4 Things to Consider Before Predictive Maintenance Programs

July 30, 2014

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Industrial companies have realized that it pays to be sustainable, literally. It is not just a noble act; we want to do it because mitigating energy and water waste is mitigating costs and improving profit. Here, LNS Research describes how predictive maintenance equals to sustainable manufacturing.

One of the things LNS Research has explored recently is the direct relationship between predictive maintenance and sustainability performance. While perhaps not a commonly drawn linkage, research shows that the correlation between how we manage our assets and overall sustainability metrics is quite clear. Indeed, industrial companies have realized that sustainability is important not just for intrinsic environmental reasons but that it makes the most business sense, as well.

However, it’s one thing to want a predictive maintenance model and quite another to actually implement it in a manufacturing environment. At times it can seem overwhelming. There is so much data to draw on, and, between the rise of Big Data and the Internet of Things, it can be daunting to consider how to achieve true predictive maintenance that is above and beyond the preventive maintenance widely practiced today.

With that, let’s look at four key things we can do in striving to implement a predictive maintenance approach across the organization and, at the same time, consider how these ideas correlate to overall sustainability performance.

1. There is no turnkey solution you can buy. When we look at many other enterprise software solutions, be they enterprise quality management software (EQMS) or manufacturing operations management (MOM), we have many existing best practices and very defined solutions we can implement to our existing processes. Sure, we often need to make a tweak here, or a customization there, but all told, a lot of these solutions speak to longstanding and widely adopted management standards and best practices.

It is not so much the case for predictive maintenance. At this point, we can’t seek out a vendor, implement a solution, and expect that -- voila -- somehow we will have an effective predictive maintenance model across the enterprise. Every manufacturing organization is unique, from the specifics of individual fixed assets to the complexities of machine-to-machine interactions, so there is no template we can simply apply and say, “Here, this is your predictive maintenance.”

Nevertheless, it is a very achievable journey to get there. At the core of many predictive maintenance systems we see today is enterprise asset management (EAM) software that serves as the main enterprise application to manage maintenance activities. Add-on applications are then typically used to develop the analytics. These are not turnkey solutions, but they are examples of current predictive maintenance models and serve as a knowledge base that can be adapted to specific assets over time.

2. Pick the right assets for predictive maintenance. There are essentially three categories of maintenance in manufacturing: break-fix (reactive), preventive, and predictive. Using a domestic example, a light bulb in your home, the three are illustrated below:

Reactive: This equates to replacing a light bulb after it has gone out; it is a fairly standard practice among most people, and one that makes perfect sense in the home environment. However, being reactive is far from optimal in any manufacturing environment, as critical machinery needs to be operating to spec at all times. The costs associated with unplanned downtime can be disastrous.

Preventive: This would involve replacing all light bulbs in the house simultaneously to ensure continual working order. While such a move makes little sense, it is the mainstay of the mature manufacturing environment. Instead of waiting for assets to fail, they are replaced at regular intervals, mitigating the need for ad hoc responses to one or more failures and reducing their associated costs.

Predictive: Taking preventive maintenance one step further, what if there were a way to accurately track the fading of each light bulb and determine when each is about to fail? As not all bulbs are used the same amount, we could extract maximum value from each bulb before replacing it with minimal interruption. While this again would amount to overkill in your home, the energy and cost savings in a large manufacturing environment are considerable.

Using these three classes of maintenance can be a great way to define predictive maintenance priorities. Look around your manufacturing environment and consider which equipment you would treat on a reactive basis as opposed to a preventive or predictive basis. MORE FROM LNS RESEARCH: A Best-Practice Guide to Environment, Health, and Safety (EHS) Performance

We have seen companies try to provide the connective tissue between asset performance and preventive maintenance, but as we say, there is still a long way to go to any sort of turnkey solution to predictive maintenance. Such solutions could provide a checklist that you can map against your own specific requirements, but you really have to start from ground zero, especially if you have no preventive maintenance program in place.

So start by categorizing your assets according to the three maintenance categories. Begin with the assets that would benefit best from a predictive maintenance approach. These are the ones you will want to start with, especially if you are trying to demonstrate quick wins with senior management, from whom you may require endorsement for a more comprehensive predictive maintenance program.

3. The overwhelming goal is to maintain “optimally.” Going back to our everyday consumer example, the light bulb in your home, although replacing all the light bulbs in your house at a regular interval might be considered responsible, it would ultimately be foolish, with the fact that some would had been barely used while others would had been used regularly. Likewise, we could also use predictive maintenance to analyze the fuel efficiency and oil quality in our cars, but only to replace the oil every month, as opposed to the manufacturer-recommended 7,500 miles. That would be costly and inefficient.

It is the same in manufacturing. “Over-maintaining” can be as bad as insufficient maintenance.

With complex machinery, things go wrong. We have to tear equipment down from time to time, re-inspect it, and figure out what went wrong. Sometimes, things don’t always go back together as planned. The easy answer would be to over-maintain. However, as with the car analogy, consider the cost of putting in oil every day, to use an extreme example.

The payback in preventive maintenance is not just trying to avoid failure; it is trying to avoid over-maintenance, especially from a cost perspective.

4. Recognize the relationship between predictive maintenance and sustainability performance. For a moment, forget about the role of a sustainability program for sustainability’s sake. The whole point of sustainability from a business/stakeholder perspective is that when we and our assets consume excess energy and water and generate excess emissions and waste, we pay for it. We don’t want to simply manage these impacts because they are bad for the world -- even if this is a noble intent.

We want to reduce these impacts simply because it is bad for business to be wasteful. And in the ongoing goal of achieving operational excellence by mitigating consumption and waste, what would be a better way to start than by implementing predictive maintenance programs to operate profitably?

Asset performance will ultimately have a bearing on overall sustainability performance. For example, if a specific piece of equipment is under-maintained and using more energy, this will impact overall energy use directly. If a few vehicles in your fleet haven’t been maintained properly and have poor emissions as a result, guess what? That factors directly into overall emissions and asset safety key performance indicators when it comes to sustainability reporting and overall sustainability performance.

With an established tie between predictive maintenance and sustainability, begin by tying these two operational strategic objectives together, especially if your predictive maintenance program is in its infancy and your organization has, or plans to implement, a sustainability program.

Top photo credit: LNS Research  Paul Leavoy is a research analyst for LNS Research, which provides unbiased benchmark research, data, and analysis to improve business performance. Based in Cambridge, Mass., LNS Research focuses on providing insights into the metrics, leadership, business processes, and technology capabilities needed for achieving operational excellence. Its free report, Enterprise Sustainability Management: A New Paradigm Emerges, details the evolution of sustainability frameworks and provides recommendations on moving forward in this area of enterprise operations.  
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