Practice, not theory:
How forward-thinking companies ars using IIoT solutions to optimize their value chains, improve production, and cut costs.

All thanks to Big Data analytics: higher-quality paintwork, fewer costs

 

Carmaker group makes annual savings of USD 2 million due to improved paint quality (fewer defects) after IIoT analytics discovers a problem on the painting line.

Problem:

Quality defects in the carmaker’s painting line – especially on Mondays. No obvious explanation for variations in quality since production parameters like spray pressure and speed were constant at all times.

IIoT analytics:

Correlation of wide range of measured values and quality level
Result: Paint quality depends on airborne dust volume and therefore the ventilation system
Solution: Ventilation system now also runs outside business hours on weekends and public holidays

Big Data:

  • Environment data: Temperature and air humidity/pressure/flow rates and quality such as dust content
  • Process data from machinery and production robots: Spray pressure, quantity, speed, color code, etc.
  • Quality level: Good, poor, imperfections, etc.

Interactions identified: Process optimization for production lines

 

Chemicals group saves over USD 100,000 per month with multiple production lines now manufacturing in parallel at the same quality.

Problem:

Quality defects in manufacturing as soon as more than one production line is operated in parallel. Each line produces zero defects on its own. Process input values offer no obvious root cause(s) or conflicts.

IIoT analytics:

Correlation of measured values from all production lines
Result: During parallel production, pressure increases in the supply lines.
Solution: Understanding interdependencies in the production process enables comparison, adjustment, and optimization of manufacturing parameters.

Big Data:

  • Process status: Running, in preparation, shutdown, etc.
  • Process parameters: Pressures, temperatures, volume flow rates, etc.
  • Quality attributes

Targeted analytics in production: higher quality and less scrap

 

Automotive supplier lowers reject rate due to quality defects with targeted root cause analysis.

Problem:

Ongoing quality defects in a steel stamping works cannot be explained by the few manufacturing parameters available.

IIoT analytics:

Multi-week analysis correlating measured values with production and process parameters
Result: Quality defects resulting from drop in stamp pressure.
Solution: Optimize manufacturing parameters to increase quality of stamp operation, resulting in less scrapand reduced production costs.

Big Data:

  • Process and machine data: Pressures, oil flows, stamp speed, temperatures, etc.
  • Quantity and quality of stamped parts

Higher operating efficiency in production – global rollout

 

Electronics group improves operating efficiency in LED panel production. Following successful deployment in one factory, a global rollout is now planned for big data analytics.

Problems:

  • Variations in the quality of LED panels.
  • Maintenance has to be synced with production, since completion of a batch must not be interrupted (as it would cause quality issues).

IIoT analytics:

Second-by-second correlation of measured values with metrics; several GB of data a day can be processed.
Result: Product quality depends on ventilation temperature. 
Solution:

  • Continuous ventilation.
  • Rolling trend analysis provides early notification of maintenance/repair requirements, so maintenance can be matched to production.

Big Data:

  • Environment data: Air pressures/flows, temperatures, etc.
  • Machine data from fans: RPM, vibrations, valve settings, etc.