new trends in analytical methods for optimising the supply chains
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New trends in analytical methods for optimising the supply chains
GOVERNMENT AND COMMERCIAL SERVICES
Gaurav Singh | Research Stream Leader
CSIRO: positive impact | Presentation title | Presenter name
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•About CSIRO
•Optimisation
•Case studies • Planning and Scheduling
•Current Research
•Learning Points
Presentation Outline
Analysis of Transport Systems & Supply Chains Typical questions requiring analysis: How can such a complex system be understood?
What is the best way to increase capacity?
How can the efficiency of the system be improved?
Why are these questions difficult to answer?
Nominal vs Effective capacity of components – trains, tracks, loading points etc Nominal = theoretical or peak capacity of system component
Effective = average performance actually achievable in practice
Variability and Uncertainty Variability = changing but predictable aspects
Uncertainty = unpredictable at planning time
The whole is not the sum of the parts – system interactions.
Many different aspects of the system could be changed
Optimisation
What: A process to assist in decision-making by applying tools like mathematics, computers and algorithms to convert data into information.
Why: to gain competitive advantage; make decisions faster; better asset utilisation; being able to make decision with whole of system view; what-if analysis.
Important questions to ask
• Are there decisions which are being made repeatedly?
• Are there assets/resources which are being under utilised?
• The type of data available?
• What is the meaning of “best” within your operations?
• Where is the variability and uncertainty in the system?
• How good is our forecast?
Optimisation and Simulation
Optimisation Chooses best options
Simulation Shows results of different options
Ideally Optimisation first
Simulation to refine and
illustrate
Examples: UPS
• Problem: Air and ground express delivery services with a fleet of 88,000 trucks. Too much time spent by the trucks idling while waiting to make a left-had turns
• Solution: An optimisation routing software that favoured right hand turns and developed routes that balanced directness with minimal left hand turns.
• Outcome: In 2005, the software eliminated 464,000 driving miles in Washington DC saving 51,000 gallons of fuel.
Typical Optimisation Architecture
Demand
Forecasting
Calculate KPI’s. Planned versus actual.
Combine known demands with past experience to create an
accurate forecast
Capacity and investment planning; pricing
Resource utilisation
Next day, next week planning
Strategic
Planning
Tactical
Planning
Operations
Disruption
management
Performance
Monitoring
Replanning and rescheduling on the day
Optimising Capacity in Existing Infrastructure HVCCC, Rio Tinto Pit-to-port
Building new transport infrastructure is expensive in Money
Time
The fastest and cheapest way to react to changes in transport requirements is through optimisation of existing infrastructure Tactical Planning – periods of weeks or
months
Operational Scheduling – periods of a days
Objectives
Contract compliance
Safety and operating rules
Maximise revenue
Equity
Speed of Solution
Repeatability
Benefits
Create rail schedules faster
What-if and iteration
Recreate schedules
Knowledge base
Demonstrate equity
Tactical Rail Planning
• Why Medium Term Plans (2 weeks to 2 years)
• Identify bottlenecks
• Maintenance alignment
• Maximise throughput
• Efficient use of trains/resources
• Maximise product quality
Tactical Rail Planning: Rio Tinto Iron Ore
• Rio Tinto Iron Ore (RTIO) operations
• Pilbara, Western Australia, 12 mines and 3 ports
• 240 Mt Operation and ~1500 km distances
• Mining
• Production plans
• Loading capacities
• Live/bulk stockpiles
• Maintenances
• Rail
• Fleet of trains: capacity, cycle time
• Network capacity
• Ports
• Car dumpers: capacity, maintenances
• Live/Bulk stockpiles
Photos courtesy of Rio Tinto
CSIRO. Smarter Information Use
Planning Tool for Rio Tinto Iron Ore
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
Photos courtesy of Rio Tinto
CSIRO. Smarter Information Use
Value for customer
CSIRO. Smarter Information Use
Results
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
Tool Tool
Major Impacts: Planning Tool for Rio Tinto Iron Ore
Since September 2011, RTIO has stopped the manual process and uses only our tool to create plans for its 240mt pa operation.
“A comparison between our tool and the previous manual approach demonstrated that, over an 18 month planning horizon, the optimiser scheduled over 500 kilotonnes of iron ore more than the plan obtained manually. Moreover, 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 developed by CSIRO.”,
-IFORS News 2012
Hunter Valley Coal Chain – World’s Largest Coal Operation
Hunter Valley Coal Chain Coordinator
Hunter Valley Coal Chain Coordinator
Responsible for planning and scheduling of all coal exports from the Hunter Valley supply chain
Independent body accountable to all of the major players in the supply chain (mining companies, ports etc)
Planning Horizons:
Strategic: Capacity expansion and changes to business rules looking at 2-10 year horizons
Tactical: Maintenance planning, capacity allocation (to different companies), etc looking at periods of up to 1 year
Operational: Managing ship queue, stockpile allocations, train scheduling etc for 1 day to maximum 2 weeks out
Live run: day of operations disruption management
Hunter Valley Rail Scheduling Example Operational planning of train trips in Hunter Valley Coal Chain ~ 2 day horizon
Inputs: Demand for railing
Availability of trains, track, load points etc
Train paths
Maintenance requirements
(Un)loading rates
Aim: Maximise throughput
Match railing to shipping priorities
Maximise train utilisation
Output: Schedule for trains
Ongoing enhancements
Recent Results 44 trains
~100 components
300 / 250 forward / return paths
32 loadpoints
2 provisioning points
Final Result:
50 train trips scheduled
380,000 tonnes delivered
Regular delivery
Small Gap
Other optimisation models for HVCCC and RTIO
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 model
CSIRO. Smarter Information Use
Collaborative Scheduling Two or more scheduling sub-systems interacting under a negotiation protocol in order to find a feasible, mutually-acceptable and near-optimal schedules for activities
Why Collaborative Scheduling: supply chains have independent players with several shared resources
Industrial problems often yield planning scheduling problems which is simply too large and/or complex
Natural boundaries within the problem bring localised:
Ownership of data, and access to data
Ownership of decisions
Synchronisation of planning processes
Resolutions/precision in plans and schedules
Skills
Facilities
Goods and Materials
Services
Demand
Design of wagons
Discharge of 50mm lumps of coal from a rail wagon. Colours represent initial height layers of coal prior to unloading.
CSIRO. Smarter Information Use
Modelling of dust
Dust generated by coal/mineral particles travelling through a conveyor transfer chute.
CSIRO. Smarter Information Use
Take home messages: Many analytical tools available for analysing and improving transport infrastructure. Data Analysis, Simulation, Capacity Expansion Optimisation, Stochastic
Optimisation
To select the right one, think about: What questions are to be answered?
Data available
On what timescale are decisions being made?
How important are uncertainty and variability in the system?
Effort into analysis commensurate with potential investment
Optimisation tools can move beyond analysing infrastructure capacity to providing guidance on a way forward.
Can improve efficiency and effectiveness of existing transport infrastructure through tools for optimised planning & scheduling
Thank you Digital Productivity and Services Flagship Gaurav Singh
Research Stream Leader
t +61 3 9545 8467 e Gaurav.Singh@csiro.au w www.cmis.csiro.au/Gaurav.Singh
GOVERNMENT AND COMMERCIAL SERVICES
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