The project aims to integrate demand forecasting with purchasing processes, supplier portal and material receipt, using predictive models and automation to anticipate needs, plan purchases and organize receipt flows according to the actual capacity of the operation. The solution also connects communication with suppliers, logistics portals and operational histories, creating a system that generates more fluidity, reduces bottlenecks and avoids excesses or shortages of materials in stock.

The main challenge was to consolidate different data sources — sales histories, purchase orders, supplier information, logistical status, scheduling and physical infrastructure — into a single analytical architecture capable of accurately predicting demands. It was necessary to transform raw data into actionable information, ensure the quality and timeliness of this data and generate insights that could be directly applied to operational decisions, such as order adjustments, reorganization of receipts and prevention of stock accumulation.

The central idea was to build an AI-based platform, powered by predictive models, that connects data from the entire logistics chain and transforms it into practical recommendations to reduce risks, improve planning and optimize resources. This included integrating structured and semi-structured sources, ensuring data enrichment, applying validation processes and delivering results in dashboards that support operational and strategic areas. The system was also designed to identify faults, anticipate maintenance needs and improve alignment between production, purchasing and receiving.

• Reduction in unplanned downtime due to anticipated failures

• Reduced maintenance and corrective intervention costs

• Increased efficiency and productivity in the logistics chain

• Better view of the life cycle of components and materials

• Decisions based on real data and not just history or feeling

• Faster adjustments to operational and strategic planning

• Improvement in the quality of service provided internally and externally

• Reduction of operational costs and waste throughout the chain

• Increased reliability and predictability in logistics operations

• Greater agility in responding to changes in demand or disruptions in the chain

• Support teams in prioritizing actions and allocating resources

• Connection of the entire logistics chain with practices aligned with Industry 4.0, promoting a more automated, intelligent and efficient environment