSIRO | Page 4
  • 5. Analytics for BMH Projects and Logistics Real Options Analysis Life Cycle Analysis CSIRO | Page 5 SD Design and Infrastructure Planning Tactical Supply Planning Operations Planning EIA / EIS Life of Mine FIFO Planning Maintenance Needs Analysis Price and Rates Forecasting Day of Operations Contract Alignment Licence to Operate Sampling and Variability Mass and Grade Accounting Sensors and Real- Time Monitoring Execution Control Cost of Complexity Mainstream logistics analytics Project Mgmnt Particulate Flow
  • 6. Summary of state-of-the-art and trends Technique Applications Maturity Challenges / Trends Discrete optimisation Infrastructure/SC CSIRO | Page 6 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
  • 7. Summary of state-of-the-art and trends Technique Applications Maturity Challenges / Trends Discrete optimisation Infrastructure/SC CSIRO | Page 7 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
  • 8. High-level system decisions Arriving trains Dump stations Storage pads Berths
  • 9. Port Simulation Simulating port operations and undertaking analyses of berth and shipping channel capacities
  • 10. 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.
  • 11. HVCC Capacity Planning - Inputs Shipping demand – scenario including variability over ~6 months Existing infrastructure – rates and efficiency/utilisation factors Relative costs of upgrades
  • 12. 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)
  • 13. 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.
  • 14. 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
  • 15. IFAP Freight Network Analysis In this scenario, the Flinders Hwy from Cloncurry to Mt Isa is highly utilised, partly by Ernest Henry mine outputs
  • 16. Summary of state-of-the-art and trends Technique Applications Maturity Challenges / Trends Discrete optimisation Infrastructure/SC CSIRO | Page 16 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
  • 17. Analysis of variability and throughput
  • 18. 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 Example: Port Waratah Coal Services 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
  • 19. 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. • Quantify variability in train unloading times • Estimate effect of various causes of uncertainty • Fit model • Evaluate effect of possible changes Variability by Mine
  • 20. 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?
  • 21. Summary of state-of-the-art and trends Technique Applications Maturity Challenges / Trends Discrete optimisation Infrastructure/SC CSIRO | Page 21 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
  • 22. Operations management
  • 23. 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
  • 24. Background: RTIO 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
  • 25. 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
  • 26. Emu Galah Gecko Gull Ibis Koala Pelican Harding Green Pool Western Creek Brolga Dingo Dove Dugite 7Mile Mesa J PPt CD CD3 CD5 2Mile Caliwingina (2025/35) EII CD2 CD+1 Installed Funded Future rail adds Mine name (possible start dates) Koodaideri (2015/30) (377) Turee Syncline (2013/24) (387) Legend Maitland Murray Camp Arches Mesa A (2010) Churdy Pool CLA CD1 CD3 CD2 CLB CD4 CD5 CD6 CD7 Bungaroo (2015/16) Yard Yard adds Brockman Refuge Silvergrass Beasley River (2028) Mesa G (2013) Mount Region (2020/26) Metawandy (2020/33) Jimma (2050/53) Lizard Lyre Possum Nammuldi new (2012/23) (20??) Nammuldi Brockman 2 (406) West Angelas Wombat JN Banksia Mallee Tom Price Paraburdoo Cockatoo Spoonbill Marandoo Yandi Osprey Quail Jabiru Juna Falcon Finch Hawk Downs 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) Marandoo BWT No Name (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 (416) (462) (362) (449) (299) (302) (274) currently ~1400 km of track, 30 + 5 consists (436) S Hill (417) (410) (310) •Pooled fleet train ~25kt 233 wagon trains •Deepdale 18kt 160 wagon trains •cycle times between 20 to 40 hours
  • 27. Rapid growth in response to demand Photos courtesy of Rio Tinto
  • 28. Planning Tool 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 Photos courtesy of Rio Tinto
  • 29. Verification and simulation 5000 4500 4000 3500 3000 2500 2000 1500 1000 500 0 Number of Trains # trains P1 P2 P3 P4 P5 P6 Plan S1 S2 S3 S4 S5 S6 400000 350000 300000 250000 200000 150000 100000 50000 0 Shipped Tonnes Port1 Port2 Port3 Shippedk t Plan S1 S2 S3 S4 S5 S6 4000 3500 3000 2500 2000 1500 1000 500 0 - 500 Net Remaining Train Hours 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 Plan Tool
  • 30. 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 Photos courtesy of Rio Tinto
  • 31. Hunter Valley Coal Chain Coordinator Courtesy HVCCC http://www.hvccc.com.au/AboutUs/Pages/MapOfOperations.aspx
  • 32. 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
  • 33. 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
  • 34. Summary of state-of-the-art and trends Technique Applications Maturity Challenges / Trends Discrete optimisation Infrastructure/SC CSIRO | Page 34 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
  • 35. 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
  • 36. Minimise Overall Maintenance Cost Availability Cost Of maintenance 100% Capital Cost of Additional Assets Minimum Cost
  • 37. Summary of state-of-the-art and trends Technique Applications Maturity Challenges / Trends Discrete optimisation Infrastructure/SC CSIRO | Page 37 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
  • 38. Principles for Robust Planning 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 CSIRO | Page 38
  • 39. Modelling Uncertainty 30000 25000 20000 15000 10000 5000 Simulated paths: Nickel 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. CSIRO | Page 39 0 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
  • 40. Results 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 CSIRO | Page 40 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
  • 41. Summary CSIRO | Page 41
  • 42. Analytics for BMH Projects and Logistics Real Options Analysis Life Cycle Analysis FIFO Planning CSIRO | Page 42 SD Design and Infrastructure Planning Tactical Supply Planning Operations Planning EIA / EIS Life of Mine Maintenance Needs Analysis Price and Rates Forecasting Day of Operations Contract Alignment Licence to Operate Sampling and Variability Mass and Grade Accounting Sensors and Real- Time Monitoring Execution Control Cost of Complexity Project Mgmnt Particulate Flow
  • 43. Summary of state-of-the-art and trends Technique Applications Maturity Challenges / Trends Discrete optimisation Infrastructure/SC CSIRO | Page 43 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
  • 44. 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

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