The automotive industry and its robust and complicated supply chain is constantly striving to reduce defective parts per million (DPPM). With autonomous vehicles entering mainstream markets over the next decade, the quality requirements for many automotive electronic systems are increasing rapidly, and suppliers have been required to shift from DPPM to DPPB (defective parts per billion) and now even to counting individual “incidents”.
As automotive tier-one manufacturers procure parts from many suppliers, the need for deeper product analytics is paramount in improving overall quality across the supply chain. It starts with closer coordination between component suppliers and EMS partners. Parametric product data must be made available to all participants of the value chain to improve overall quality and help reduce the likelihood of low-quality automotive electronic systems from leaving the manufacturing floor.
The need for better data
The automotive value chain is highly fragmented and the ability to collect product data analytics from the multitude of suppliers is difficult and time consuming. As big data solutions enter the electronics market, the ability to share data across the supply chain is becoming a critical necessity to reducing the number of low-quality devices and RMAs before they become devastating recalls. Sharing data downstream is often problematic as suppliers are still unwilling to share data with their customers, however sharing data upstream (e.g. electronics OEM sharing certain, specific data with their suppliers) is becoming more common and is proving to be extremely valuable for the pioneering companies who are doing it.
Organizations can gain many insights from IC and multichip test and process data; to board and test rework genealogy; to systems performance data; and even in-use data or reliability data from returns. Essentially, organizations can trace every test part through the value chain and understand its essential manufacturing DNA. The ability to collect and analyze product data across multiple test operations to find hidden problems is essential in eliminating escapes. If product engineers can quickly perform root cause analysis on an RMA, the source of manufacturing problems can be pinpointed and addressed in hours or days, instead of weeks or months (or longer). The ability to ship only high-quality parts is within reach if the data can be cleanly collected, analyzed and the findings made actionable.
What data sharing can achieve
The ability to share data across the value chain has four overarching key benefits including:
- Lower RMA Costs
RMA management is a constant concern for manufacturers and having parametric data for every facet of the test process enables a speedier resolution to identify issues within parts along the supply chain. Having the genealogy of every tested part also reduces “No Trouble Found” (NTF) rates when supposedly bad parts retuned to the supplier are retested but operate as expected.
- Improved Quality and Time-to-Quality
Time-To-Quality is a critical success factor of every value chain and enabling big data solutions throughout the value chain can reduce time to reach board level DPPB goals and provide an online quality link between chips and boards.
- More Efficient Test Processes
With more information comes better testing. Suppliers can test suspect parts more rigorously and test perfect parts less, reducing the need for expensive additional testing without sacrificing quality.
- Better System Performance
Complete component genealogy enables better pairing of devices within a system to maximize overall system performance, quality and reliability. For example, a memory and CPU can be paired based on similar power consumption or speed.
Information is knowledge and much can be gleaned from collecting and sharing data throughout the automotive manufacturing value chain. As cars become more autonomous, the demand for chips, sensors and electronics in vehicles will grow dramatically, with many more of these systems becoming mission-critical elements. The need to improve quality from DPPM to DPPB and beyond has never been greater with autonomous vehicles set to enter the industry in massive scale as early as 2020. Having complete access to all manufacturing information from ICs to modules to complete electronic systems across the entire automotive value chain can significantly limit defects and can literally save lives.