Submitted by Sheila Kester, Vice President of Operations at TwinThread
Similarly to working with capital assets, having high variations in mean time between failures when servicing thousands of small assets will cause quite a blow to operational efficiency when faced with the prospect of simultaneous multiple repairs.
Being unable to efficiently schedule field-based repairs could represent a serious risk to the financial well-being of your company. The last thing you want is to incur unnecessary costs resulting from multiple intermittent in-field damage repairs because of variation in operation across your fleet.
Fortunately, this doesn’t need to be the case. Implementing predictive analytics will help you to better understand when your equipment is likely to fail. Using a predictive platform to identify dips in performance and slight variations in function can paint your domain experts a picture of when it will be most efficient to conduct preventive maintenance. What’s more, these insights on variation can be scaled. Switching from a reactive approach to a proactive, predictive methodology will allow you to batch maintenance efforts, resulting in asset repairs happening in one trip (instead of several). This means big savings on labor costs. And, not only that. It means greater uptime - freeing your fleet up and empowering you to go after more business.
Variations in operation, if left unattended, will lead to higher labor costs and lower overall pricing for your services. However, with your subject-matter experts working with the predictive operations solution, these variations will dwindle before they ever cause any hold-up in your operations.
Without the ability to identify operational variability, you’ll be forced to take a reactive approach in how you operate. Taking on a preventive approach allows you to take greater control over your operational future.
To learn more about how you can uncover variations that influence preventive maintenance schedules, schedule a demo with TwinThread.