Client
Leading multinational in the beverage sector, with a regional presence and complex, distributed logistics operations within the operational gear of each organization.
The Challenge
The client faced multiple frictions in its logistics planning:
High demand levels on peak days that saturated operational capacity.
Difficulty redirecting volume to valley days, even when available capacity existed.
Planning recommendations that were not carried out due to the operational disconnect between teams.
Limited flexibility in the delivery windows offered to B2B clients, which affected operational continuity.
The challenge was to design a system that generates actionable recommendations, adapted to the dynamics of each client and aligned with real operational capacities.
The Solution: a Hybrid Model
The implemented solution combined business heuristic rules with supervised and unsupervised Machine Learning models:
✅ Cloud-native architecture
The model was entirely developed and implemented on AWS, utilizing services such as:
- S3 for secure storage of structured and unstructured data.
- Lambda and Athena for serverless processing and efficient analytical queries.
- DynamoDB for fast and scalable parameter management.
✅ Flexible recommendation model
The system recommends optimal delivery dates for each client, based on multiple variables:
- Frequency and purchasing patterns
- Channel type and geography
- Available operational capacity
- Contractual restrictions and agreed SLAs
✅ Integration with real processes
It was directly integrated into the logistics planning workflow, through the client's internal tools and dashboards designed to facilitate action.