“Houston, we have a problem” must have carried a different meaning depending on whether you were an astronaut on board Apollo XIII, the astronaut’s family, in mission control or a rocket engineer on the Saturn project. While the example seems obvious, many people have a vague idea on where to apply predictive maintenance in their business. When we ask about whose jobs will be impacted we very often don’t get beyond “the maintenance engineer”. And how is said maintenance engineer going to use the predictions?
Let’s have a look at some roles/business fields impacted most by predictive maintenance analytics. And let’s start with the above-mentioned maintenance engineer.
The goals of predictive maintenance analytics are multiple but some of the main subjects are: moving unplanned to planned, better scheduling of maintenance, improved maintenance activity prioritisation. The maintenance engineer really wants to get an improved worksheet which tells him what to focus on during the upcoming activities. Predictive analytics should therefore drive an ‘intelligent’ worksheet, which combines preventive maintenance with prioritised predictive maintenance activities. The Maintenance engineer’s contact with predictive maintenance should involve little more than a revised worksheet.
The maintenance scheduler may get impacted a bit more; instead of spreading out maintenance activities based on counters (time, cycles, mileage,…), predictive maintenance schedules are more dynamic and combine the former (i.e. due to legal requirements) with the predictive information. As a first step, the traditional schedules should be left untouched but activities augmented with predicted visits (improved worksheets). As a second step, the maintenance schedule should be optimised; in fact, predictive analytics isn’t even required for this step but I’m puzzled to never see truly optimised maintenance schedules…! A third step would involve spreading out maintenance visits; if need be, even negotiating with the legislator to allow this within certain limits. This third step will almost certainly require collaboration between OEMs, Operators and MROs.
The reliability engineer is crucial for two main benefits from predictive maintenance: understanding the past and improving the future. The reliability engineer is not just interested in the predictions as such, but really in why the predictions were made. The improved insight should allow the reliability engineer to find root causes, define behavioural patterns (need to find them in order to avoid them), propose solutions, etc. Better insights into what causes certain failures and how well they can be predicted will also allow the reliability engineer to come up with new maintenance scheduling information.
Because predictive maintenance analytics focus on maintenance, people tend to forget the main goal lies outside maintenance: optimal uptime at the lowest possible cost. In my book, uptime means more than just guaranteeing equipment can be operated; it should really mean it’s fit for the task. When a large industrial robot has one of it’s grippers broken but the one required for a specific task works fine, that machine is 100% available for that task, even if from a technical point of view, it’s broken…! Giving fleet planners (machine park planners – let’s use fleet as a generic description) deep insight into fleet health allows them to assign the right machine to the right task.
While the CFO doesn’t generally care about the mechanics of maintenance, they typically do care about the cost of maintenance and operational risk. Predictive analytics can give an insight in both. Maintenance management or the COO may actually be interested in simulating the impact of budget constraints on fleet availability, maintenance effectiveness, etc.
These are just a few examples of the impact of predictive maintenance analytics on different corporate roles; one can easily come up with more. The main lesson of this thought exercise is that PdA impacts the whole organisation, predictions (or derivative information) should be presented appropriately so that every role can correctly interpret the results and apply the best conclusions. Whoever sat through an hour-long discussion between statisticians on the interpretation of a prediction knows what I’m talking about: keep it simple, contextualised and usable!