OEMs Can Monetize Predictive Maintenance
Improving equipment reliability and profitability can be achieved by gathering data on assets through predictive maintenance and optimizing performance through OEMs. This article shares two models that differentiate business whether it’s through sensor-based maintenance or software-based maintenance.
The ability to perform predictive maintenance on a machine may not be ubiquitous today, but it will soon become a standard offering on packaging and processing equipment.
If an OEM opts not to provide predictive maintenance they run the risk of obsolescence in the long term, said Blake Griffin, senior analyst at Interact Analysis, a global market research firm focused on the entire automation value chain. [Predictive Maintenance] is a trend that is unstoppable.
Griffin was presenting on the findings of a new PMMI white paper Packaging and Predictive Maintenance, during the virtual Executive Leadership Conference on April 19, 2021. Predictive maintenance, is defined by Interact Analysis as a way to monitor a machine, or a component on a machine, to determine when it is likely to fail and to take action to stop it, thus avoiding downtime.
It is a technology built around data architectures and networking, but the definition we gave it [includes the] use of technology to gather data on assets, like temperature or vibration levels, and perform an analysis on that data to predict when the asset needs to be repaired to eliminate the risk of failure, Griffin said. Its always about the bottom line from the user perspective. If you can improve equipment effectiveness, you can improve profitability as a company. Its about risk avoidance and optimization, those are the two key value propositions for predictive maintenance.
It is also a way for OEMs to reconnect with manufacturers to boost customer satisfaction in regards to service, Griffin said, citing another PMMI report from a few years ago that found customer satisfaction with OEM service declined from 42% to 21% between 2015 – 2019 due to an aging workforce carrying a lot of native knowledge retiring and new, less experienced talent coming in. And predictive maintenance is essential in machines where the operation cant be replaced by manual labor, such as filling and dosing, form, fill, and seal, and labeling, decorating, and coding.
So, with 45% of CPGs piloting or already using predictive maintenance technology and another 29.4% evaluating it, now is the time for machine builders to offer it as a service.
There are two different approaches to predictive maintenance: sensor-based and software-based. The first version uses wireless smart sensors to communicate asset health. Once sensors are deployed, then edge computing devices could be tied into the system to transmit useful data to the cloud to do the analytics. The software-based approach, on the other hand, taps into the smart devices already on the machine like the drives and PLC.
If you have a lot of sensing components around the machine it is giving good information on how the machine is behaving, Griffin said. Also safety stops can be indicative of stress levels the machine is facing. The software-based option will need to be flexible enough to adapt to emerging Internet of Things (IoT) platforms which many automation suppliers are rolling out, but in proprietary formats. Dont lock yourself out of a solution by being overly dependent on one. Think about the context of the entire operation, not just the context of your machine.
Ultimately, OEMs will want to make money on a predictive maintenance service, but there are often obstacles in the way of that. For example, their customers may restrict remote access. There is also a fear of cannibalizing revenue associated with service level agreements (SLAs) or the fear of cannibalizing replacement revenue. SLAs just need to be redesigned around a factor of uptime. If you guarantee 95% uptime, you price it based on how much downtime effected the operation, and anything above 95% is charged a premium, said Griffin.
It is also a way of differentiating business, and, as end users see the value they will lift those remote access restrictions. In addition, as predictive maintenance becomes ubiquitous across the industry, OEMs can develop more complementary services or offer completely new business models such as machine-as-service.