Artificial Intelligence and Machine Learning at Dassault Systèmes – Part 7/7

This is the conclusion to our seven-part Industrial Artificial Intelligence (AI) blog series, In the last installment we explored the value of AI in industrial sectors. Now let’s look at how AI and machine learning (ML) technologies are integrated into EXALEAD and NETVIBES Information Intelligence on the 3DEXPERIENCE platform.

EXALEAD and NETVIBES are the Dassault Systèmes Information Intelligence brands on the 3DEXPERIENCE platform. They enable organizations to gather, align and enrich big data – whether internal or external, structured or unstructured, simple or complex, real-time or archived – and to deliver that information to users within high-value applications. The portfolio includes:

  • Sourcing & Standardization Intelligence
  • V+R Business Intelligence
  • Industry Intelligence

The solutions have incorporated big data management technologies and advanced analytics, including ML tools and techniques, since the company’s inception. Specifically, EXALEAD’s first product was a Web-scale search platform designed for enterprise use. It used ML to enrich data by surfacing hidden information and relationships, and successfully integrated advanced analytics into its sub-second query processing framework. As such, it provided an ideal foundation for the development of EXALEAD’s current ML technologies on the 3DEXPERIENCE platform, and its big data processing framework remains the core engine powering the brand’s suite of enterprise applications.

Sourcing & Standardization Intelligence

The Sourcing & Standardization Intelligence solution addresses four common and costly problems about how to: define product part standards and share them across projects; choose whether to create a new part, reuse an existing one from a previous project, or buy it from a supplier; equip purchasing departments with the right level of knowledge to improve cost efficiency; and eventually, drive reuse in engineering in accordance with the company’s sourcing and standardization policy.

These problems are solved by AI and ML techniques that automatically group parts together based on their shape similarity and semantic characteristics, as well as aggregating technological and business information to break down the silos between engineering and procurement. The result is a pre-identified classification of parts that complexity and sourcing managers can review and use to launch appropriate actions to either de-duplicate the part references or renegotiate with suppliers.

In addition, the PartSupply service on the 3DEXPERIENCE Marketplace makes comparative data available on tens of millions of catalogued parts from more than 700 suppliers. Users can search to evaluate technical characteristics, performance and quality, and quickly secure vendors in their area able to deliver.

V+R Business Intelligence

Aimed at bridging the gap between virtual and real worlds, the V+R Business Intelligence portfolio allows customers to fully leverage advanced analytics around product data along with related projects, issues, changes and quality processes. Uncovering such metrics is especially challenging because related data is so heterogeneous, ranging from technical notes, 2D drawings and 3D models to multiple bills of materials stored in various formats.

To overcome this challenge, EXALEAD uses specific search and aggregation mechanisms and ML algorithms to produce mathematical representations of these diverse objects, allowing them to be integrated in relevant context for analytics purposes.

One solution using this strategy is Asset Quality Intelligence (AQI). Dedicated to improving the quality and reliability of assets in their operation phase, the AQI cognitive search engine uses internal and/or external data to detect anomalies and emerging trends, while automatically classifying and clustering multi-source field issues.

The use of ML in this context provides a full 360° view of similar issues. It also enables valuable discovery with deep learning techniques used (when data volumes are sufficient) to surface and qualify issues. The solution is also designed to satisfy the need for transparency with sufficient visibility into the data used to aid comprehension, confidence, and certification – while avoiding over-simplifying and weakening algorithms – in order to expose them in a way that is comprehensible to non-specialists (for example, all the parameters and filters used during indexation are exposed, even if complete indexation algorithms are not).

Additional V+R Intelligence solutions also apply ML techniques to solve custom analytics challenges. Developed with customers, they cover many use cases around 360° fleet views, 360° customer views, analysis of customer churn risk, sales prospect scoring, customer targeting, discovery of adverse effects in FDA data and manufacturing procedures, regulation discovery and matching for individual specifications or change requests, and new supplier discovery.

Industry Intelligence

NETVIBES Industry Intelligence solutions equip enterprises to listen, learn, and act on all the social Web data that matters to their business. ML-enabled algorithms allow users to analyze these data and extract relevant insights on all-in-one dashboards for informed decision-making.

Available on the Dassault Systèmes 3DEXPERIENCE platform, Information Intelligence solutions support continuous product improvement and integration, and enable essential analytics governance and traceability (history/versioning), ultimately helping customers improve innovation, operational excellence and business performance.

Part 1: Why AI Matters to Industrial Markets

Part 2: Industrial applications of Artificial Intelligence and Machine Learning

Part 3: Differentiating Between Artificial Intelligence, Machine Learning and Deep Learning

Part 4: Benefits of Machine Learning in Industrial Contexts

Part 5: Key Challenges of Artificial Intelligence in Industrial Sectors

Part 6: Realizing the Value of Artificial Intelligence in Industrial Sectors

Part 7: Artificial Intelligence and Machine Learning at Dassault Systèmes




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