Innovation
at the heart of our solutions

The innovation isn't in the technology, but in how it is applied

We combine robotic innovation with human intelligence to achieve what is and will remain essential: satisfying our customers, reducing their logistics costs, and improving the performance of their supply chain all over the world.

Observing market trends, identifying emerging technologies and opportunities to respond to operational problems… Our scope for innovation extends beyond intralogistics, to cover the whole supply chain, in an omnichannel environment. SAVOYE LABS is our innovation label, uniting scientists, robotics experts, engineers, and users, to design the solutions of the future. We openly collaborate with a broad ecosystem of schools, science labs, and research bodies.

FROM PROTOTYPE TO PRODUCT

Users at the center of the innovation process

Our approach relies on a long and continuous history of innovation in products and services, based on real use cases. Innovating with our customers is an essential aspect of converting a technology or concept into a concrete, operational application and then confirming its benefits. Co-designing, co-developing, and testing solutions for the future with our customers is our way of ensuring that we achieve the things that really matter.

prototype
ARTIFICIAL INTELLIGENCE

Operational research at the heart of control

Optimized choice of the next packages to prepare, control of sequencing equipment, planning tool, dynamic routing calculation… As a result of significant operational research, modeling, and simulation, SAVOYE has filed for many patents. SAVOYE embeds these innovations into its solutions, to solve a large number of strategic, tactical, and operational problems.

MACHINE LEARNING

Data: the essential lever of tomorrow’s solutions

Robotics, forecasting, artificial intelligence, indoor localization, machine learning: the key issue is the leveraging and analysis of data to ease the flow of operations and predict future activity. Providing this ability to capitalize on existing data and offering software features with high added value: this is a major challenge for the supply chain of the future. With many fields of application: flow prediction, labor management, preventive maintenance, anticipating stockouts, optimized resupply, etc.

Machine learning: what use cases?
machine learning
SUSTAINABLE DEVELOPMENT

Responding to social and environmental challenges

We invest in R&D developments that will help us improve our environmental impacts. Reducing the energy consumption of equipment, eco-design of our products to reduce their carbon footprint and improve their recycling, ergonomic workstations, reducing transported volumes: these are all levers to achieve the things that really matter.