Atlantis Foundries was able to achieve zero defects for three months in a row thanks to machine learning. Here’s why the human component can’t be discounted.
Cape Town is sometimes called the “Silicon Valley of South Africa”. Yet, it was a pleasant surprise to learn that Atlantis Foundries became not only an African, but a global leader in the foundry industry using AI and machine learning to achieve zero defects on the truck engine block castings that they delivered to their customers in Europe and the United States.
There is much to learn from what Pieter shares with us, but I would like to highlight one aspect in particular. It has become fashionable to talk about going “beyond lean” in the rush to market Industry 4.0 technologies. To be sure, these new technologies are game-changing and cannot be ignored, but I think that referring to them as “beyond lean” is the result of a fundamental misunderstanding what lean thinking is about.
Before introducing AI, Atlantis Foundries invested in continuous improvement for many years to improve their casting defect rate from very poor to industry standard. This meant that, by the time they started experimenting with AI, there was already a problem-solving culture in the organization that they could build on. You will hear Pieter refer to AI as another “tool” in the improvement journey. In other words, just like we use 5 Whys as a tool to find root causes, there is now this very powerful tool called AI that can be used in situations where humans cannot cope with the amount of information that needs to be processed.
Like any other problem-solving tool, AI can be used incorrectly or inappropriately if you do not apply lean to the problem at hand. Asking what issue we need to solve has to be the first step, which will inform our problem-solving and make it effective. Atlantis Foundries thought very carefully about this question and that guided them as they figure out how to tap into AI.
This story shows us how important lean thinking is in making a success of new technologies like machine learning and AI. Read full interview.
