The electronics industry’s quest for ever-lower defective parts per million (DPPM) rates across complicated supply chains is a tremendous challenge for suppliers. Customers are demanding less than 1 DPPM per device and have moved to measuring defective parts per billion (DPPB).
So far so good: 2018 hasn’t brought any of the high-profile, electronics manufacturing defects that steadily increased last year. More than likely, however, it’s not going to stay quiet for long as the demands for high-quality and new levels of yield across the electronics supply chain haven’t slowed down.
Whether in consumer electronics, autonomous cars, or IOT systems, every detail of the manufacturing process is becoming “mission-critical.” One small defect at any point in a supply chain can have colossal ramifications. This year, analytics on data pulled from each link in the supply chain will prove invaluable as manufacturers call on vendors take proactive measures to help ensure defects are discovered before they reach the end of the supply chain.
Photo courtesy: Waymo
Automotive manufacturers, in a race to produce autonomous and more IoT- connected cars, may set the precedent for data-driven defect discovery. Simultaneously, the industry is hitting record numbers of defect-based recalls, with 52.9 million recalls in 2016 a massive number that reflects Takata’s airbag recall issues that ultimately drove the company into bankruptcy. The following year the number still reached 28.1 million. Since 2007, amid the demand for advanced, semiconductor-based technologies, the number of recalls based on electronic components has increased fivefold, according to National Highway Traffic Safety Administration (NHTSA recall data). Poorly manufactured electronic components are increasingly leading to skyrocketing amounts of recalls.
The era of the autonomous auto
The staggering recalls can be attributed to an industry that faces the pressures of more complex engineering, reduced product testing times, and increasing costs. The technological shift towards electric and autonomous technologies adds new layers of recall risks.
With autonomous vehicles promising to enter mainstream markets in force over the next decade, the quality requirements for automotive electronic systems are increasing rapidly, and suppliers are already being squeezed to shift from measuring quality in DPPM to DPPB, and amid that manufacturers will lean on vendors to account for individual incidents.
Overall defective product or work is the major cause of recall claims, according to a new report from insurer Allianz Global Corporate & Specialty, and automotive recalls are the most expensive and large-scale due to a “ripple effect.” The report, Product Recall: Managing The Impact of the New Risk Landscape, showed that the average cost of a significant incident is in excess of $12 million and the potential for larger and more complex losses are greater than ever before.
Proactively reducing defect risk begins and ends with data, with an emphasis on the way it is collected, how issues are detected and acted upon before, during and after the manufacturing process. The common practice for any manufacturers, testing components and systems in separate silos, is coming to a rapid end. Today’s supply chain won’t meet the end users’ demand for quality when each player limits test coverage data to pass/fail or “meet/doesn’t meet specs.” Quality issues are only passed on to the next players in the supply chain.
In 2018, we can expect a maturation of data harmonization from every link in the supply chain to take root, with analysis taken from the perspective of the end product. Advances in big data technologies - from Hadoop to machine learning – are available as the backbone to support analysis of large data sets.
Reversing the recall trends will create a demand for newer, deeper levels of data, but it is the transparency and cross pollinating between supply chain players that is most critical. Data alone cannot completely prevent defects, but clearly with the demands of the electronics industry only promising to grow, manufacturers need to significantly increase the likelihood that they will catch and prevent issues before it's too late.