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52 SMT007 MAGAZINE I MAY 2024 ...AI can predict potential equipment failures before they occur. talization plan, one that includes a specialized approach to data as well as alignment and com- mitment from internal teams to the successful application of it. From Data to Actionable Insights: The Power of Analytics Modern factories generate a wealth of data from machines, sensors, and soware systems. But the true power lies in transforming the data into actionable insights to drive tangible improvements. is requires a seamless infor- mation flow, from rich machine data to insight- ful dashboards and, ultimately, actionable steps on the shop floor. Here's a breakdown of the ideal data-to-action cycle: 1. Rich machine data: Capture comprehensive data from all aspects of production. 2. Standardization and broker- ing: Ensure data standardiza- tion through a central broker, allowing for seamless integration across different systems. 3. Cloud analytics: Leverage the power of cloud computing for advanced analytics and AI model training. 4. Insightful dashboards: Present insights and key performance indicators (KPIs) in a user-friendly and visually appealing manner. is allows potential bottlenecks, equipment failures, quality issues, etc., to be identified in real-time. 5. Intelligent actions: Bring corrective actions to the surface, complete with AI- guided playbook recommendations, and enable annotation by factory experts— allowing excellence anywhere in the orga- nization to become excellence everywhere. is approach to data unlocks exceptional opportunities both immediately and in the lon- ger run. Genealogy and rich machine data can be harnessed to automatically provide valuable insights for operational decisions in the man- ufacturing process. With the help of advanced algorithms and machine learning techniques, this data can be analyzed in real-time to iden- tify trends, detect anomalies, and predict potential issues before they occur. Examples of how advanced data techniques are unlocking proactive problem-solving: • Automated root cause analysis: Tradi- tionally, identifying the root cause of a quality issue can be a time-consuming and laborious process. AI can analyze data from various sources, including machine sensors, inspection sys- tems, and process data, to auto- matically identify and diagnose problems on the factory floor. is not only reduces resolu- tion times but also empowers manufacturers to prevent sim- ilar issues from recurring in the future. • Predictive maintenance: By ana- lyzing sensor data from machines, AI can predict potential equipment failures before they occur. is proactive approach to maintenance allows manufacturers to schedule repairs during downtime, mini- mizing production disruptions and maxi- mizing equipment lifespan. ese kinds of insights allow businesses to optimize their operations, increase efficiency, and reduce downtime. Furthermore, these insights can support strategic decision-mak- ing, enabling continuous improvement and innovation in the manufacturing process. For example, with the rich data from all machines along the route, defects found in the testing process can be automatically linked back to placement machines, specific components, recipe instructions, manufacturing equipment, etc., and identified with remarkable accuracy. With this framework in place and intelligent actions now flowing, manufacturers can sit