To really grasp the benefits optimized pricing adds to a product uptime-driven model, we have to first recognize the true cost of downtime. Downtime can generally be defined as a period of time when something such as a network, factory, or piece of machinery is not in operation – specifically as a result of some sort of failure or malfunction. And if you’ve ever had one of these things break down at a critical phase of a project, then you know exactly how that downtime can impact your schedules, your brand reputation, and your overall profit potential.

And, speaking of profit potential, True Downtime Cost® (TDC) includes all the business support costs dedicated to maintenance and repair time, added to the potential lost business opportunity costs. From tangible costs like lost capacity, lost production, direct labor and inventory, to the intangibles like responsiveness, stress, and reputation, some TDCs can be difficult to earn back.

This costly impact on after-sales service operations is making it crucial for pricing teams to be proactive when it comes to maximized product uptime and a deeper focus on servitization, the shift from product to outcome-based service. That’s why we created our newest Syncron original Orange Paper, Pricing for Product Uptime: Navigating the Most Disruptive Change in Pricing History. 


So, what’s at the root of this TDC? In many cases, it’s the manufacturer’s reliance on a preventative maintenance model rather than a predictive maintenance model. While the two seem similar, preventative maintenance is actually just maintenance based on fixed schedules like time, events, and meter readings, and it’s a known method in many manufacturing maintenance processes.

From Preventative to Predictive Maintenance

Predictive maintenance, however, can be realized through the application of sophisticated machine learning techniques, and it’s now the new standard for reducing cost, risk and lost production in manufacturing. “With accurate predictive maintenance tools,” says Arimo, “manufacturers can choose to do maintenance only when needed, avoid costly or dangerous unplanned downtime, or schedule repair and maintenance personnel and resources more efficiently.

This trend of shifting from preventative to predictive maintenance to offset the true cost of downtime is catching wind. Just recently, United Technologies Corp. (UTC) announced their dedication to scaling their digital and data analytics capabilities across the business, by opening a Brooklyn office dedicated primarily to developing predictive maintenance applications for jet engines and smart-building tools.

According to data pulled from sensor-equipped aircraft, the company’s aerospace segment has taken past examples of unscheduled maintenance, run retroactive analytics, and found that, had predictive maintenance strategies been in place, “we would have known 25 out of 26 times that [part failure] was coming,” says Vince Campisi, UTC’s senior vice president for digital and CIO. And, predicting part failure before it happens is at the core of maximizing product uptime.

Applying Data – The Right Way

Data and its proper application, however, is a common roadblock in organizations struggling to make this same change today. According to Don Fitchett, founder of Business Industrial Network, predictive maintenance and analytics like TDC come from “analyzing all cost factors associated with downtime, and using this information for cost justification and day to day management decisions.” But in the case of many manufacturers, “this data is already being collected and simply needs to be consolidated and organized according to the TDC guidelines.

And, back in the UTC Brooklyn war room, data scientists are working with internal data across the company, as well as external data (think technical specs like weather and air quality), to determine factors that may lead to unplanned maintenance. For example, one analysis found that air quality correlated strongly with part degradation, which could have been useful information for how to rotate fleets in order to reduce downtime and save on this costly break-fix style of maintenance.

We’re turning away from that break-fix way of thinking, and headed into a much more uptime-focused approach to service, with predictive technologies and strategies at the core. Download your copy of our Orange Paper today, and start putting these pricing strategies into play, and place product uptime at the forefront of your after-sales service operations.