Predictive maintenance is not for me…

 

… because that’s not how it’s done in our market
Well, what shall I say? Prepare for the tsunami! While it’s true that switching (fully or partially) to predictive maintenance requires a transformation of how business is done, one thing is for sure: in every (!) market, there’s bound to be a player who moves first. And predictive maintenance has such disruptive potential that the first-mover advantage may be much larger than many executives suspect. The truth in the statement lies in the fact that many stakeholders, not in the least the mechanics, need to be taught how to deal with the prescriptions resulting from predictive analytics. And this – like most change processes – will require determination, drive and  a lot of training in order to make the transition successful. I believe it was Steve Jobs who once said something along the lines of ‘People don’t know they need something until I show it to them’. The same goes for predictive maintenance; it’s not whether the market (any market!) is moving that way, it’s about when and who will be leading the pack.
… because we don’t have data
Aaah, the ‘garbage in, garbage out’ argument. Obviously this is a true statement. But ‘we don’t have data’ typically points to different issues than what the statement says; most often it refers to either the fact ‘there is data but we don’t know where’ (as it’s often spread across many different systems – ERP, CRM, MRO, MMO, etc.), or it points to the fact that ‘we have no clue what you need’. Predictive analytics are associated with Big Data and, while often true that more data is better, this doesn’t have to be the case. I’ve seen many, surprisingly good, predictions from very limited data sets. So what’s key here? Work with partners who can easily ingest data from different sources, in different formats, etc. Also work with partners who can point you towards the data you need; you’d be surprised at how much of it you already have.
… because we don’t have sensors
A refinement on the previous objection and this one slightly more to the point. Many predictive maintenance applications  – but not all – require equipment status data. However, as we’ve seen from the first objection, a full roll-out of predictive maintenance approach may have to overcome organisational hurdles and take time. Therefore, start with the low-hanging fruit and gradually get a buy-in from the organisation through early successes. What’s more, upon inspection, we often find plenty of sensors, either unbeknownst to the client or in situations where the client doesn’t have access to the sensor data (in many cases, they do have access to the data but can’t interpret it – a case where automated data analytics can help!).
… because we don’t have a business case
This is worrying for a number of reasons; not the least because companies should at least have investigated what the potential impact could be to their business of new technologies, business concepts, etc. The lack of a business case make come from a lack of understanding of predictive maintenance and unlike as described in the last argument, a denial of this lack of understanding. As we know by now, one of the main (but not only) benefits of predictive maintenance is the moving of unplanned maintenance to planned maintenance. According to cross-industry consensus, unplanned maintenance is 3x-9x more expensive compared to planned maintenance. The impact of moving one to the other is obvious! However, many companies fail to track what’s planned and what’s unplanned and therefore have no idea on potential savings that could be generated by predictive analytics. Lacking this KPI, it also seems impossible to assess other impacts such as customer satisfaction, employee satisfaction, reliability improvements, etc.
… because our maintenance organisation is already stretched out
Let’s say we have a prediction for a failure and want to move it to planned maintenance (the event is still taking place but it’s nature changes). A commonly heard objection is ‘but my maintenance planning is already full’. Well, what would you do when the failure occurs? In that case, a maintenance event would still take place, only it would either upset the maintenance planning or would be executed by a specific ‘unplanned maintenance’ intervention team. Either way, time and money can be saved by predicting the event and executing the maintenance during a planned downtime. As a matter of fact, moving unplanned to planned actually frees up maintenance time and resources (to be totally correct: after an initial workload increase to catch up to the zero point). The opposite is actually called ‘the vicious circle of maintenance’: unplanned events disrupt maintenance planning, which is therefore often not fully or perfectly executed, which in turn generates more unplanned events, etc.
… because we don’t trust the predictions
Humans are not wired for statistics! While we have a natural propensity for pattern recognition, this talent also tends to fool us into wrong conclusions… Any predictive maintenance (or, by extension, any predictive analytics project) initiative should be accompanied by good training to make sure predictions are interpreted correctly. Even better, business apps tuned towards individual job requirements which translate the prediction into circumstantial actions should be developed and deployed. At the current state of affairs, starting with a limited scope project (call it a pilot or POC), should allow the delivery team to validate the presence of enough and good enough data, build the business case, etc.
… because we don’t understand predictive maintenance
Well, at least they’re being honest! Companies that are aware of their lack of understanding are halfway down the path towards the cure…
Understanding predictive maintenance starts with understanding predictions (see above) but then also seeing how this affects your business; operationally, financially, commercially, etc. Predictive, and by extension prescriptive, maintenance has  far-reaching impact, both internal and external (non-predictive approaches will have a hard time competing with well-implemented predictive maintenance organisations!).
These are, by far, not all the objections we encounter but they give a general idea of how/why many organisations are scared of the currently ongoing change. As with so many technological or business advances, it’s often easier to formulate objections that to stick one’s neck out and back up the new approach. However, as history has shown us (in general and with regards to maintenance as shown by the evolution from reactive > proactive > condition based), maintenance is inevitably evolving this way. Getting it right is hard but not getting it is expensive! We’ll inevitably get front-runners and a flock of successful followers but many of the late starters will simply be run over by more efficient and effective competitors. Business is changing at an ever-faster pace and executives have to be ever-more flexible at adapting to the changing environment. Make sure you get one or more trustworthy partners to introduce you to the concepts and tools for predictive maintenance. At this time of writing, the front-runners are already out of the starting blocks!