The higher your level of analytics, the more value you will be able to extract from your data to create a global end-to-end control of your manufacturing process.

 

AI predictive analytics 

97% of companies plan to integrate AI into their manufacturing activities over the next two years, according to a Deloitte survey. AI Predictive analytics requires real-time prediction capabilities for quality monitoring, and anomaly detection. AI automation empowers operational experts to execute predictive modeling rapidly and efficiently. Analyzing trends and behaviors using predictive modeling, statistical algorithms and machine learning is called predictive analytics. 

Using these predicted outcomes, companies can create preventative measures and reduce possible future risks, such as product recalls, defaults and malfunctions. Predictive analytics is becoming more popular among manufacturers as they recognize its benefits.

Reduce downtime and maintenance demands

Manufacturing pain points as downtime are being addressed with predictive analytics. By collecting more data and making correlations, predictive analytics becomes more accurate. 

Predictive Maintenance Analytics can help manufacturers track every detail of a workflow in real time, reducing machines downtime. The possibility of collecting more data points is enhanced by connected real-time devices. Through predictive maintenance algorithms,.unplanned machine downtime can be prevented and unnecessary maintenance can be avoided, allowing managers to plan maintenance and make necessary adjustments in advance.

*Predictive maintenance is the approach to predicting the failure of operational equipment and implementing preventative maintenance to avoid unplanned downtime.

Improving quality by Predictive analytics

Monitoring performance allows for the alerting of processes that are out of tolerance or are potentially affecting quality. By stopping or adjusting a process earlier, rework can be greatly reduced or eliminated. A possible quality failure could be detected by changing the production process or by predicting machine wear to schedule maintenance before it occurs.

QualityLine AI Predictive analytics technology

QualityLine AI manufacturing analytics provides advanced prediction and analytics algorithms for:

  • Machines and testing stations performance and prediction 
  • Product performance and prediction
  • Automated data mapping 
  • Machine learning 
  • Anomaly detection 

QualityLine uses automated anomaly detection by automatically scanning the collected data and identifying quality and yield problems of each product and each process according to the level of severity. 

The findings of this anomaly detection are shown in a methodological way in the dashboards to enable a quick and accurate root cause analysis. 

Automated data mapping: 

QualityLine automatically maps each manufacturing data structure (testing, sensing, manual, ERP/MES, any other shop floor systems). This enables a quick integration of any data sources, including changes. 

The scanning process is done automatically and it is based on AI pattern recognition technology that enables the technology to automatically map each structure. 

Anomaly detection 

QualityLine uses automated anomaly detection by automatically scanning the collected data and identifying quality and yield problems of each product and each process according to the level of severity.

Boosting manufacturing performance 

The use of AI predictive analytics in manufacturing can solve many complex production problems. As the manufacturing industry generates massive amounts of data from many repetitive and manual tasks. Utilizing AI predictive analytics will improve manufacturing performance, reduce downtime, and optimize efficiency. Register below to our upcoming webinar on predictive analytics: