Automotive electronics are all electronic systems designed for use in vehicles, like semiconductors (chips), sensors, engine management, ignition, carputers and infotainment systems. 

The importance of automotive electronics has been steadily increasing in recent years, signifying the shift from mechanical to electronic systems in the industry. 

Back in 1980, electronics accounted for just about 10% of the total car cost, but by 2010, this share had already reached around 35%.

Looking ahead to 2030, it’s projected that electronics will make up roughly 50% of the cost of a new car. 

The primary drivers behind this trend in electronics usage in cars are the broader trends of connectivity and automation.

With cars becoming more electronic every day, is it possible to control quality?

It’s a tough one, but there are already solutions available to overcome this challenge. 

We need a holistic monitoring and control solution that covers the entire manufacturing process globally, including contract manufacturing and vendor manufacturing.

The main idea is to create an ability to support quality management throughout the entire product lifecycle from design to the execution to performance in the field.

 

Which data to be analyzed for continuous improvement for the automotive industry

 

In the past, companies have managed quality internally, using static data & documents or isolated applications to track quality. 

Currently, numerous companies have either developed their in-house solutions or adopted third-party advanced manufacturing solutions created through artificial intelligence and machine learning algorithms. 

The objective is to utilize data analytics in a technology-driven approach that leverages real-time manufacturing data to monitor the production process and enhance manufacturing performance.

Visualizing and controlling in real-time critical manufacturing data points can lead to data-driven decision-making, improved efficiency, reduced costs, and enhanced product quality in the automotive manufacturing industry.

Collecting and analyzing data from our manufacturing process is a crucial factor in our capacity to enhance yield, quality, and reduce manufacturing costs significantly.

Which data to be analyzed for continuous improvement? 

In the automotive manufacturing industry, several key data points are crucial  to ensure efficient production, quality control, and overall process improvement. Some of the most important manufacturing data to be visualized in the automotive sector include:

  • Production Line Performance: Real-time visualization of production line metrics such as cycle times, downtime, and throughput helps identify bottlenecks and optimize the manufacturing process.
  • Quality Control Metrics: Monitoring and visualizing data related to defect rates, scrap rates, and rework rates are essential to ensure that the vehicles meet high-quality standards.
  • Equipment Health and Maintenance: Data on equipment performance, maintenance schedules, and downtime can be visualized to schedule preventive maintenance, minimize unplanned downtime, and extend the lifespan of machinery.
  • Quality Testing Results: Visualizing detailed test data of every tested parameter from various quality testing processes allows quick identification of defects or issues, leading to timely corrective actions.
  • Overall Equipment Effectiveness (OEE): OEE is a comprehensive metric that measures equipment efficiency, availability, and quality. Visualizing OEE data helps in continuous improvement efforts.
  • Customer Feedback and Warranty Claims: Tracking and visualizing customer feedback and warranty claims data provide valuable insights into product quality and areas for improvement.

The specific data to be analyzed and visualized may vary depending on the automotive manufacturer’s unique processes and priorities but to focus on quality 

the 3 main sources for quality and yield improvements are: 

  • Test data from Automated Testing Equipment. 
  • Test data from manual testing processes. 
  • Analyses of repairing processes (failed units during the manufacturing process and also units that were returned from customers). 

AI Analytics for the automotive industry

Gathering the required data consistently, is one thing.  

But how can we effectively analyze the data into actionable information in the realm of smart manufacturing?

AI Advanced Analytics improves the Quality of electrical and electronics modules to reduce defects in the vehicle, auto repairs and recalls by automatically integrating all your manufacturing data to one diagnostic Analytics.

With remote monitoring solutions, you can achieve holistic end-to-end transparency throughout the entire manufacturing cycle, providing precise information about each unit tested.

Through automated root cause analysis, manufacturing efficiency is maximized, and failure rates are reduced, leading to enhanced production efficiency and improved product quality.

QualityLine can help you solve your quality issues by introducing advanced AI based solutions into your Manufacturing Process without disrupting any of your operations.

QualityLine AI Manufacturing Analytics delivers holistic end-to-end visibility and control of your entire manufacturing Quality process. 

It enables a seamless integration with all your manufacturing data sources and automatically generates an analytics and interactive dashboarding system that will automatically predict and send alerts for any anomaly detection to prevent degradation in the manufacturing quality process.

QualityLine AI integrates with the existing manufacturing processes, machinery, and data output:

  • Integrate with any type of saved manufacturing data structure.
  • Continuously collect the data in real time. 
  • Unify and harmonize your data of any data source – creating a global unified Digital Twin database.
  • Automatically analyzes the data using advanced analytics algorithms and machine learning.
  • Visualize the data – creating interactive analytics dashboarding systems.
  • Prediction and automatic alerts of failures and anomalies in manufacturing 

 

Eyal Kaufman (PhD)

AI Manufacturing Analytics Expert