The Challenge

Our client struggled to plan multi-stage production and shipments internationally with the involvement of third-party logistics providers. The existing process took two weeks to generate a schedule and meet customer demand. They needed a more efficient way to plan logistics activities, minimizing cost and time while meeting demand.

Our Approach

We built a solution encompassing the network of international production, treatment, preparation, and distribution facilities. We constructed a workflow that starts with gathering data directly from the customer’s SAP system and datalake. We then built a MIP (mixed-integer programming) optimization model using Python/PuLP to generate a cost-reduced schedule to meet customer demand while respecting supply chain constraints, including location-location movement restrictions (e.g., tariffs & embargos), pre-processing limitations, and subproduct storage time limits.​


1. Streamlined Processes:

The new schedule provided a solution in minutes, replacing a manual system that took up to two weeks.

2. Optimized Schedules:

The new schedules could save millions of dollars with efficient network utilization.

3. Robust Design for the Future:

Easily testing future scenarios, like tariff changes or route shutdowns, is now possible.

Key Takeaways

Improved decision making doesn’t just save time; it leads to cost reduction across the board, from logistics to overall business operations.

The Aimpoint Difference

The Aimpoint Digital OR team has an extensive background in solving complex supply chain, logistics, network, and routing optimization problems. This project required a deep understanding of both supply chain and optimization modeling.

Our team has solved hundreds of similar problems across a wide variety of industries and understands how to build something that is both rigorous and practical.

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