Problem Statement
In industries like yours, it’s a common frustration—overstocking and lost sales happening at the same time. To make matters worse, rebalancing inventory between DCs often means relying on costly expedited transportation. Then comes the added pressure from finance asking, “How can we lower inventory levels?”—as if the high transportation costs of rebalancing weren’t enough already.
It’s shocking to see that many companies today are spending 20-30% of their transportation budgets on stock rebalancing. That’s real and often hard to tackle. The bigger question we should be asking is: How do we sustainably reduce inventory levels without driving up transportation costs and sacrificing service level? And how do we make this process more dynamic and efficient?
Sure, people talk about MEIO or advanced inventory planning systems, but let’s be honest—how often do those actually deliver results? Most companies still rely on spreadsheets and human experiences to manage inventory balancing. It’s not scalable, it’s not dynamic, and it’s definitely not efficient.
So, how can we break out of this cycle and find a smarter way forward?
Solution Statement
Years ago, I saw AB InBev managing inventory rebalancing with a well-structured process based on Excel spreadsheets. They factored in production schedules, inventory goals for each DC, and transportation mode options in terms of cost and lead time. It worked—but it wasn’t optimizing the end-to-end cost.
That inspired us at Sophus to take it further. Using AI-driven optimization, we developed a solution that integrates inventory levels, production or sourcing plans, and transportation into one cohesive model. It provides a single source of truth for dynamically and sustainably optimizing inventory and replenishment plans.
When we implemented this recently at Hisense, the results were a 5% inventory reduction and a 35% drop in rebalancing transportation costs.
