A product recall is the process of replacing defective goods for consumers. 

Each year, manufacturers recall millions of products, from all types of consumer electronics to the automotive industry. How to improve Quality and Avoid Recalls using AI technology?

End-to-end control of all your manufacturing process

Maximize quality and efficiency by 30%

 

More than just replacing of products: What a recall truly costs 

The most critical damage is the loss of reputation that leads to loss of market share. 

Everyone remembers about the Samsung Galaxy Recall, which resulted in the organization suffering a loss of around $3 billion dollars. Samsung at this time also lost $26 billion in value in the stock market.  After a three-month-long investigation, the company determined that the incidents were caused by the combination of two types of faulty batteries from different suppliers.

According to a recent study about the automotive-recall resurgence, the number of recalls related to electronic and electrical systems have risen nearly 30 percent per year. The auto industry faces a new challenge—vehicle quality.

Automakers and suppliers have paid almost $11.8 billion in claims and recorded $10.3 billion in warranty accruals for U.S. recalls in 2016. In 2017, on average, 3.1 vehicles were recalled for each vehicle sold.

The main reasons for recalls: Lack of visibility and end-to- end control in the company’s Manufacturing Operations globally. 

Why is it so difficult to control the manufacturing process? 

In each factory we find a variety of different machines that deliver different data structures that are not unified and which do not speak “one language”. 

This situation limits the ability to gain deep control and insights of the process and detect malfunctions due to  quality issues and efficiency issues.

How recalls can be prevented? 

The first and most important would be to integrate and unify all manufacturing data to one database and comprehensive automated analytics system, including data from suppliers.

The next step is to arrange the data in a Meaningful way : 

  • Set Tests, process and field data of each unit and each step in the product’s life cycle.
  • Ensure that Quality and Yield issues are flagged automatically. 
  • set an effective way to identify the causes of detected problems that is quick and easy..
  • Set Automatic alerts and predictions of abnormal behavior..  
  • Constantly measure all of your Key Performance Indicators.

How to use AI technology to gain end-to end control of your Manufacturing line and avoid Recalls 

The digital transformation approach meets consumer demand with safe, high quality products, while maintaining effective and efficient operations with trading partners.

QualityLine is an Analytics software that automatically and continuously integrates and analyzes multiple types of data from any factory worldwide using AI technology to optimize quality and efficiency of the production line.

The technology systematically runs an automated root cause analysis that includes all your manufacturing data, including data from suppliers. 

A correlation between tested parameters will predict failures. The system will send automatic alerts for abnormal behaviors preventing your organization from dealing with a recall incident.

Benefits from QualityLine Solution 

Root cause analysis and deep diagnostics

  • Quick drill down to a single tested unit
  • Cp/Cpk analysis
  • Prediction of failures
  • Correlation coefficient between tested parameters
  • Correlation between faulty products and technicians analysis

Advanced Analytics model: Unlimited Manufacturing data being managed to 1 database

  • Products performances dashboard
  • SMT Analytics (AOI, placement, SPI) dashboard
  • Retests Analytics dashboard
  • Faulty units repair analytics dashboard
  • RMA analysis
  • Data cleansing analytics dashboard
  • Financial control analytics dashboard (cost of lack of quality and yield)

Automatic alerts, reports and traceability

  • Automatic alerts for abnormal behaviors
  • Traceability and test history of each unit throughout its life cycle
  • Automatic reports scheduler
  • Preventive maintenance of equipment
  • Qualification of employee monitoring
  • Routing control of manufacturing process monitoring