Parapetrol

Parapetrol is a B2B distributor with over 25 years in the market, serving 6,000+ business clients. Their catalogue spans 10,000+ SKUs across specialised product lines.

Despite decades of commercial success, their pricing and inventory operations ran on instinct rather than data. The business needed to transition from experience-based decision-making to machine-learning-driven intelligence.

Parapetrol’s wholesale team was setting per-client discounts manually — no standardised framework, no margin visibility, no market benchmarking.

With €6.6M tied up in stock and no demand forecasting, the company had no systematic way to identify which products were earning their shelf space. Both problems shared a root cause: critical business decisions made without data.

Sudolabs delivered a margin-aware pricing application in approximately 8 weeks. The system uses machine learning to generate per-SKU pricing and discount recommendations, combining private transaction data with web-scraped competitor signals.

Following deployment, Sudolabs conducted a deep inventory analysis confirming strong seasonal demand patterns and that just 7.64% of products generate 80% of revenue.

10,000+ SKUs are now priced through machine learning. Wholesale salespeople have real-time visibility into the financial consequence of every discount they negotiate.

On the inventory side, €6.6M in stock has been analysed with predictive demand forecasting. The analysis surfaced €290K in slow-moving dead stock.