Issue link: https://iconnect007.uberflip.com/i/1504794
46 SMT007 MAGAZINE I AUGUST 2023 to a product indicating the year and week it was made—differs from the real date code. e second is a production lot mismatch. ese are cases in which the traced production lot—the number used to relay the batch of ori- gin—does not reflect the real lot code. is dis- parity indicates the presence of multiple lots within a single traced lot, which suggests reel design variations may have been grouped and packaged together accidentally. e third is a part number mismatch. A part number reflects an item's particular rating, volt- age, speed, and many of its other qualities, and these parts need to be assembled with compat- ible ones. When a mix- up occurs and the wrong components get paired, the functionality of the end product is compro- mised. To make matters worse, it is very hard to detect this discrepancy during onsite testing. ese issues contam- inate the supply chain with untraceable com- ponents, compromising product quality, risking regulatory non-compliance, and potentially triggering reputational or economic damage. Identifying these discrepancies is crucial for manufacturers to bolster traceability capabili- ties and improve overall supply chain transpar- ency. AI and Big Data Close the Loop Like any controlled parameter, traceabil- ity data requires a feedback loop for effective quality assurance. But within the electronics manufacturing industry, such a closed loop does not exist. Fortunately, recent advances in AI algorithms and big data analytics have pro- vided manufacturers with the means to visu- ally establish, automate, and verify all trace- ability data. With these tools, manufacturers can follow a three-step roadmap to address the shortcom- ings of current traceability practices. e first is to elevate traceability resolution from batch level to individual material level, in accordance with the soon-to-be enacted IPC-1782B, a standard that is strongly recom- mended for high-end and life-critical prod- ucts like those in the automotive and medical spaces. With this upgrade, manufacturers can address batch incidents by check- ing for various defects at the component level with singular precision. e second is to conduct AI-powered visual veri- fication of all top mark- ings on every board, which can even be accomplished without additional hard- ware. is allows for a finer resolution of verified infor- mation, enabling manufac- turers to better distinguish between marking lots and documented supply chain lots. is heightened level of traceability provides a robust foundation for accurate data analysis and decision-making at any point in the lifecy- cle of any product. e last step involves closing the loop with the previously mentioned feedback in as close to real time as possible. By integrating verified traceability data into the production line, man- ufacturers can effectively close the loop on data handover errors. To that end, typing errors, human errors, and systematic procedural errors can be identified and fixed promptly, ensuring an accurate data trail throughout the supply chain and production process. Original equipment manufacturers (OEMs) who put these measures into action will see With these tools, manufacturers can follow a three-step roadmap to address the shortcomings of current traceability practices.