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

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

The central idea was to build an AI -based platform, fed by predictive models that connect data from the entire logistics chain and turn them into practical recommendations to reduce risk, improve planning and optimize resources. This included integrating structured and semi -structured sources, ensuring data enrichment, applying validation processes and delivering results to dashboards that support operational and strategic areas. The system was also designed to identify failures, anticipate maintenance needs and improve the alignment between production, purchases and receipt.

• Non-planned stop reduction by fault anticipation

• Cost decrease with maintenance and corrective intervention

• Increased efficiency and productivity in the logistics chain

• Best view of the life cycle of components and materials

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

• Faster adjustments in operational and strategic planning

• Improvement in the quality of service provided internally and externally

• Reduction of operating and waste costs along the chain

• Increased reliability and predictability in logistics operations

• greater agility in responses to demand changes or chain breaks

• Support to team prioritization and resource allocation

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