Invest in Predictive Maintenance?

The purpose of Predictive Maintenance Programs (PMP) is to prevent/reduce unscheduled outage of equipment. The direct business benefits are in: reduced maintenance costs; reduction of breakdowns; and the reduction of maintenance time spent.  Benefits that are tempting … but not all your equipment is ‘eligible’ for a PMP.

Equipment that is not critical to operations or has redundant options – often adds little value when included in a Predictive Maintenance Program. Besides this you have to weigh the investments you make in maintenance initiatives balancing short/medium and long term objectives. Should you:

  • Invest in maintenance vs. investing in new equipment; or
  • Invest in maintenance vs. re-dispatching.

This with a sense of balance keeping a constant eye on operating costs, integrity levels, product quality et cetera. In summary delicate balancing …

Should You Invest?

Yes! … Take the transformational example of Rolls Royce, providing TotalCare services for their engines.  Ranging today from power, to equipment health monitoring and predictive maintenance. A transformational move from manufacturing to services. Daring but one that paid off: 47% of total revenues in 2013 were directly related to their services program.

Others, such as Hyundai, are following in their footsteps: The Connected Smart Ship: “Services are expected to include real-time alerts and warnings, predictive maintenance and more efficient scheduling”.

Your motives to invest do not have to be so radical/game changing. Less transformational examples provide more than enough reason to decide to start investing  in PMPs, some examples:

  • 99% uptime – availability of important assets increasing customer satisfaction1;
  • 50% reduction in maintenance costs from reactive to predictive maintenance on pumps2;
  • Up to 75% reduction in breakdowns2;

Where to invest?

An important step towards a successful Predictive Maintenance Program is the selection of the right asset(s) to focus on. You can take multiple routes and combinations thereof to identify which assets to invest in:

  1. Frequency Route: Based on the number of times specific assets fail you make your selection. This is simple and quick, but also means that all your assets have the same value to you (?).
  2. Criticality Route: Using at hand criticality ranking in your FMECAs you select the assets that are most likely to fail and have the largest implications on health, safety, environment, production, quality et cetera. The danger in this approach is that you may select the assets that have already been fitted with multiple layers of protection, have been over-engineered and will not have shown any or many failures.
  3. Impact Route: This focusses on the contribution of equipment failure to losses. Quantified losses such as downtime, defects, quality degradation, yield and energy losses plus qualified losses such as service-level degradation, damage to product/company image and alike. This is often the best starting point for asset selection.

Selection of the assets is followed by a checklist to determine what asset data is available to start the PMP.  Stepping through this checklist with you allows us to determine how feasible a PMP will be for your site/operations.

Predictive Maintenance graph

Exciting Times

According a recent report by marketsandmarkets the operational predictive maintenance market size is expected to grow at a compound annual growth rate (CAGR) of 26.5% to $1.8 billion in 2020. Other forecasts by ABI Research projects revenues from maintenance analytics to have a CAGR of 22% and a market value of $24.7 billion in 2019.

Growth will be driven by investments in IoT as is indicated in some of the research. But communications technology is just the means by which business will change. Real drivers for the growth projected will be the business value the applications will have. The value can be transformational, efficiency driven or a combination of both. Regardless exiting times as projected spending on maintenance is expected to outperform the healthcare analytics market by a substantial amount (including predictive analytics ;)).

UREASON Uniquely Positioned

Our first Predictive Maintenance project was in 2003 with Siemens Power Generation. In a decade+ we have built valuable experience, a solution platform for Predictive Maintenance and gained a lot of expertise. We can support you in your operations with remote surveillance, diagnostic, predictive and prescriptive analytics software and capabilities.


  • 1: FT Series: Manufacturers reap the benefits of customer service, 2013
  • 2: US Department of Energy O&M Best Practice Guide, 2010
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