Overview
In the fast-paced world of quick commerce (Q-commerce), efficient inventory management is vital. Blinkit and Zepto, two leading players in India’s 10-minute grocery delivery ecosystem, face constant pressure to keep shelves stocked without overburdening storage. Actowiz Solutions deployed advanced AI models backed by real-time web scraping and historical pattern analysis to accurately forecast inventory restocks—ensuring zero missed sales and reduced wastage.
Actowiz’s Approach
1. Real-Time Scraping Engine
Using Blinkit and Zepto's front-end interfaces, Actowiz set up a high-frequency scraping system that extracted:
- Product availability by pin code
- Stock status (e.g., "Only 2 Left", "Out of Stock")
- Price changes and discounts
- Reappearance timestamp of previously out-of-stock SKUs
- Location-wise category rank
2. Historical Dataset Modeling
Actowiz gathered 6 months of SKU availability snapshots (every 30 minutes) for key cities like:
- Delhi NCR
- Mumbai
- Bengaluru
- Pune
- Hyderabad
Sample data structure:
| Date |
City |
SKU |
In Stock |
Quantity Tag |
Reappeared After (hours) |
| 2025-05-12 |
Mumbai |
Amul Toned Milk 1L |
No |
- |
4 |
| 2025-05-12 |
Mumbai |
Amul Toned Milk 1L |
Yes |
"Only 3 left" |
- |
This data was used to train time-series models for each product + pin code combination.
3. Machine Learning Models Used
- LSTM Neural Networks for SKU restock time prediction
- Random Forest Classifiers for binary classification (restock likely/not likely in next 12 hours)
- Gradient Boosted Trees for multi-variate regression on stockout duration
- Bayesian Inference for estimating restocking frequency during promotions
4. Integrating External Signals
The AI models were enhanced with:
- Blinkit & Zepto app promotions
- Festival dates & traffic spikes (e.g., Diwali, Independence Day)
- Weather data for fresh produce perishability
- Vendor-wise replenishment cadence
Key Features of Actowiz’s AI System
| Feature |
Description |
| SKU-Level Prediction |
Hourly probability of restock for individual SKUs by city |
| Stockout Alert System |
Dashboard alert for high-demand SKUs nearing depletion |
| Price vs. Restock Analysis |
Predict restocking lag based on recent discounts or flash sales |
| Vendor Mapping |
Correlate restock speed with past vendor delivery times |
| City-wise Heatmaps |
Visualize restocking rates across hyperlocal zones |
Dashboard Snapshot (Sample)
A dynamic dashboard powered by Actowiz displayed:
For Blinkit – Andheri West, Mumbai
- Top 10 SKUs predicted to be out-of-stock in next 4 hours
- Estimated replenishment time (in hrs)
- Historical avg. restock window
| SKU Name |
Stock Status |
ETA to Replenish |
Demand Surge (%) |
| Tropicana Orange 1L |
Low (Only 2) |
3.5 hours |
+42% |
| Britannia Bread 400g |
Out of Stock |
5.2 hours |
+37% |
| Dettol Handwash 250ml |
Low |
2.0 hours |
+18% |
Client Testimonial
"Actowiz’s predictive inventory system transformed how we plan replenishments. We no longer chase restocks manually. Instead, we’re proactively ready for every demand surge."
– Inventory Head, Blinkit (Mumbai Ops)
Conclusion
This case highlights how real-time data scraping, when paired with AI/ML modeling, can unlock powerful restocking predictions in hyperlocal delivery models. For Blinkit and Zepto, Actowiz Solutions helped move from reactive inventory handling to predictive precision—ensuring consumers always find what they need when they want it.
