Cognitive Insights to Boost Product Quality and Asset Performance – Part 4/5

EXALEAD Asset Quality Intelligence solution

The EXALEAD Asset Quality Intelligence (AQI) solution is designed to help companies achieve four primary objectives:

  1. More rapidly and accurately detect and understand current or potential quality issues;
  2. Aid support engineers in correcting existing problems;
  3. Help quality managers and engineers develop and deploy preventive maintenance measures to avoid potential problems; and
  4. Minimize future quality issues by providing design, engineering and manufacturing teams (including project and program managers) with lessons-learned intelligence.

To fulfill these objectives, AQI consolidates data from all identified sources of quality-related information. It uses machine learning to mine this important information and reveal potential similarities in quality issues.

Menus and graphs help users refine search and analytics options in order to investigate issues and causes. Once issues are analyzed and the right actions determined, the solution enables these actions to be integrated into a task management framework for rapid resolution and full traceability.

Step 1: Collect Data

As an insight engine, AQI uses advanced search engine technology to collect and index a wide variety of internal sources, like databases and data lakes, and external sources, like websites and open data repositories. This includes data from the virtual world of digital design and simulation, and real-world data from manufacturing, usage and maintenance activities.

The data can be free-form, unstructured content like service notes, consumer forum comments, company emails, 2D and 3D drawings, call center recordings and CRM notes. Or it can be semi- structured data like IoT sensor log files, warranty claims databases or highly structured data like that managed in Product Lifecycle Management (PLM) databases, Enterprise Resource Planning (ERP), Manufacturing Execution Systems (MES) and more.

Step 2: Preprocess Data

In order to facilitate data retrieval and analysis, AQI uses machine learning algorithms to preprocess the data. During this phase, raw data is converted into clean data suitable for analysis. The tasks executed include data cleansing (e.g., checking data validity and converting formats), possibly replacing missing values with estimates, and providing baseline normalization (e.g., regularizing text cases, measurement units and ranges, etc.). Algorithms are also used to identify and remove irrelevant or redundant attributes from data that could have an impact on the accuracy of the analyses.

Step 3: Investigate Issues

After initial processing, machine learning techniques are used to mine the data for hidden relationships, patterns, trends and anomalies, and to reveal these insights through automatically generated graphs and charts.

Identify recurring patterns

These dynamic visualizations can be used to zoom in on the relationships between objects, events, people, places and documents – greatly facilitating an investigator’s ability to detect and understand significant issues.

Investigators can also refine the options and parameters used by the AQI algorithms to create personalized views of the information. This facilitates individual understanding while still enabling collaborative teams to access and work from a shared result set (a “single version of the truth”).

Going a step further, users also have the option to execute custom or off-the-shelf algorithms. This is enabled via the AQI solution’s built-in machine learning studio, which provides an interface for developing or importing custom algorithms to complement the platform’s native algorithms and search-based techniques for rendering, contextualizing and exploring data.

No advanced data science skills are required, however, to use the AQI solution. It makes the work of data scientists easier and more effective, but is designed to automatically enhance any user’s ability to:

  • Detect links or similarities between incidents (clustering),
  • Identify trends,
  • Reveal abnormal (anomalous) behavior,
  • Contextualize discrete pieces of information,
  • Analyze incident causes,
  • Make predictions about future product behavior,
  • Recommend corrective or preventive actions, and
  • Measure the real or anticipated impact of issues.

In addition, the workflows developed for the action items above can be shared and reused, boosting collaboration and enabling continuous improvement in analytic techniques and data processing workflows.

Step 4: Ensure Digital Continuity

This continuous improvement in workflows, collaboration, and data models is key to enabling continuous improvement in product quality and asset performance, outcomes of the Dassault Systèmes 3DEXPERIENCE platform.

Thanks to its integration into the platform, the EXALEAD AQI solution enables users to seamlessly access essential governance and project management tools from ENOVIA and thus better support the execution and monitoring of their analytics workflows.

The solution also enriches the digital replica – the 3DEXPERIENCE twin – with real world information from each physical product in operation, providing a “single source of truth” referential maintained throughout the respective product’s overall lifecycle.

The 3DEXPERIENCE twin is unique in that it uses advanced digital modeling and simulation to enable changes to design, manufacturing, maintenance and other product-related processes to be realistically, safely, and cost-effectively explored and tested in a virtual environment before being implemented. As a complementary tool for understanding and improving quality, AQI is of essential value in this virtual environment, and an important enabler of full digital continuity for products and assets.

In the platform-based project management system, attach personnel, assign tasks, track progress, share knowledge and automate resolution paths for issues identified in AQI.

In the next blog installment we’ll look at the benefits of continuous quality improvement enabled by AQI, including improved compliance, operational excellence and reduced service cost. Read Part 5: The high rewards of continous quality improvement.

 

Part 1: The high cost of poor product quality

Part 2: Information silos

Part 3: Weak-signal intelligence and Cognitive Insight Engines

Part 4: EXALEAD Asset Quality Intelligence solution.

Part 5: The high rewards of continuous quality improvement.

 

 

 

Read our White Paper on Cognitive Insights to Boost Product Quality and Asset Performance