Operational Excellence is a term that is increasingly used in discussions about machines used by our clients. However, the exact context and implications of the OE of the actual equipment is often an undefined requirement. As an integrated part of our equipment solutions, the Selmers Plant Management System (SPMS) can provide this context to plant owners and managers at all levels.
Selmers’ Area Sales Manager Ramon Keizer is quite enthusiastic about this development. ‘SPMS is a platform that we developed years ago because more and more customers started to work in a data-driven way and increasingly want to make decisions based on data,‘ says Ramon. ‘Although that data is, or used to be, primarily focused on minimizing downtime and optimizing forecasting of maintenance, among other things, there is much more that can be done with that data. With SPMS we add a context to the data that is extracted from equipment through, for instance, PLCs. We always discuss the desired context in detail with the customer beforehand. This ranges from quality and pain points to capacity aspects and incident monitoring.’
‘You can think of things like processing time of a product per station in the production process, the position(s) of the product and the transport time from one station to the next. But also the relevant key process data collections as well as environmental data such as ambient temperature and humidity and incidents that have occurred can be very useful.’ By placing sensors at critical points in a production line, even more relevant data can be collected and data trending can be analyzed. In fact, with SPMS we follow each product during its journey from raw material to finished product and delivery. Of course, the automated recording of data is not only much more accurate and efficient than manual counting lists, but ultimately creates a logbook, or “digital thread”.
Patterns and trends
‘Additional scanning of individual products and providing them with a QR code allows for individual logistics and processing, thus creating a digital thread. By comparing historical data with planned/similar scheduled projects, certain trends or patterns can be brought to the attention of our clients which allows for early allocation by the project teams. For example, if you can estimate the time of maintenance more accurately, you may combine that downtime with other work on the line. As a result, you’ll have less frequent downtime.’
‘At the same time, all this data also exposes our own performance related to the equipment we have supplied. Provided we get permission from customers, we can use their enriched data to optimize our own performance as well. In some cases, for example when it turns out that the power consumption is too high or too much air is extracted, a different setting can be more appropriate for a particular customer situation. Finally, it is also possible to set an alarm or threshold when a certain parameter drops below the KPI value or threatens to do so.’