Across organizations, it is common to see curtailed capital expenditure spending, especially on expensive assets. With ‘everything as a service’ models becoming rampant, at several places, these capital expenditure items are already making way for working capital per month costs or per usage costs. Whichever way you look at it, it makes sense to exploit and fully utilize expensive assets. Downtime can be expensive, in pure play terms as well as based on the impact it may cause to the overall activities.
To prevent downtime, predictive maintenance has been around for quite some time now. In most instances, though predictive maintenance is done post-facto when the asset is brought in for servicing, or it is already causing some problems. Leveraging Internet of Things (IoT), a lot of this predictive maintenance can be on-going and can ensure higher utilization and availability of the asset. Today predictive maintenance is all about detecting the problems & errors before they occur. Traditional business intelligence (BI) is no longer enough.
Today there are a number of products & tools providing real-time analytics. These comb through vast quantities of data and finds outliers all at rapid speed, providing businesses with greater insight without the usual time delays. The promise and potential are huge. Several manufacturing industries like automotive, off-highway, industrial equipment are starting to link this vision of fully automated factories.
Managing up-time challenges for a manufacturer of off-highway vehicles – A case
For a company manufacturing off-highway vehicles and forklift trucks, managing up-time of these vehicles was never an area that they were directly involved in. But with customers expecting high up-time, it was critical that they got in and worked on a solution that would minimize downtime for its customers. This meant additional cost and planting additional electronic devices, which will track and monitor key performing metrics of the vehicle. But the value that this created for the customers was huge. Based on the parameters captured, data was analyzed at the back end and service reminders were created for the customers as well to the nearest service center. Based on this, the vehicle could be brought in and refurbished without causing any unplanned downtime.
Reducing downtime of machines, apart from saving a lot of time is synonymous with creating more revenue opportunities for the organization.
We agree on one thing though, that for most organizations, the path is not entirely clear. Depending on organization policies, there may be security concerns, challenges over what data should be sent and collected and of course bringing in some standardization among different locations.
Knowing that machine is going to have a problem, being able to prevent it before it occurs, or before the problem has become severe, is something that will definitely increase the productivity of every machine. When all of your machines are running on 100% productivity, it’s an ideal situation.