Recently Copperberg has organized a webinar dedicated to IIoT. Among others, we’ve had a chance to hear about new digital maintenance perspectives from an industry leader’s executive.
As a company with almost 3000 employees in 25 countries, Quant is a global leader that keeps pushing the boundaries. Their CDO Olof Hedin, told us about their latest tools and solutions that open a new era for industrial maintenance. We’ve heard about specific machines, costs, and time needed for setting up the predictive maintenance system.
Quant’s digitalization journey has always been centered around innovative automation solutions and the process of discovering ways of generating new areas of income. This reflected their own work, as well as the work they do for their customers. Mr. Olof Hedin, the company’s CDO clarified that IIoT solutions don’t always demand complicated transformation processes within a company. As a matter of fact, it usually takes up to one month until the new technologies, both hardware and software, are up and running.
The possible improvements in manufacturing can be identified by experts in the field based on a combination of operational assessment and benchmarks. “Quant calibrates performance targets against benchmarks and sets challenging yet realistic goals for their customers”, explained Mr. Hedin. An example of the study is seen in the picture below.
“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 is based on wireless predictive maintenance. Mr. Hedin described how the advancements in the industry will open 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 machineries, 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 the 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.