The Impact of AI on Reducing Manufacturing Operational Costs
By optimizing manufacturing efficiency and reducing waste, Industry 4.0 has accelerated ROI and profitability. In 2021, the global smart manufacturing market was valued at USD 88.7 billion; by 2027 is projected to reach USD 228.2 billion with a CAGR of 18.5% from 2022 to 2027.
Artificial Intelligence leveraging data into actionable insights
The use of AI algorithms has enabled the collection of manufacturing data in real-time across multiple factory locations worldwide. The lack of control and quality of the manufacturing process has been replaced with manufacturing intelligence that generates alerts to production issues, predictions and deep root cause analyses.
Some examples of how AI Software solutions can reduce time and cost.
- Deep root cause analysis: Data for every point in the manufacturing process and every location is correlated, predicting failures and sending automatic alerts for abnormal behavior.
- Optimize quality: With automatic alerts and the quick and accurate drill-down to single units and parameters, quality problems can be quickly identified and prevented during the production process.
- Improve First Pass Yield: Identify and predict where inefficiencies occur, and suggest specific changes to the production line.
- Increase production Capacity: QualityLine increases capacity by quickly identifying any errors in the manufacturing process, which will also be prevented.
- Avoid Downtime: As all false testing activities (manual and automated) as well as repairs of faulty units are closely monitored by QualityLine, the overall average standard time to produce a unit will be reduced. In addition to identifying unnecessarily repeated tests, QualityLine also identifies the coefficients of correlation between tests. By identifying and preventing inefficient or faulty activities, manufacturing teams can avoid downtime.
- Prevent Waste: AI Analytics optimizes production efficiency by minimizing waste of materials and other resources (such as electricity).
Utilizing AI Analytics to increase efficiency and productivity in manufacturing by realizing the value of data
A majority of companies do not recognize the value of data or how to collect, store, and analyze data effectively since it is a complex process due to the variety of manufacturing machines and of data sources. A substantial amount of data isn’t analyzed, such as data produced by machines equipped with sensors, which produce more information than most companies are able to process.
By integrating, analyzing, and predicting data in real-time, AI analytics technology can enable companies to improve operational efficiency and gain competitive advantages.
QualityLine’s AI analytics and automated data integration optimize manufacturing efficiency and product quality by continuously collecting manufacturing data into a unified digital twin database.
By harmonizing and analyzing data sources across multiple global locations in real time, en-to-end control is gained, resulting in an increase of 30% in quality and efficiency within the first six months. Analytic dashboards, reports, alerts, and deep root cause analysis are provided by the technology.