Preventing Perishable Goods Stockouts
Engagement Overview
A regional convenience store chain operating over 200 locations faced persistent stockouts and elevated spoilage across its perishable goods category—dairy, prepared foods, and fresh produce. A data-driven engagement redesigned the chain's replenishment model by identifying structural inefficiencies in its supply logic and deploying a predictive stockout model.
The Challenge
The client's replenishment process relied on static par levels set quarterly, with minimal responsiveness to demand variability driven by day-of-week patterns, local events, or weather. The consequences were twofold: overstocking of low-velocity SKUs drove spoilage costs, while understocking of high-velocity items caused frequent, revenue-eroding stockouts. Store managers lacked the analytical tools to anticipate or escalate risk in advance.
Analysis & Methodology
The engagement began with a comprehensive diagnostic of POS transaction data, inventory logs, and delivery records across a representative sample of store locations. Root causes of stockout events were mapped across SKUs, store clusters, and time periods.
A probability-of-stockout model was then developed using forward stepwise regression—a method that builds a predictive model incrementally, adding one variable at a time and retaining only those that meaningfully improve predictive accuracy. Starting from a large pool of candidate variables (including day-of-week, recent sales velocity, supplier lead time, and seasonal indices), the model selected the most statistically significant drivers of stockout risk. This approach produced a transparent, interpretable model well-suited to operational deployment.
The model was embedded into a daily replenishment alert system, enabling store managers and supply planners to act on predicted risk before stockouts occurred.
Key Insights
During our comprehensive diagnostic, as well as through our development of the aformentioned stockout model, we found the following:
Stockout frequency was highly concentrated: the top 15% of SKUs by velocity accounted for over 70% of lost sales events
Supplier lead time variability was a primary unmanaged risk factor, particularly for prepared food items with short shelf life
Day-of-week and proximity to local events were among the strongest predictors of elevated stockout probability identified by our model
Results
Deployment of the probability-of-stockout model and associated replenishment protocol changes generated measurable improvements across the perishable goods category in particular:
30% reduction in lost sales events attributable to stockouts in targeted SKU clusters
Approximately $450K in annualized cost savings, driven by reduced spoilage, lower emergency restocking costs, and recovered revenue
Improved supply planner efficiency through prioritized, model-driven daily alerts replacing manual inventory reviews