Reducing revenue Loss

In the competitive landscape of Home Supplies (imagine Home Depot but similar scale clients), preventing out-of-stock situations is essential for preserving revenue and customer trust. When I joined the analytics team, inventory decisions across 12 key clients were largely reactive that were driven by historical experience, manual checks, and limited reporting. While teams were aware of the impact of stockouts, there was no scalable method in place to quantify or predict the revenue at risk due to recurring gaps in inventory.

Identifying this as a critical opportunity, I led the development of a scalable data solution to proactively reduce out-of-stock-driven revenue loss. Working closely with client supply chain leads, purchasing department and account managers, I designed SQL-based models that ingested 3 years of inventory, sales, and fulfillment data across 1000s of SKUs. The goal was to detect recurring patterns by product and location that consistently led to missed sales.

To operationalize these insights, I developed a trend analysis framework that flagged high-risk SKUs based on stockout frequency, missed fulfillment rates, and time-to-replenishment metrics. These outputs were visualized through Tableau dashboards, empowering both internal teams and client stakeholders to take immediate action. The dashboards prioritized SKUs dynamically each week, guiding planners toward the highest-impact inventory decisions.

The result was a measurable improvement in inventory efficiency. By shifting from reactive to predictive planning, out-of-stock-related revenue loss dropped by 18%, representing approximately $7.2M in annual savings across the 12-client portfolio. But beyond the numbers, the solution helped instill a mindset shift. Inventory planning became more data-informed, with teams using trend-driven prioritization as a standard part of their weekly operational rhythm.

Crucially, the solution gained traction because it wasn’t positioned as a black-box model. Instead, we emphasized its practical utility: a visibility tool that augmented existing intuition and gave teams a better starting point for their decision-making. By embedding the outputs into existing workflows and offering clear action points, the initiative moved from “just another dashboard” to a core decision support asset across the business.

Below is the formula I used to calculate revenue lost.

Spilled Revenue = ([Revenue]/ ([In-Stock Rate] + ((1 - [In-Stock Rate])*(1- [COGS Spill Rate])))) - [Revenue]