Root cause analysis

to get to where we need to go.

So I want to dive a little bit deeper just on the root cause analysis component of this, because we can identify failures of systems, and that's great.

But if you just fix the failure and don't fix the problem that caused it, then you're going to have that failure over and over again.

And You know, AI, I think, can help you with that root cause analysis.

Can you talk a little bit about how AI changes the process of root cause analysis from a speed and a quality standpoint to get to where we need to go to solve these problems permanently instead of just band-aid fixing things?

Yes, that's a good point.

In here, we try to help the service and maintenance teams, of course, and also our warehouse customers.

Why? Because the traditional root cause analysis needs to have the evidence between across systems, across teams, across maintenance schedules, different knowledge standards that we have, spare parts and all of that.

Speaking of fixing systemic structures, let’s look at how hospitals respond when things go wrong.

The National Patient Safety Foundation published on moving from RCA root cause analysis to RCA2.

The two stands for action.

Yes, a much-needed evolution.

And most of them had written down near misses that happened beforehand.

And they just didn't do good root cause analysis.

They didn't do good corrective actions.

And boom, it was just waiting to kill somebody.

We have maybe a good start with our summit course that we're putting together.

Get out there and look.

Is the work being done the same way as we designed it to be? Find any discrepancies and start deciding, do we need to take this through root cause analysis, or do we need to put some corrective actions in place to make it easier for them to get the work done? Because everybody's motivated by saving time and effort, and if we don't design that into the system to...

make sure that they are saving time and effort doing the job, then they're going to find a shortcut.

Now this flows onto like what we call the root cause ladder.

Have you guys ever heard of the root cause or figuring out the seven whys? Do you guys ever do that part of your commentary?

Root cause.

Root cause.

I've heard of root beer.

What's that? Yeah, what is that? Yeah, what is

that? Root cause.

Explain.

Of all of the disease, meaning all the violations, whether it comes to the rights of Allah or rights of the creation of Allah, it goes back to denial of the hereafter.

And when it comes on the, uh, flip side, on the positive side, the root cause, you know, RCA, Root Cause Analysis, of all the good and all the khair that, uh, comes into wujud, comes into existence, it is, uh, the belief in the hereafter, belief in accountability, belief of standing in front of Allah subhanahu wa ta'ala, taqwa.

That is why it is repeated over and over and over and over and over and over again, uh, that there is a Day of Judgment, there is a Yawm ud-Deen, there is a Yawm ul-Jazaa, there is a Yawm ul-Hisab, Day of Reckoning, Day of Judgment, Day of Qiyamah, when you will have to stand in front of Allah Ta'ala.

So belief in that is the root cause m-- of all of the khair that will come.

When you realize, man, I keep changing this bearing every – three months or whatnot, and it's not really getting a lot of use, you have to figure out why.

Is it aligned well? Does the role align well? So it's root cause analysis.

I know it's heard everywhere, but I think root cause analysis is the biggest lifesaver of any maintenance tech, maintenance manager, or supervisor.

Do you guys have like a set standard operating procedure as far as like, hey, this is we're doing maintenance on this item X amount of times, like every four weeks we check all this stuff or or, you know, I talked to another company not so long ago.

What they do specifically is they know that they have certain issues or certain reoccurring issues.

What happens when the people who understand them best are no longer there? In this episode, we explore how Tweezer is creating a deterministic blueprint of complex application states across IBM Z, Linux One, and connected distributed environments.

We also look at how that foundation can give AI agents the context they need to perform meaningful root cause analysis and, of course, propose fixes that can actually be tested.

Ohad, welcome to Z in Action.

Thank you very much.

And that's how actually we build legacy, right? Legacy is a result of decades of these types of changes and fixes and improvements getting kind of built one on top of the other.

Well, today, that entire experience looks different.

Today, in the banks that we work with, we enable that same operations team to be able to ask, in the case of an operative event, a nightly job fails, they are able to get a meaningful explanation of not only what just happened here, is what is the root cause analysis? What is the reasoning? What is the business role of this activity? How does that business role impacts other business capabilities and activities, not only at the level of technical meaning, right? Not only if this will not be triggered, then the next one will also be stuck, but rather, these clients or these bankers will not be able to provide them with the right uh credit facility for example right that's a pretty meaningful thing and and they are able to do that by simply asking a question or even automatically getting the trigger of an answer when an incident happens with a fully detailed analysis within minutes now add to that that once they provide that type of insight they are able to provide it back to Ben or to his team because now Ben is no longer the bottleneck.

Ben is only kind of the orchestrator, the architect, the person who decides how good looks like, but he doesn't need to be in every detail to explain every last step that happens within the system.