Predictive maintenance is almost all about data, software, etc. and is therefore for many maintenance departments very far from their natural habitat. This scares off many, but in reality it shouldn’t. Modern businesses can no longer function with hard walls between departments. What’s important is that every department knows how what they are doing influences other departments’ work. This also means that CIO’s have to be more business experts than ICT experts. Here’s a quick overview of the main steps towards predictive maintenance.


Machines generate data – more and more of it. Some business leaders are surprised to find out they have a machine park that has been generating data for many years. Only, because they didn’t find a use for it, the data was just stored or (alarmingly often) just dumped… Collecting data requires very little infrastructure (most of it is done in the Cloud). Sometimes, equipment requires retrofitting with sensors and data communications capabilities; many IoT or M2M solutions exist today, both from the OEMs and from third party vendors.


Once it is collected, interpreting it and presenting it in a meaningful way, turns data into information. Many cases are known where just this step allows companies to save such huge amounts, we’re often told they can’t believe they didn’t take that step sooner. Certain patterns, exceptional behaviours, etc. quickly become apparent through a good representation (our brains are wired to recognise patterns – which also sometimes leads us to taking wrong decisions…).


While BI (Business Intelligence) tools could slice and dice through data and help with the representation, prediction is a whole other matter. Why has prediction come to the forefront recently? Well, the first step – collect – has gotten a big boost through Big Data and the second step – represent – has gotten really powerful at analysing huge amounts of data with multiple dimensions. So people naturally started wondering not just about the past but whether they could infer future behaviour from the data they were collecting. A vast library of algorithms is available and we’ve got the computing power. The hard step is really to come up with the appropriate algorithm for every situation – and that situation keeps changing all the time (just read some of our other blogs to see how we coped with this).


Predictions are good but what should you do with them? There has to be a framework to help users take the right decisions; for their own requirement but relative to corporate objectives. Just reacting to a prediction may have huge negative impacts somewhere else in the company. Solutions should therefore be designed with subject matter experts and business experts in order to come up with correct decision support tools (based on the predictions but also weighting other impacts – operational and financial).


From the optimise step, actions are proposed, which then need to be executed. These maintenance actions set in motion a series of events (from calling the mechanic to ordering spare parts). A close integration between the optimisation and execution steps is necessary to avoid launching unnecessary activities (i.e. sending a mechanic when the spare part is not available) and creating an alternative sequence of events.

As one can see, IT permeates the predictive maintenance environment. But it should remain very clear: maintenance expertise still sits at the core of the solution. IT is merely an enabler. In order to avoid it becoming too much IT oriented, solutions should be designed with the end user in mind. The reliability analyst will have very different needs than the mechanic for instance. Tuning these solutions towards user needs requires a strong involvement of all roles during the design and training phases of a predictive maintenance project. Training should also extend to upper management who need to be informed on what to expect (both in general and in particular with regard to their equipment/data/use/…).

Looks complex? Well, it isn’t. Today, solutions exist for each of these steps. Most crucial for successfully implementing a predictive maintenance approach is bringing together the right partners – but that’s a discussion point for another blog.