Issue link: https://iconnect007.uberflip.com/i/1520213
54 SMT007 MAGAZINE I MAY 2024 • Predictive quality: Automated data anal- ysis and insights can reveal that a spe- cific manufacturing process, product revi- sion, or even a product design is showing a high defects per million opportunities (DPMO). AI-powered analytics identify the root cause and recommend actions to be implemented on the factory floor. Continuous monitoring and comparison against benchmarks assess and confirm the effectiveness of the recommended actions. – For example, contextualizing the rich machine data with the materials used in production allows manufacturers to isolate material-related problems, such as date codes, and enact line stops auto- matically, preventing further quality defects. • Predictive utilization: AI empowers manufacturers with foresight into poten- tial production shortfalls. is allows for proactive measures to minimize delays and disruptions. By analyzing various data sources like production targets, routes, schedules, and machine data, AI can identify areas for improvement and suggest targeted interventions on the shop floor. ese insights lead to continuous improvement in process efficiency and cost savings from reduced downtime and resource waste. – For example, planned run times can be validated against genealogical and historical data. is allows manufacturers to identify golden run times, enable more accurate planning, ensure continuous product flow, and for under-utilized equipment to be transferred to alternate lines. • AI-driven supply chain optimi- zation: AI can analyze historical demand patterns, production data, and real-time market fluctuations to optimize supply chains. is can help elec- tronics manufacturers ensure just-in-time delivery of components and prevent stock shortages, ultimately leading to smoother production processes and reduced costs. – For example, when a component runs out, manufacturers can contextualize attrition levels against lead times from supply chain information, enabling them to set specified attrition thresholds to reduce material shortage downtimes. ese are just a few examples, and the pos- sibilities are constantly expanding. As tech- nologies evolve and partnerships within the ecosystem strengthen, we can expect even more innovative AI applications to rapidly emerge. By fostering collaboration within the man- ufacturing ecosystem, solution providers can empower electronics manufacturers to unlock the full potential of this transformative tech- nology. The Human Element and The Road Ahead While AI automates certain tasks, human expertise remains critical. AI excels at data anal- ysis and pattern recognition, but human judg- ment and problem-solving are irreplaceable. However, AI can and should augment human workflows, freeing workers to focus on higher- level decision-making and continuous improvement initiatives specific to their organizations. The powerful combination of human and machine intel- ligence is the key to achieving true manufacturing excellence. AI systems now constantly ana- lyze data to automatically iden- tify and diagnose problems on the factory floor, including those pre- viously noted for automated root cause analysis and predictive utili- zation, and support guided play- books with corrective actions.