Stockout Rate: Calculating Lost Revenue from Missing Inventory
- Stockout Rate = (Units Out of Stock / Total Units in Demand) × 100.
- Each % drop in stockouts can lift revenue by 2–3 % in Tier‑2/3 markets.
- EdgeOS + Dark Store Mesh gives real‑time visibility, cutting stockouts by 30 %.
Introduction
When a buyer in Guwahati scrolls past a *“Out of Stock”* banner, the loss is not just an abandoned cart—it’s a revenue gap that compounds with every missed sale. In India, where Cash‑On‑Delivery (COD) and Real‑Time Order Tracking (RTO) dominate, understanding and reducing the Stockout Rate is pivotal for margin preservation. This post shows you how to quantify that lost revenue and how Edgistify’s tech stack can help you stay stocked, even during the festive rush.
The Anatomy of a Stockout
- Definition : When customer demand exceeds available inventory at a fulfillment node.
- Impact :
- Immediate revenue loss.
- Long‑term brand erosion.
- Higher RTO and return rates.
Formula: \[ \text{Stockout Rate (\%)} = \left( \frac{\text{Units Out of Stock}}{\text{Total Units in Demand}} \right) \times 100 \]
| City | Units in Demand | Units Out of Stock | Stockout Rate |
|---|---|---|---|
| Mumbai | 10,000 | 400 | 4.0 % |
| Bangalore | 8,500 | 300 | 3.5 % |
| Guwahati | 3,200 | 200 | 6.3 % |
From Numbers to Revenue
- 1. Average Order Value (AOV) = ₹1,200
- 2. Conversion Rate (CR) = 2.5 % (typical for Tier‑2 cities)
- 3. Lost Revenue per Stockout = AOV × CR × (Units Out of Stock)
| City | Units Out of Stock | Lost Revenue (₹) |
|---|---|---|
| Mumbai | 400 | 400 × 1,200 × 0.025 = ₹12,000 |
| Bangalore | 300 | ₹9,000 |
| Guwahati | 200 | ₹6,000 |
- A 1 % reduction in Stockout Rate can lift revenue by ~₹54,000 in our sample.
- A 5 % drop yields ₹270,000—equivalent to the lost revenue across the sample.
Problem–Solution Matrix
| Problem | Root Cause | Solution (Edgistify) |
|---|---|---|
| Inaccurate inventory visibility | Manual stock counts | EdgeOS real‑time dashboards |
| Delayed restock decisions | No predictive analytics | Dark Store Mesh forecasting |
| Inefficient returns handling | Lack of NDR management | NDR Management module |
| High RTO due to unavailability | Poor cross‑regional coordination | EdgeOS inter‑node sync |
Edgistify Integration
- EdgeOS gives you *real‑time* inventory data at every node, eliminating stale counts that lead to stockouts.
- Dark Store Mesh uses predictive analytics to forecast demand spikes—especially useful during Diwali or Holi in Tier‑2 cities—ensuring you stock the right SKU at the right time.
- NDR Management optimises reverse logistics, turning returns into quick restocking opportunities rather than lost revenue.
By weaving these technologies into your supply‑chain fabric, you transform a static inventory snapshot into a dynamic, decision‑driving engine.
Conclusion
Stockouts are not just a customer experience issue—they’re a direct revenue drain that can be quantified and mitigated. By calculating the Stockout Rate, translating it into lost revenue, and deploying EdgeOS, Dark Store Mesh, and NDR Management, Indian e‑commerce firms can slash stockouts by up to 30 % and reclaim millions in lost sales, even during the busiest festive seasons.