Sales of star products grew faster than production and supply capacity, forcing difficult decisions about which clients to prioritize. Limited visibility into inventory locations and status across distribution centers made it harder to respond quickly, risking non-compliance, financial losses, and penalties from unmet client demands and quotas as supply chains and production lines scale.
A predictive analytics platform connects directly to the company's core business system, combining historical inventory, production, and sales data with open orders. A machine-learning model forecasts demand and recommends which clients to prioritize and which warehouse should fulfill each order, maximizing margin while reducing penalties, with a real-time dashboard giving decision-makers full visibility into exactly where every product is and its status.
The predictive analytics solution drove a $22M USD increase in income and a 50% decrease in excess stock, alongside fewer penalties on late or incomplete orders through better sales strategy and coordination. Continuously flowing product also reduced warehouse lifecycle across existing stock.
USD income increase
Decrease in stock
Penalties on late or incomplete orders
Warehouse inventory turnover
For this project, we combined different technologies to deliver the full solution.
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