Algorithms to support industrial decision-making.
Planning thousands of operations, increasing the capacity of existing equipment, sizing investments, and reducing the resources required for testing: AI and data science offer practical ways to improve industrial performance.
Beyond generative AI and agents, machine learning, optimization models, and digital twins are already delivering measurable results and informing decisions based on data and operational constraints.
At Avencore, we combine industrial expertise with mathematical modeling to address these complex decision-making challenges.
Through six anonymized project examples, Avencore illustrates these applications across procurement, logistics, production, and engineering.
- Procurement: Assessing supplier pricing more accurately
A machine learning model analyzes 3D models and technical product specifications to estimate product costs and prepare for supplier negotiations.
The result: 5% in realized savings across a high-volume procurement category and a 25% faster supplier RFQ process.
- Logistics: Reducing the distance traveled to supply a plant
A model recalculates transportation routes across several hundred suppliers, taking their delivery constraints into account.
Applied to routes traveled in 2025, the simulation identifies potential reductions of 38% in transportation costs and 48% in distance traveled.
- Production: Testing options before investing
In food manufacturing, a digital twin compares scenarios combining improvements to existing equipment, extended operating hours, and additional production lines.
It demonstrates that a 30% increase in production volume can be accommodated and recalculates a scenario in 20 seconds when assumptions change.
- Production: Increasing capacity without buying new equipment
At an aerospace supplier, surface treatment operations are constraining the production ramp-up.
By optimizing how parts are assigned to existing equipment and the sequence in which they are processed, the model shows that the workshop could handle 40% more volume without additional investment.
- Packaging: Improving scheduling to deliver on time
A model adjusts the production schedule to account for line capacity, changeovers, and delivery priorities.
The result: an 8-percentage-point improvement in on-time delivery, equivalent to eight additional on-time deliveries for every 100 deliveries.
- Engineering: Shortening development time for complex systems
For complex systems, such as those used in defense, a model optimizes prototype configurations and the sequence of tests to increase equipment reuse.
An initial test showed the potential to reduce non-recurring costs by 15% and the amount of hardware required by 25%.
