OptimInventory is a scientifically grounded inventory-optimization platform built on more than nine years of peer-reviewed research. The simulation engine behind it has been cited in journals including Sustainability, Applied Sciences, and the European Journal of Operational Research.
Most companies hold either too much inventory — tying up capital, warehouse space, and emissions in stock that doesn't move — or too little, losing sales every time a customer order can't be fulfilled. The trade-off seems unavoidable, but it isn't. The conflict only exists when inventory levels are set by intuition or fixed rules of thumb.
OptimInventory uses simulation modelling and machine learning to find the precise inventory configuration that meets your service targets at the lowest possible cost — while accounting for stochastic demand, supplier lead times, working-day patterns, minimum order quantities, and your own constraints.
Independent academic studies using OptimInventory's simulation engine have demonstrated:
These are not marketing numbers. They are published, replicable, peer-reviewed findings from journals indexed in Scopus and Web of Science.
Inventory optimization affects every part of supply-chain performance:
We can run a free preliminary simulation on a sample of your inventory data and show you the cost and emissions reduction that's available without changing your service levels.