Issue link: https://iconnect007.uberflip.com/i/1534953
Yes, if done properly, and this is another variable of the equation. You can know that, for example, a cer- tain machine or line was utilized vs. another. You add that to the equation, to the learning process. For instance, you will know that a machine that pro- cessed X million parts before having scheduled maintenance has a higher probability of misprocessing parts. So, you may want to adjust your main- tenance plans based on the planned utilization and the basic correlation of factors that affect the predictability. As you're describing this, it is clear there's a lot of information coming in all at once. In my factory, who runs this? What skill sets are needed? As you said, it's a journey, and the jour- ney normally starts with a little higher degree of intervention from humans because all the specific learning that must take place. It is certainly the vision that you would simply install a system, and it would be fully automatic and accurately predictive, correlat- ing all these data streams without any intervention immediately. But to get to that vision and make it concrete, some steps need to be taken. That's where the human factor comes in. Under the assumption that all those things are integrated, it is sometimes still difficult for even the most advanced algorithms to ensure predictability. That is where supervised learning models come in, and, by definition, somebody is controlling that. You need experienced people from the factory to support and teach the advanced system the best course of action in a certain situation. You said you need somebody who understands the processes, but you also need somebody that under- stands the system. This is a significant change, but it is also change management. These oper- ators have been working with every- thing manual for their entire lives. Now they will have a system, and they need to understand that the system will help them, not work against them. That's disruptive. We know change is difficult for humans, especially when they have been doing something the same way for 20 years. That could be a big barrier. Another important reason that the organizations themselves need to pre- pare for the introduction of an MES is because, with all the intelligence that an MES can provide these days, you do not want it to be viewed as the enemy by staff. There is a transformation that will happen, people will be needed. But instead of valuable personnel doing less value-added work such as machine changeovers, they will be much better utilized. Their experience will be utilized to monitor and teach the system so that the system can help them do a better and more efficient job. This leads to a conversation around sustainability, where a system like this has a green impact, if you will, that people are seeking. Talk a little bit about how that plays into it. If your system is connected to your ERP and actively collecting and ana- lyzing data, you can set parameters aligned with your sustainability goals— such as using only eco-friendly or reg- ulation-compliant raw materials. With these criteria in place, the system can alert you in real time: "This part is being produced using a component that doesn't meet your defined sus- tainability standards." The character- istics of this component do not match the configuration. Somebody intro- duced some raw materials that are not compliant. The system will prevent these things from happening. It's con- trolling the sustainability of the execu- " " You need experienced people from the factory to support and teach the advanced system the best course of action in a certain situation.