Imagine the following scenario: You’re working in the electronics manufacturing industry as an operations manager in charge of overseeing the assembly of cutting-edge devices. One day, you receive news that one of the series of products you’ve shipped to market has experienced a major malfunction, causing an uproar among customers. You frantically try to pinpoint the root cause only to lose yourself in the labyrinth of incomplete traceability data. The frustration mounts, leaving you grappling with the consequences of an ineffective traceability system. But what if there was a way to transform this experience into a seamless operation?
Traceability is the cornerstone of ensuring product quality, compliance, and customer trust, but traceability data as it exists today is not nearly as reliable as one might hope. Enter individual material traceability, an upgraded approach to filling in data gaps, mitigating vulnerabilities, and eliminating potentially costly consequences.
The Challenge
Despite diligent efforts by manufacturers to implement robust traceability systems, three gaps persist between expectations and reality. The first is a date code mismatch. In some cases, the traced date code—the code assigned to a product indicating the year and week it was made differs from the real date code. The second is a production lot mismatch.
These are cases in which the traced production lot the number used to relay the batch of origin—does not reflect the real lot code. This disparity indicates the presence of multiple lots within a single traced lot, which suggests reel design variations may have been grouped and packaged together accidentally. The third is a part number mismatch. A part number reflects an item’s particular rating, voltage, speed, and many of its other qualities, and these parts need to be assembled with compatible ones.
When a mix-up occurs and the wrong components get paired, the functionality of the end product is compromised. To make matters worse, it is very hard to detect this discrepancy during onsite testing. These issues contaminate the supply chain with untraceable components, compromising product quality, risking regulatory non-compliance, and potentially triggering reputational or economic damage. Identifying these discrepancies is crucial for manufacturers to bolster traceability capabilities and improve overall supply chain transparency.
AI and Big Data Close the Loop
Like any controlled parameter, traceability 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 provided manufacturers with the means to visually establish, automate, and verify all traceability data.
With these tools, manufacturers can follow a three-step roadmap to address the shortcomings of current traceability practices. The 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 recommended for high-end and life-critical products like those in the automotive and medical spaces.
With this upgrade, manufacturers can address batch incidents by checking for various defects at the component level with singular precision. The second is to conduct AI-powered visual verification of all top markings on every board, which can even be accomplished without additional hardware.
This allows for a finer resolution of verified information, enabling manufacturers to better distinguish between marking lots and documented supply chain lots. This heightened level of traceability provides a robust foundation for accurate data analysis and decision-making at any point in the lifecycle of any product.
The 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, manufacturers 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 improvements beyond just enriching traceability; they will also be able to assign quantifiable values to their operational efficiency, product quality, and to the capital they end up saving.
A Data Wake-up Call
Insufficient traceability doesn’t just create headaches for OEMs; it affects everyone across the value chain, including suppliers, distributors, and especially consumers. Manufacturers, regulators, and industry stakeholders cannot continue operating under the illusion that conventional traceability procedures meet the moment, especially when the devices in question are used in the automotive, medical, or defense industries where the potential consequences of complacency are far too dire. Taking proactive steps to bolster traceability is the best way to protect the integrity of our most essential electronic devices and the people who depend on them.
The article was written by Dr. Eyal Weiss, Founder and CTO at Cybord.ai
The article was originally published in SMT007 Magazine.
