Predictive Maintenance – What’s stopping us? [Part 1]

When hype meets reality – that’s when we all need to listen!

Working in the software industry, you are constantly challenged to innovate and look into digital opportunities. On one side, there is the hype and on the other, you have your customers asking you to develop. When customer demand intersects with hype, that’s when you have to listen very carefully.

Digital opportunities

Predictive Maintenance (PM) is one such area. The promise of big data and the Internet of Things (IoT) is currently a focal area in the asset intensive oil and gas sector ─ and for good reason. The oil and gas industry is turning every stone to see where and how they can cut operational and capital costs. Many of these cost-cutting exercises result in new ideas for optimizing asset management.

On a general level, we seem to be looking at very big numbers. The global consulting firm, McKinsey, is one of many optimistic analysts saying:

“A major source of value—potentially more than $360 billion per year [across all worksites (i.e. across all industries)] — would be improved equipment maintenance. Using sensors to monitor the health of machinery in use, companies can shift to a condition-based maintenance model (maintaining equipment when there is an actual need rather than relying on a regular maintenance schedule or repairing equipment only when it breaks down.”[McKinsey Global Institute, 2015: Unlocking the potential of the Internet of Things]

Determining the value of Predictive Maintenance

Only a few years ago I would have dismissed these numbers to say that it doesn’t critically impact how IFS should respond. There is still a disbelief in the documented value of predictive maintenance and this seems to be rooted in the fact that we still lack the right metrics to measure it. Nonetheless, what costs are we saving?

• Counting the lifetime cost of an asset?
• Reduced man-hour cost of carrying out maintenance?
• Reduced cost of replacement parts?
• Reduced inventory?
• Reduced risk of failure and how do you measure this?

A business case will vary from situation to situation and will also depend on the amount of time you’ve allotted for measuring any impact. Some require quick, cash-based returns and others may take a broader approach and incorporate non-cash elements.

Addressing barriers to maximize benefits

The barriers to realizing the benefits can be considerable, too. The most notable barriers, as far as I can see, can be summarized as follows:

1. Technology: the cost of installing RFID tags, etc.
2. Interoperability
3. Intellectual property
4. Security and compliance
5. Organization and talent
   • Available resource pool
   • Both onsite staff and planning/engineering department

In discussing with industry peers, I see that we can quite efficiently address 1-3. These are technical in nature and can be solved when you put your mind to it. Compliance with third party/government institutions may be a hindrance in some cases, as regulators and classification societies see Predictive Maintenance as still being in its infancy. Still, with the right attention, I am confident regulators and classification societies will listen and develop along with you.

I am therefore left with the last barrier of organization and talent, which I suspect may the trickiest barrier to overcome. Changing the framework and work practices for such a critical domain as maintenance may easily prove difficult to drive bottom up. A strong leadership is therefore needed to drive changes and motivate the company in a new direction.

So, even if the value of PM in some instances is still regarded with disbelief and the barriers preventing change seem high, we do seem to be at the point where we should start to act.

Taking action

Clients who evaluate their Computerized Maintenance Management Systems (CMMS) / Enterprise Asset Management (EAM) systems are unanimously saying to IFS that we need to make sure we can evolve into a framework of PM. This is no longer hype, but something we need to address.

A telling example is the direction in which GE seems to be heading:

GE’s engage Drilling Services offering enhances BOP system availability by transferring the maintenance and service of pressure control equipment to GE. This includes on-rig GE personnel, management of parts, overhaul and repair, continuous certification, data monitoring, and management of change. This new arrangement is a performance-based alliance that leverages the scale of GE data, predictive analytics, insights and continuous certification, positioning the company as a long-term commercial, operational and technical partner.

—Drilling Contractor, Feb 8, 2016

My point is that changes are already happening within our industry. Some do this by themselves and some outsource parts of it to partners. When hype meets reality – that’s when we all need to listen!

In my next blog, I will detail how IFS is dealing with Predictive Maintenance and IoT. We see IFS playing a large part in delivering the value of this change. Stay tuned.

Do you have questions or comments about predictive maintenance?

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