optimizing the resource to market supply chain in mining and metal processing operations

Optimizing the "Resource-to-Market"

Supply Chain by Embracing

Variability

Daniel Spitty

February 2014

SME Conference, Salt Lake City, USA

Schneider Electric 2 - MMM

The problem

What is the best increment excavation sequence

and the best material blending combination

such that tonnage, quality, cost, and NPV targets

are met?

Schneider Electric 3 - MMM

The problem

What is the best sequence of material movements,

processing options and blending combinations

through the supply chain such that fixed and

variable demand is fulfilled whilst achieving

throughput, cost, utilization and quality objectives.

Context

●Planning and Scheduling

●What is going to happen next?

●Use what has happened as a starting point as knowledge

Context

●Resource-to-Market Supply Chain

●Bulk commodity, vertically integrated, multiple functions

Context

●Variability

●Beyond our control and best predications

●Using probability opposed to binary (zero or one)

Context

●Optimization

●Everyone wants to make the best decision

●What is the best outcome when there are multiple objectives?

Observations in mining

Silos: Process, Technology, People

Variability

Where does variability exist?

●Macro

●Weather

●Economic

●Political

●Micro

●Business strategy

●Material

●Asset capability

●Asset availability

Why is variability difficult to manage?

●Non linear relationships within the supply chain

●Dynamic and difficult to model

●Permutations and combinations of possibilities are huge

●Often focus on only the next immediate problem to solve

●Execution, Scheduling and Planning is not integrated

●Mining companies are now optimizing to multiple objectives

●Moving from only throughput to ….

● Cost and;

● Revenue and;

● Quality and;

● Safety

Outcomes as a result

●Segmentation of planning team

●Unaided, problem too complex for one person to

solve across supply chain

●Silo systems implemented

●Solve specific problems in each function

● For each planning horizon

●Many limitations

● Assumptions

● Averages

● Buffer

●Hard constraints

●Were once preventable situations

14

Enablers to embracing variability

Integrating and Optimizing drivers of productivity

Production Workforce

Assets Energy

Integrate

Automate

Optimize

• Competencies

• Rosters and Schedules

• Tasks

• Training

• Demand

• Capacity

• Quantity

• Quality

• Blending

• Fixed Plant

• Mobile Fleet

• Logistics Infrastructure

• Office

• Administrative

• Power

• Water and Environmental

• Consumables

Quality

Utilisation

OEE

Throughput

Fulfilment

Profit/Cost

Yield

Environmental

Emissions

Objectives

Safety

Non-Linear optimization

●Explore more accurate and representative

possibilities

●Non-linear, multi-objective meta-heuristic

optimisation techniques can provide

counter intuitive solutions

●Start to break new ground and allow

planners to challenge the status quo

●Challenge the natural and inherent buffer

that exists in their current planning

assumptions

Co-existence of planners and science

●Able to override and lock activities in place

●Ability to still make manual decisions

●Optimisation techniques using meta-heuristic approaches make this

complex process possible

Conclusion

Concluding remarks

●Variability will forever exist for mining companies in the resource-to-

market supply chain

●Utilizing non-linear optimization techniques, embrace variability and

determine impacts

●End to End Supply Chain

●Multiple time horizons

●Optimize across all the functional areas of the operation using science

●Enable optimal decision making

● Trade off between competing objectives such as throughput, quality, cost,

revenue and profit.

Thank you!