PRIZ Academy
Innovation is not just about new ideas—it’s about solving real problems and generating measurable financial value. In our latest PRIZ Academy webinar, industry experts gathered to discuss systematic problem-solving, the challenges in manufacturing and engineering, and how AI-driven tools can optimize production processes.
Innovation is not just about new ideas—it’s about solving real problems and generating measurable financial value. In our latest PRIZ Academy webinar, industry experts gathered to discuss systematic problem-solving, the challenges in manufacturing and engineering, and how AI-driven tools can optimize production processes.
Anatoly kicked off the session by challenging common misconceptions about innovation. He emphasized that true innovation should lead to measurable financial gain and solve real problems, not just introduce new technology. He also explained why failures in engineering systems present valuable opportunities for innovation and why traditional brainstorming methods often fall short.
As microelectronics become more complex, the cost of chip production continues to rise due to additional interconnection layers. Anatoly introduced functional modeling as a way to analyze and improve engineering systems by reducing complexity without sacrificing functionality. Real-world examples included amplifier circuits, vacuum cleaners, and wafer cleaning processes in semiconductor manufacturing.
Manufacturing inefficiencies often arise from unnecessary corrective actions and metrology steps. Richard Platt highlighted the challenge of pushing for changes in well-established industries, while Alex suggested that metrology data should be used to refine processes rather than add complexity. Anatoly proposed a systematic approach to focus on productive operations and reduce waste.
The discussion shifted to real-world examples of AI applications in manufacturing. Anatoly shared insights from the tantalum capacitor production industry, where process inefficiencies were identified and addressed using AI-driven analysis. Examples included:
✔️ Eliminating condensation issues in the furnace by modifying binders
✔️ Addressing cooling system corrosion by grounding components
✔️ Reducing wafer breakage during heating by optimizing gas pressure and boat design
As AI and digital tools become integral to industrial problem-solving, software deployment options become crucial. Alex explained the differences between shared cloud and on-premise installations, highlighting security concerns, intellectual property protection, and support challenges. The discussion also touched on the importance of evaluating solutions based on functionality rather than cost alone.
Arul shared insights from his company, Tennessee Valley Authority, where hundreds of improvement ideas are submitted annually. He explained their cost-to-value analysis method for prioritizing projects. Richard Platt introduced the concept of factoring in the cost of not solving a problem, leading to a broader discussion on preventative measures and their financial impact.
The session concluded with a discussion on structured problem-solving in engineering. Anatoly emphasized that AI assists in the process but does not replace human decision-making. Visualization, systematic analysis, and iterative improvements are key to making meaningful progress. Arul and Anatoly also explored the importance of learning from mistakes rather than striving for perfection.
