What data inspection, testing and validation could do for manufacturers

This applies predictive analytics to simulations, to analyse systems that have yet to exist. Many complex engineering, operations, and business systems can be modeled as discrete-event systems, including manufacturing process control; supply chain, routes taken by autonomous forklifts in hazard avoidance, and error identification for automated HVAC systems. 

Some systems may be complex in inherent design, or operate in uncertain environments.  So simulation is often the only feasible analytics tool to model / study design and operations of these systems.

Simulation modeling can increase effectiveness of design planning, by considering "What If" scenarios.  Covering development lifecycling before, during and after process implementation or execution.  So process analytics can model systems that are too expensive to build or too dangerous to experiment with in practice.

With TÜV NORD's inspection, test and validation, data could be loaded into powerful simulation software that allows realistic 3D models to be built.

Artificial intelligence systems need to be trained before predictive models could be put into action.  Training is conducted using data from previous outcomes and observations.  This is achieved with the machine applying right statistical models, applied to neural networks and deep learning.  Example being, an autonomous robot who learns after experiencing obstacles.  Or a self-learning sensor that learns from false alarms.   Accuracy of machine behavior can be improved if fed reliable data from outcomes.  

Despite advances in unstructured data (photos, social media), Text is still the predominant data structure.  Manufacturing industries, often inudated with reports, spreadsheets, data logs from systems, and QA / QC data, find difficulty in deriving insights from these huge volumes.  

Text analytics is the use of text classification and cluster, as well as topic modeling and vectors, to:

• Discern defect rates, understanding if failures are isolated or indicative of wider failures across manufacturing lines
• Establish trends around reports
• Develop predictive and prescriptive models around spreadsheets