This document discusses optimizing the resource-to-market supply chain by embracing variability. It notes that variability exists beyond our control in factors like weather, economics and material properties. It proposes using non-linear optimization techniques to explore more possibilities when planning under uncertainty. This allows optimization across multiple objectives like throughput, cost, quality and safety. By integrating planning with optimization science that considers variability, companies can determine impacts and make better end-to-end supply chain decisions under complex conditions.

1. Optimizing the "Resource-to-Market"
Supply Chain by Embracing
Variability
Daniel Spitty
February 2014
SME Conference, Salt Lake City, USA

2. 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?

3. 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.

4. Context
●Planning and Scheduling
● What is going to happen next?
● Use what has happened as a starting point as knowledge

5. Context
●Resource-to-Market Supply Chain
● Bulk commodity, vertically integrated, multiple functions

6. Context
●Variability
● Beyond our control and best predications
● Using probability opposed to binary (zero or one)

7. Context
●Optimization
● Everyone wants to make the best decision
● What is the best outcome when there are multiple objectives?

8. Observations in mining

9. Silos: Process, Technology, People

10. Variability

11. Where does variability exist?
●Macro
● Weather
● Economic
● Political
●Micro
● Business strategy
● Material
● Asset capability
● Asset availability

12. 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

13. 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. 14
Enablers to embracing variability

15. Integrating and Optimizing drivers of productivity
Production Workforce
AssetsEnergy
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

16. 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

17. 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

18. Conclusion

19. 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.

20. Thank you!