Whatever your project size is, we will handle it well with all the standards fulfilled! We are here to give 100% satisfaction.
For job seekers, please visit our Career Page or send your resume to hr@actowizsolutions.com
Actowiz Solutions helped a U.S. retailer sync inventory, improve delivery times, and optimize warehouse planning with real-time Amazon & Walmart scraping.
The client faced three major operational bottlenecks:
1. Inventory sync issues between their online platforms (Amazon, Walmart, internal eCommerce) and physical stores.
2. Unpredictable delivery times leading to low customer satisfaction and frequent returns.
3. Inefficient warehouse utilization, with either overstock or unfulfilled demand in key cities like Chicago and Indianapolis.
They needed a real-time, automated solution to monitor inventory levels and delivery performance across Amazon Fulfillment Centers and Walmart store networks.
Actowiz Solutions deployed an integrated data scraping system to collect and process:
This data was synced via a custom dashboard and API into the client’s ERP and warehouse management systems for automated decision-making.
We built custom SKU-matching crawlers to extract the following data from Amazon and Walmart daily:
| Platform | SKU | Stock Status | Fulfilled By | Price (USD) | ETA (ZIP 60601) |
|---|---|---|---|---|---|
| Amazon | ELEC-BX101 | In Stock | Amazon FBA | 49.99 | 1 Day |
| Walmart | ELEC-BX101 | Low in Store | Walmart DC | 47.99 | 2 Days |
| Amazon | HOM-DT230 | Out of Stock | Merchant | N/A | N/A |
This real-time feed allowed the client to:
Result:
Order cancellations dropped by 18%, and cart abandonment due to “out of stock” errors reduced by 23% in 60 days.
By scraping estimated delivery times from Amazon.com and Walmart.com for over 500 ZIP codes in Illinois, Indiana, and Michigan, Actowiz Solutions created a delivery-time intelligence dashboard.
Sample Delivery Time Dataset:
| Platform | SKU | ZIP Code | Delivery ETA | Fulfillment Center | Prime Eligible |
|---|---|---|---|---|---|
| Amazon | HOM-DT230 | 60601 | 2 Days | Joliet, IL | Yes |
| Walmart | HOM-DT230 | 60601 | 3 Days | Store #456 – Cicero | No |
Insights uncovered:
Using this data, the client:
Result:
Average delivery time reduced from 3.5 days to 1.9 days, and delivery SLA compliance improved by 34%.
Actowiz Solutions implemented stock frequency analysis using scraped historical inventory data for 6 months.
Insights Derived:
Warehouse Allocation Dashboard View:
| SKU | Location | Avg Weekly Demand | Overstock % | Suggested Action |
|---|---|---|---|---|
| ELEC-BX101 | Chicago IL | 1,200 units | 5% | Keep steady replenishment |
| HOM-DT230 | Peoria IL | 300 units | 42% | Divert stock to Chicago |
| ACC-CBL999 | Joliet IL | 50 units | 3% | Move to clearance stock |
Based on these insights, the client revised their warehouse plans:
Result:
Warehouse storage costs dropped by 21%, and order fulfillment rates improved by 26%.
| Feature | Description |
|---|---|
| Amazon & Walmart Scraper | Near real-time scraping with SKU/ZIP targeting |
| Historical Price & Stock Trends | 90-day view for restock pattern detection |
| Fulfillment Heatmap | Visualization of Amazon/Walmart delivery speeds per ZIP |
| ERP/API Integration | Seamless feed into internal inventory & WMS systems |
| Anomaly Detection Alerts | Notifications for unusual price/stock behavior |
“Actowiz Solutions transformed our operations. Their scraping engine made our data actionable—we now make restocking decisions in real-time, not weeks later. From delivery SLAs to shelf planning, we’re running smarter than ever”
— Director of Operations, Midwest Retailer
| Metric | Before | After (6 Months) |
|---|---|---|
| Avg Delivery Time | 3.5 days | 1.9 days |
| Inventory Stockouts | 12.4% | 6.1% |
| Order SLA Compliance | 61% | 95% |
| Warehouse Utilization Efficiency | 64% | 87% |
| Cart Abandonment (Stock Issue) | 18.7% | 5.2% |
With Actowiz Solution's web scraping and data intelligence tools, the client gained a synchronized inventory, predictive delivery insights, and optimized warehouse workflows across three major U.S. states.
This case proves how data scraping, when implemented strategically, bridges the operational gap between digital platforms and physical logistics—leading to measurable business outcomes.