“We have a database of over a thousand plants where we have gone in and checked the costs of spare parts, subcontractors, labor etc. Then we see if they’re up in the right corner. If yes they are spending too much money. We found customers in the down left corner, they’re probably spending too little on maintenance.
Basically the ideal position is somewhere around here (the yellow square), of course dependent on the industry. What we do is, we go in and do this assessment and then we take the customers on a journey to bring them to their best position”.
Those clients that have already started innovating their systems, are now in a more privileged position as they are continuously building on the set foundation. However, those who are about to start now are not late, giving the fact that some systems can be upgraded. “Whether there is old equipment or if there is a good solution we can connect to that one as well, and of course, we are trying to analyze that data with more and more advanced analytics.”
One of the most important tools that Quant used is quantPredict where the company connects the customers’ assets. This is just one part of Quant’s digital toolbox, which ranges from mobile applications, a technical support center to drone inspections.
The technology behind IIoT solutions are based on wireless predictive maintenance. Mr. Hedin described how the advancements in the industry opens a new era for the maintenance:
“Wireless sensor to keep down the cost we connected it to our central collection unit which is based on a PTC product, and in our terminology we call that quantPredict Common Core which is the product that we sell to customers. Prior to that we had no information on these machinery, we just did time base maintenance or reacted when they crashed. Now we’ve been able to connect them and we are able to do less (unnecessary) preventive maintenance.”
An example of IIoT digital journey can be seen at ABB plant in Ludvika, Sweden. A long time ago they’ve started using quantWorx which is a basic tool, only later to be enriched with a full portfolio of Quant tools. This resulted in more efficiency and provided new values to the customers.
Moreover, if we look at one specific machine we will be able to see how the wireless sensors work, the CDO explicated:
“If we take the Wireless IIoT – Juaristi Milling Machine for example, here we had no live measurement, it wasn’t connected to maintenance, so we put in a couple vibrations, air pressure, temperature, operating time, and connected that to a cellular network.”
Connecting the data via cellular network doesn’t impose a serious safety risk as the data shared consists of information such as the air flow fluctuation. Finally, Mr. Hedin assessed the savings potential, and concluded that on the bottom line predictive maintenance is an easy way to save customers’ money.
As the companies abandon the practice of changing their filters every three months and replacing the fully functioning parts, the maintenance teams get a communication channel and more analytics tools that enable them to make fully informed decisions.