Comment prévenir ou atténuer la récurrence des défaillances et améliorer la fiabilité ?

1. precise problem identification Let's see: In an automobile factory, there was an increase in engine failures in new vehicles. Using root cause analysis, it was discovered that the source of the failures lay in a specific batch of defective parts supplied by a supplier. The problem was corrected by changing the supplier's quality inspection process, eliminating the recurrence of failures.

Many confuse root cause with other extenuating circumstances, so you need to avoid that mistake. As an example, a fracture in metal caused by fatigue may not have fatigue as root cause. It could be that the metal was not processed properly, was missing alloying agents, or was damaged by poor workmanship. Ensure that you do the homework, that is, all the detective work to get to the thing(s) that if eliminated, removes the failure mechanism entirely. This is the "root".

Fault tree analysis (FCA) is a quantitative risk analysis method used to find the probability of the root node that leads the system to a failure condition. Where FTA is a method of RCA that uses boolean logic to determine the root causes of an undesirable event. Coming to the topic we can adopt FTA in both a proactive and reactive manner for a better reliable system.

RCA is digging deeper to discover the real reasons why something went wrong instead of just dealing with the surface issues. RCA helps prevent future problems, reduce waste, and make things work better.

In addressing the challenge of preventing or mitigating failure recurrence and enhancing reliability, I adopt a holistic approach that blends the principles of the Toyota Production System with cutting-edge AI technologies. Globally, my strategy focuses on continuous improvement and respect for people, central tenets of the Toyota method. This involves fostering a culture where feedback is actively sought and valued, enabling the identification and swift resolution of inefficiencies. By leveraging AI, I can analyze vast datasets to pinpoint failure patterns, predict potential issues, and automate quality control processes. This not only improves operational reliability but also drives innovation, enhancing company interests.

FMEA is not just about finding what might go wrong; it's a structured approach that prompts us to anticipate, evaluate, and prioritize potential failures in a process.

One of the most challenging parts in conducting of FMEA is to discover new potential failure modes. That is where a creative human side and explorative approaches of going beyond previously known boundaries start to play significant roles to envision potentially new and dangerous failure modes. The documentation part is also crucial in order to keep up with the previously known failure modes, so to avoid as much as possible the unproductive efforts of re-discovery of the previous buried knowledge again and again. Maybe with time one would be able to come up with an algorithm for new knowledge discovery. Then, an implementation of AI approaches would become feasible for the overall process?

Fault trees analysis is invaluable not because it can possibly get you to a root cause but that it makes you (1) think deeper about larger scale interactions (2) see weaknesses in a complex system that you may not know was even there and (3) see how your component in the system affects another.