AI: Ending the "he said, she said" of supply chain quality control
In the complex world of manufacturing, distinguishing between defective and non-defective products can be a subtle art, often leading to significant disagreements among stakeholders and impacting production line efficiency. This presentation delves into the transformative role of artificial intelligence in refining quality control processes, with a spotlight on Zetamotion’s cutting-edge AI solutions.
We'll kick things off with an interactive visual quiz, inviting the audience to identify defects in a series of images. This engaging approach not only highlights the subjective nature of traditional defect detection methods but also sets the stage for discussing the vital role of AI in standardising quality assessments. To further illustrate the limitations of human perception versus computer vision, we plan to incorporate optical illusions.
The presentation will then shift to examine catastrophic events stemming from quality control failures, emphasising that the devil truly lies in the details. We'll explore how persistent quality issues affect not only the integrity of products but also workplace culture, introducing factors like fatigue and stress that are prevalent in the manufacturing sector. Here, we will engage the audience with a question: “What quality control challenges are you facing?” Responses will be gathered using real-time polls via Mentimeter, fostering a dynamic interaction.
We will conclude with a discussion on the key advantages of integrating AI into manufacturing quality control, demonstrating how Zetamotion can help businesses fully realise their production capabilities. A video simulation of our solution will provide a concrete example of how our real-time analysis and automated defect detection significantly enhance manufacturing processes.
The session will wrap up with an interactive Q&A, allowing attendees to discuss the implications of AI in manufacturing, share their experiences, and explore potential applications in their own areas of work.