From the firm's own write-upReceipt no. 0483
What sucked
Pricing and inventory decisions relied on experience rather than data.
6M in inventory the company had no systematic way to identify underperforming products.
What they built
Sudolabs built an ML-powered pricing engine combined with predictive inventory analysis. The system generates per-SKU pricing and discount recommendations by merging internal transaction data with web-scraped competitor intelligence, and forecasts demand to flag seasonal patterns and slow movers.
What changed
10,000+ SKUs priced by ML
Machine learning now generates pricing and discount recommendations across more than 10,000 SKUs.
€290K dead stock surfaced
Predictive inventory analysis identified €290K in slow-moving dead stock.
Revenue concentration exposed
Analysis revealed that 7.64% of products generate 80% of revenue, focusing inventory strategy.
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