Predictive quality, enabled by extensive sensor integration and machine-learning techniques, is one of the most widely-heralded benefits of the fourth industrial revolution.

The approach combines many of the technologies that underpin the new wave of industrial digitization, such as networked sensors, big data, advanced analytics, and machine learning. It is a technique that, by identifying complex patterns over hundreds or thousands of variables in ways that traditional analysis cannot, enables operators to develop a deeper, data-driven understanding of why failures occur.

  • Streaming Data Engine allows collecting Real-Time Data from several kinds of Manufacturing Sensors. The Predictive Model processes the data to produce a prediction

  • Live Dashboard can display Real-Time Data and Prediction

  • Persistent Staging Area (PSA) stores Historical Data


PSA is queryable and data can be used for further statistical analysis, graphs and monitoring dashboards

Where a machine can suffer hundreds or thousands of different kinds of failures, sometimes very rare, it can be impractical to create traditional models of high-enough quality to adequately predict them all.

Our Data Science Competence Center had developed an accurate machine-learning models. Model-based predictive quality becomes a breakthrough way to solve high-value problems.

That’s why establishing a robust data backbone is a fundamental enabler for digital reliability and maintenance. Most organizations already have systems in place to record maintenance- and reliability-related data, but the effectiveness of such systems can be undermined by poor analysis.

Artificial-intelligence techniques, such as natural-language processing, can help organizations transform poorly organized historical data into a form more suitable for automated analysis.


Once they have their data in place, companies need a means to access it. SDG data lake architecture collects data from multiple systems and sources, creating a single source of truth and bridging the information gap between systems to provide a complete picture of an asset’s health. This critical component of the data architecture has multiple uses: it provides the basis for digital performance management, descriptive analytics, and dashboards, while also serving as a unified layer for new maintenance and reliability applications and supplying the data required for advanced-analytics models.

A predictive quality involves the use of descriptive analytics and data visualizations to provide a real-time view of asset health and reliability performance.

Digital performance management automates the generation and presentation of the key metrics and qualitative information that companies use in their maintenance and quality programs.

Advanced analytics can also help to accelerate and standardize the cost-benefit analyses and decision-making that underpin maintenance and reliability activities, helping teams choose the right maintenance strategy such as run-to-fail, planned preventative maintenance, or condition-based maintenance for each asset.