Predictive Quality

Perfecting product and process quality through data-driven prediction.

The “Why”

Predictive Quality Assurance enables organizations to make data-driven predictions of product- and process-related quality. The goal here is optimized QA by using a learning data analytics model to predict optimizations and prescriptively provide recommended actions based on which employees can make more qualified decisions about actions in addition to their own observations.

Benefits of Predictive Quality

+12% higher productivity  

-6% less scrap  

Symbolisches Icon für Produktivitäten

-18% less rework  

-15% of resources saved


A basic prerequisite for Predictive Quality Assurance is the digitalization of quality monitoring. Malfunctions and failures in the production process as well as defects and deficiencies in the goods produced are often still recorded manually today. Here, via mobile devices suitable for the production environment with intelligent dialog-guided input, employees must be able to log paperlessly and record all relevant data.

Next level QA through intelligent pattern recognition

Once digital quality data from production is available, AI models can classify incidents, match them with historical data from incidents, identify previously unseen patterns, and use these insights to recommend preventive measures that will prevent future quality defects and rework in the first place.

Business Value

  • Prevent defective batches
  • Reduce rework effort
  • Reduce costs per unit
  • Detect rejects at an early stage
  • The output of O.K. parts increased

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We look forward to your questions on the use of artificial intelligence.

Industries & Fields of Application


#predictivequality #quality standards #medical supply

Digitization of quality assurance processes regulated by FDA 21 CFR Part 11, which speeds up logging, saves huge amounts of paper, and creates the foundation for Predictive QA.

Mechanical Production

#predictive quality #manufacturing #monitoring

Recording and evaluating measurement data at individual manufacturing stations in a production line. Detect defective components during assembly of complex units before final inspection.

Solution. Through Composite AI.

AI models are used that enable intelligent, digital logging of quality data and faults. This data is classified by AI and matched with historical data. A “recommender system” suggests remedial actions for faults. Time series’ are analyzed to identify patterns and derive preventive measures for quality assurance.

Skill 08
AIDataInformation Retrieval

Human Computer Interaction

Enable interaction between humans & computers in a satisfying way that likens human-to-human interaction as, for instance, in open dialogues.

Skill 07
AIDataInformation Recognition

Predictive Quality

Use your data to be able to foreknow future trends or to influence likely outcomes. Bask in any of the incremental learning effects that come along.

Skill 03
AIDataImage Recognition

AI Image Classification

Automatically classify images according to a certain scheme in the blink of an eye. Use this skill to make time for the tasks that really matter to you.

AIOS – The “How”

Implement the use case 5-10x faster with AIOS.

The AI Operating System “AIOS” from Leftshift One brings “Predictive Quality” “end-to-end” 5 to 10 times faster into production. The software uses the pre-configured AI models in the internal Marketplace to intelligently automate all necessary processes and manage the analysis of the required data. AIOS ensures 24/7 operation and visualizes all required information in a clear dashboard.

How To Start A Project