Aimpoint Digital Supply Chain Intelligence

A global retailer/CPG company needed to rebalance inventory across a multi-echelon network without compromising service levels. Instead of relying on static inventory rules and fixed buffers, Aimpoint used mathematical optimization to determine the right sourcing, shipment, and safety stock decisions across the network. The result was roughly a 30% reduction in safety stock, along with stronger service performance and better operational efficiency.

An agriculture company needed to optimize multi-stage production and distribution planning across a global logistics network. Aimpoint applied an optimization model to evaluate production and logistics decisions together, helping the business move beyond manual planning and see the full network trade-offs more clearly. The company achieved a 5–10% reduction in logistics costs while also improving the speed and consistency of planning decisions.

A global steel manufacturer faced highly constrained, large-scale operations where production scheduling decisions were too complex for traditional planning methods alone. Aimpoint applied advanced optimization to replace slower, rule-based
scheduling with a more rigorous approach that could evaluate far more options in far less time. The outcome was production planning in minutes, not days, giving the organization faster decisions and greater confidence in what would actually work.

A large-scale industrial operation needed to improve production throughput whilereducing planning complexity and manual decision-making. Aimpoint implementedconstraint-based optimization and scenario modeling to help the business allocate resources more effectively, test alternatives quickly, and make decisions based on proven trade-offs rather than habit. The result was improved throughput and reduced planning time, with more efficient use of constrained resources across operations.