Overview
Optimisation techniques like Linear Programming (LP) and optimal control theory are essential across the commodity sectors.
Refiners use LP to optimize crude selection and product output, while upstream companies schedule drilling to maximize output under budget and risk constraints. In trading, LP helps determine optimal storage use or capital allocation across multi-metal portfolios, maximizing risk-adjusted returns. Coal miners and logistics firms optimize coal flows, blending, and contract structures, and use integer programming for efficient railcar and shipping schedules.
Large grain traders apply network flow optimization to route grain from inland silos to export ports at minimal cost, balancing contract fulfillment and logistics constraints. They also optimise processing margins, such as deciding how much soy to crush versus trade. Farmers use LP for crop planning-choosing planting mixes to maximize expected profit given land and cost limits. Food companies use multi-period optimization to manage inventory levels, avoiding both stockouts and excess spoilage.
These approaches enable companies to make data-driven, profit-maximizing decisions in complex, real-world supply chains.
Refiners use LP to optimize crude selection and product output, while upstream companies schedule drilling to maximize output under budget and risk constraints. In trading, LP helps determine optimal storage use or capital allocation across multi-metal portfolios, maximizing risk-adjusted returns. Coal miners and logistics firms optimize coal flows, blending, and contract structures, and use integer programming for efficient railcar and shipping schedules.
Large grain traders apply network flow optimization to route grain from inland silos to export ports at minimal cost, balancing contract fulfillment and logistics constraints. They also optimise processing margins, such as deciding how much soy to crush versus trade. Farmers use LP for crop planning-choosing planting mixes to maximize expected profit given land and cost limits. Food companies use multi-period optimization to manage inventory levels, avoiding both stockouts and excess spoilage.
These approaches enable companies to make data-driven, profit-maximizing decisions in complex, real-world supply chains.