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