A late start with predictive maintenance can no longer be compensated for. Important hours, days, weeks, months or even years in which systems would have had time to learn are already gone. Irretrievably lost. Nothing can be made up for. Gone is gone. You should tackle this. Now.
If you think this is bad news, we would say: True. So do not wait any longer. Why? Let us take a step back and clarify the why:
You know this, you have already experienced or gone through all this - depending on the drama: a part breaks down, the machine stands still, you react. This form of reactive maintenance may be suitable for small or not so important machines. But it is risky. Downtimes always cost money. Perhaps you have already gone one step further and started to replace wear parts at fixed intervals? This makes sense as a preventive measure because it can be planned. But maybe it was six months, two years or even five years too early? Then that too is expensive... Or you have been able to produce more than actually planned.
Great order situation. But what if wear parts fail earlier than planned? This is exactly the point where a well-rehearsed system suddenly stops working. Wear does not primarily depend on age, but on how intensively something is used. And you miss out on the digitisation of this wear every time you replace a part because it is two years old. Replaced is replaced, digitisation is missed.
Do not miss the opportunity to continuously digitise the health status of your plants, machines and wear parts - in other words, to record the condition 'just before failure' once and for all. Combined with artificial intelligence, this allows you to predict the condition of wear parts. If you do not do this, you will miss many, many moments of learning and the chance to react early - but not too early. You know that: wear happens over time. Systems learn over time. If you use predictive maintenance, it saves you money, prevents damage to machines and makes you and your company faster. You will see: Over time, you will act more and more precisely - a real competitive advantage.
Opt for Predictive Maintenance. And for good news.
Author: DI. Mag. Markus Loinig
Email: markus@senzoro.com
