Issue link: https://iconnect007.uberflip.com/i/1500520
68 SMT007 MAGAZINE I JUNE 2023 ate in markets that demand a detailed trail for every part on every board. Consider military defense systems, automotive soware, and medical technologies, where consequences of a malfunction can be dire. Unfortunately, supply chain disruptions, especially in the wake of the pandemic, mud- died the waters. Average manufacturing lead times increased from three and a half months to nearly a year, forcing OEMs to circumvent their standard suppliers and source compo- nents from alternative suppliers, oen with improper storage standards and subpar materi- als, as well as mixed lots and ambiguous trace- ability information. Although the negative impacts of supply chain slowdowns on electronic components have diminished, assuming there are zero defects across thousands upon thousands of components would be wrong-minded. Given that defective and counterfeit components remain, manufacturers should raise their traceability standards to boost quality, out- put efficiency, and financial clarity, all of which help strengthen the bottom line. Leveraging AI to do this offers the path of least resistance. The Root of the Problem Many industry leaders appreciate AI's capac- ity to improve various phases of the manufac- turing process, but oen miss its potential impact on the traceability capabilities of indi- vidual components. Presently, there are several levels of trace- ability across various industries. In the first level, monitoring relevant equipment parts in the manufacturing value chain is conducted inconsistently. e next two levels involve the use of serial numbers to match PCBs to their associated batch. e fourth and highest level monitors the actual placement of the compo- nents onto PCBs. However, this maximum level of traceability incorrectly assumes that all components within a reel are exactly the same, and therein lies the root of the problem. Level Up ough Level 4 reflects the highest industry standard, many companies have yet to upgrade traceability beyond the first and second tiers; this leads to troublesome gaps in their visibil- ity. Moreover, current lab testing processes are woefully ineffective in identifying compro- mised material, as they rely solely on sampling a smattering of components out of thousands. OEMs should strive to extend their traceabil- ity standards beyond Level 4 to attain complete exploratory traceability. AI-powered visual technology can enable this heightened level of traceability, offering a far more efficient and cost-effective solution than lab testing batches of components. Regardless of where compo- nents are sourced, visual recognition pow- ered by AI can rapidly analyze every compo- nent on every board for authenticity and com- ponent integrity without concern for human error. When implemented, manufacturers can pinpoint any problematic component and conduct rapid surgical recalls on an individual basis with minimal disruption to the manufac- turing process. Just as doctors administer blood tests to assess a patient's health, OEMs need to take the same approach to each and every elec- tronic component. Luckily, AI can transform this otherwise Sisyphean task into one that is far more efficient and manageable. No Extra Hardware Necessary e manufacturing industry needs to embrace AI tools if they truly wish to pioneer air-tight 4A traceability toward a more intelli- gent, efficient, and cost-effective future. All it takes is a SaaS integration with their existing SMT hardware to gain true visibility into what is going on in their products. SMT007 Dr. Eyal Weiss is CTO and founder of Cybord.