MIT Sloan Management Review
// THE CHALLENGE
Researchers at MIT's Center for Transportation & Logistics studied the global inventory management problem: businesses worldwide hold approximately $8 trillion in inventory at any given time, with an estimated 20-30% of that inventory classified as excess, obsolete, or mispositioned. Traditional inventory models based on EOQ formulas and static safety stock calculations fail to account for demand uncertainty, lead time variability, and multi-echelon network effects. The MIT team found that most companies optimize inventory at the SKU-location level in isolation, missing system-wide optimization opportunities that could free billions in working capital.
// THE SOLUTION
MIT's research examined how AI and optimization algorithms address these limitations by treating the supply chain as an interconnected system rather than a collection of independent stocking points. AI-driven approaches dynamically recalculate safety stock based on real-time demand signals and supply reliability, optimize inventory positioning across the network using multi-echelon optimization, and generate probabilistic demand forecasts that feed directly into stochastic inventory models. The research found that companies applying these techniques achieved dramatically better trade-offs between inventory investment and service performance.
"The companies that will dominate the next decade of supply chain performance are those that treat inventory optimization as an AI problem, not a spreadsheet exercise."
"AI-driven planning doesn't just cut costs — it buys you time, and time is the most expensive thing in supply chain."— Gartner