Heat Maps in the Warehouse: Optimizing Storage Based on Product Velocity
- Velocity‑Based Placement : Move high‑turnover items to front‑line slots, low‑turnover items deeper.
- Data‑Driven Dashboards : EdgeOS captures real‑time movement, turning raw counts into actionable heat maps.
- Operational Gains : Reduce picking time by 15‑25%, cut RTO incidents, and improve COD fulfillment in Tier‑2/3 hubs.
Introduction
In the high‑velocity environment of Indian e‑commerce, every second shaved off the picking process translates to happier customers and lower cost per order. Cities like Mumbai, Bangalore, and even emerging hubs such as Guwahati are witnessing a surge in COD orders and Return‑to‑Origin (RTO) claims, thanks to the rapid adoption of online shopping. Traditional “first‑come, first‑served” storage strategies fail to account for the nuanced movement patterns of SKUs. Enter warehouse heat maps—a science‑based, data‑driven method that visualises product velocity and informs optimal placement.
Why Heat Maps Matter in Indian Warehouses
| Metric | Why It Matters | Typical Indian Scenario |
|---|---|---|
| Picking Time | Directly impacts fulfillment cost & customer wait time | In Tier‑2 cities, an extra 30 s per order can mean the difference between on‑time delivery and RTO |
| Stockouts | High‑velocity SKUs deplete quickly | Holiday season in Mumbai sees 12‑hour stockout windows for mobile accessories |
| Inventory Carrying Cost | Deep‑floor items tie up capital | Shadowfax warehouses in Bangalore often hold 20% slow‑moving inventory in cold zones |
Heat maps translate these metrics into a visual matrix, allowing managers to spot “hot” (high‑velocity) and “cold” (low‑velocity) zones at a glance.
Understanding Product Velocity
Product velocity is a composite of three sub‑metrics: 1. Dispatch Frequency – Orders per day. 2. Replenishment Rate – Restock frequency. 3. Return Rate – RTOs per SKU.
A simple formula to quantify velocity:
\[ \text{Velocity} = \frac{\text{Dispatch Frequency} + \text{Replenishment Rate} - \text{Return Rate}}{3} \]
- Dispatch Frequency : 120/day
- Replenishment Rate : 30/day
- Return Rate : 5/day
\[ \text{Velocity} = \frac{120 + 30 - 5}{3} = 45 \, \text{units/day} \]
Higher velocity scores indicate SKUs that demand front‑line placement.
Data Collection: From IoT to EdgeOS
| Source | Data Captured | EdgeOS Role |
|---|---|---|
| RFID Tags | Scan timestamps, location | Aggregates in real‑time, feeds heat map engine |
| Barcode Scanners | Picking logs | Normalises across multiple warehouses |
| IoT Sensors | Shelf weight, temperature | Adjusts for dynamic shelf capacity |
EdgeOS’s lightweight processing unit sits at the warehouse edge, crunching raw data into a Heat Map Dashboard that updates every 5 minutes. This immediacy is vital in Tier‑2 cities where demand can spike overnight.
Problem‑Solution Matrix
| Problem | Root Cause | Heat Map Solution |
|---|---|---|
| Long Picking Paths | Random SKU placement | Hot‑zone placement; high‑velocity items within 2‑row radius |
| High RTO Rates | Mis‑aligned inventory & demand | Dynamic re‑allocation; move items with rising return rates to more accessible zones |
| Under‑utilised Pallet Space | Over‑stocking of low‑velocity SKUs | “Cold‑zone” consolidation; place low‑velocity items in bulk storage or deep racks |
| Inconsistent Stock Visibility | Manual stock‑takes | EdgeOS real‑time alerts; automated cycle counts triggered by heat map thresholds |
Implementing Heat Map Strategies
- 1. Segment SKUs into Velocity Bands
- Hot (≥ 40 units/day) – Front‑line, single‑shelf.
- Warm (20–39 units/day) – Near‑front, 2‑row depth.
- Cold (< 20 units/day) – Deep racks, bulk storage.
- 2. Map Heat Zones on Floor Plan
- Use GIS‑style overlays; colour‑code zones (red=hot, amber=warm, blue=cold).
- 3. Integrate with Dark Store Mesh
- For micro‑fulfilment hubs in cities like Guwahati, the Dark Store Mesh routes orders to nearest heat‑zone, reducing travel time.
- 4. Set Dynamic Thresholds
- EdgeOS monitors velocity drift; if a SKU’s velocity crosses a band, the system auto‑flags it for repositioning.
- 5. Pilot in a Tier‑2 Hub
- Start with 200 SKUs in a Delhivery‑managed warehouse; track picking time pre‑ and post‑heat‑map.
- 6. Scale
- Roll out to Shadowfax‑operated centers in Bangalore, leveraging EdgeOS’s multi‑warehouse sync.
Edgistify Integration: EdgeOS, Dark Store Mesh, and NDR Management
- EdgeOS serves as the intelligence layer, turning raw movement data into heat‑map visualisations. Its edge‑processing reduces latency, critical for COD‑heavy Tier‑2 markets.
- Dark Store Mesh links micro‑fulfilment nodes, ensuring that the heat‑map‑optimized zones are served by the nearest store, cutting delivery time by up to 30 %.
- NDR Management (No‑Delivery‑Reason) tracks why items fail delivery—often due to mis‑placement or stockouts. Heat maps help pre‑empt NDRs by ensuring high‑velocity items are never out of the way.
Together, these components form a symbiotic ecosystem: EdgeOS informs placement, Dark Store Mesh delivers, and NDR Management validates the outcome.
Conclusion
Warehouse heat maps are more than a visual aid—they are a strategic lever that aligns storage layout with real‑world demand dynamics. By quantifying product velocity, harnessing EdgeOS’s real‑time analytics, and integrating with Dark Store Mesh, Indian e‑commerce players can slash picking time, reduce RTO incidents, and deliver on the COD promise that consumers expect. In a market where milliseconds matter, heat maps turn data into decisive action.