As our post-pandemic economy continues to take shape, retailers across the spectrum continue their struggle with a common bottleneck — inconsistent replenishment — leaving chronic bare spots on store shelves and customers frustrated.
Simple math tells the story: A hypothetical retailer with 1,000 stores may carry as many as 10,000 SKUs at each location. In turn, those numbers could multiply into 10 million distinct decisions toward right-sizing inventory and allocation, in sync with localized customer demand. How can retailers achieve profit-optimal “right numbers” across every SKU? In short, which stores get how much, and how often?
Artificial Intelligence (AI) and machine learning (ML) have taken a lead role in the solution. This advanced data science is now rapidly refining and leveraging new dimensions of real-time, granular-level data — accurately forecasting effects of promotions and markdowns, while incorporating external factors such as projected shipping delays or even local weather patterns.
This delivers new levels of hyper-targeted, data-driven replenishment and inventory optimization, reducing carrying costs of misallocated products, at the same time eliminating lost sales from extended out-of-stocks — and ultimately enhancing overall profitability.
Some C-suite retail decision makers have been wary of adopting AI/ML-powered solutions because they fear it might entail a full-scale, capex-heavy “rip-and-replace” of their entire replenishment infrastructure. In reality, those legacy systems may simply be hindered by a few “clogged arteries'' that would benefit from an improved diet of fresh data.
The science behind the solution combines AI/ML algorithms with an automated technique for achieving an optimized balance between “short” and “long” risks — what data scientists have termed stochastic optimization.
We can explain this process by comparing it to how a self-driving vehicle navigates roadways: An initial layer of AI/ML first “sees the world” by interpreting all available data points — recognizing differences between an ice cream truck and an ambulance — and assesses their likely paths. That real-time data is then filtered through an optimization engine that calculates the most efficient route between points A and B.
Applied to retail replenishment, the base AI/ML layer interprets all available data variables — recent sales history, possible impact from promotions, and other pricing changes, along with supply chain disruptions or other external factors beyond the retailer’s control. That data is next fed through a stochastic optimization engine, which factors in all applicable business rules and other constraints.