Corrective to Preventive to Predictive Maintenance & Technology
It used to be that things break and people would have them fixed. If your car stopped working, you would have it towed and then a mechanic would assess it, get the spare parts and rebuild it at a premium. Then, we started getting into the visual management portion of things with sensors and controls warning you that things were out of tolerance and therefore something should be checked. Your oil level is low and therefore you get a light on your dashboard indicating that you should check it. This worked very well in specific applications like your car however in complex manufacturing and heavy capital environments that was not the case.
With the digital era, factories and buildings are getting smarter and more efficient. Load monitoring of your electric consumption via SEM3 modules is being required by code in specific areas of the country in order to give processed data to the user to make smarter and more intelligent decisions. Mindsphere is being used to gather data from equipment and PLC’s to understand when the equipment is running outside of the control limits and therefore alert the user that something is going on and should be checked. It is no longer only about fixing equipment that crashes or doing the preventive maintenance on it, is about getting big amounts of processed data to the end user in a way that a decision can be quickly made. Per Siemens vision 2020 we’re facing the 4th industrial revolution with the IoT.
The possibilities are endless here, the question is how much can be done and how quick can this be implemented. The truth is there is of course the hardware cost but really the big one is the software cost and specifically the subscription portion. All these require potentially consultants to develop a custom solution and/or experts working at the enterprise level developing all kinds of apps and integrating them to the business. The truth is even though this has been on the works for over a decade, the knowhow, the expertise and the industry buy in is still not there. The other challenge is how to sell something this technical to non technical people, the business cases exist however proving there is a correlation between the results vs the investment is hard given that there could be multiple factors driving the result.