Published on March 13, 2014
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
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 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
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
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.
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