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Optimize your performance and competitiveness with AI-driven data analysis with Avencore and NETVIBES

A strategic partnership for industrial performance

Avencore has partnered with NETVIBES, the data science brand of Dassault Systèmes, to offer our clients a unique combination of deep industrial knowledge and data science expertise.

This partnership aims to enhance performance and competitiveness through the analysis of large volumes of often unstructured data leveraging various technologies such as Artificial Intelligence (AI), Machine Learning (ML), and Large Language Models (LLM). The ability to transform this data into actionable insights is crucial for industrial players, driving innovation and boosting competitiveness.

Data, AI, and Virtual Twins: Accelerators of competitiveness in industrial sectors

Through our partnership with NETVIBES, we help our clients harness and derive value from their data from diverse sources, to identify opportunities for cost and performance optimization.

We also advise our clients on the adoption of virtual twins to simulate and test various production or product configurations. The virtual twins developed by Dassault Systèmes make it possible to assess specific scenarios (potential improvements, shocks, evolutions, etc.) through virtual modeling, simulation, and visualization, enabling smoother real-world implementation.

A few examples of use cases made possible by the Avencore-NETVIBES partnership

  • Cost reduction : A manufacturer of recreational vehicle engines can generate significant savings by using a solution that analyzes all spending based on technical data.
  • Eco-design: A household appliance company can use AI in the eco-design of a product to gain insights for making informed decisions between different technical concepts, based on their environmental impacts.
  • Maintenance and availability improvement: A defense industry manufacturer responsible for operational readiness and maintenance can use AI to simulate and identify bottlenecks in the “system of systems” (equipment, inventory, human resources, facilities, spare parts, etc.).
  • Supply Chain resilience and procurement: An automotive manufacturer can develop a cost breakdown model to quantify the impact of market volatility (raw materials, energy, components, etc.) on various costs, thereby equipping buyers for more effective supplier negotiations.
  • Decarbonizing the Supply Chain (scope 3): A construction industry player can build an energy efficiency model based on data science and AI to analyze the impact of energy price fluctuations and identify concrete decarbonization opportunities among its suppliers.