As other domains such as procurement, supply chain, production planning, etc. get increasingly lean, attention focuses on the few remaining areas where large gains are expected from increasing efficiency. Fleet uptime or machine park uptime is thé focus area today. Indeed, investors increasingly look at asset utilisation to determine whether an operation is run efficiently or not. As we know, in the past many mistakes have been made by focusing on acquisition cost at the cost of quality. This has led to a lot of disruption with regards to equipment uptime which, in turn, renders inefficient any of the lean initiatives mentioned above. So, what are the important factors determining uptime? We’ll look at the two most important ones:

– reducing the number of failures

– reducing time to repair (TTR)

Reducing the number of failures sounds pretty obvious: purchase better equipment and you’re set. Sure, but how do you know the equipment is better? Sometimes, it’s easily measurable; i.e. I’ve known a case where steel screws were replaced by titanium ones. Although the latter were maybe five times more expensive, their total cost on the machine may have been less than 1,000$ whereas one failure caused by a steel screw cost 25,000$. Taking an integrated business approach to purchasing saved a lot of money over the lifetime of the equipment. In other cases, the extra quality is hard to measure and one has to trust the supplier. This ‘trust’ can be captured by SLA’s, warranty contracts or even fully servicized approach (where the supplier gets paid if and when the equipment functions according to a preset standard).

Number of failures can also be reduced by improving maintenance; pretty straightforward for run of the mill things such as clogging oil filters, etc. One just sets a measurement by which a trigger is set off and performs the cleaning or replacement. This is what happens with your car; every 15,000 miles or so certain things get replaced, whatever their status. The low price of both the parts involved and the intervention allows for such an approach. Things become more complex when different schedules need to be executed on complex equipment: allow all of the triggers to work independently (engine, landing gear, hydraulics, etc. on a plane for instance) may cause maintenance requirements almost every day. At least some of these need to be synchronised and ideally, the whole maintenance schedule should be optimised. Mind you, optimisation doesn’t necessarily mean a minimisation of the number of interventions! It should rather focus on minimising impact on operational requirements.

In order to further reduce the number of failures, wouldn’t it be great if we could prevent those events that occur less often? This involves predicting the event and prescribing an action in order to minimise its impact on production. This is exactly the focus of prescriptive maintenance; combining predictions (resulting from predictive analytics) with cause/effect/cost analysis to come up with the most appropriate course of action. Ideally, if maintenance is prescribed, it enters the same optimisation logic as described above. Remember, the goal is to optimise asset utilisation.

Reducing TTR is too often overlooked or just approached by process standardisation. However, many studies have shown that TTR is highly impacted by the time it takes to diagnose the problem and the time to get the technician/parts on site – especially in the case of moving equipment. Predictive analytics may help reduce both: the first, by providing the technician with a list of the systems/parts most at risk at any moment in time and the second by making sure the ‘risky’ parts are available. There’s nothing worse than having to set in motion an unprepared chain of actors (technical department, supplier, tier 1,…) for tracking down a hard to find part. This is even worse when the failing machine slows down or halts an entire production chain…

Poor ROA (Return On Assets) is often a trigger for takeovers because the buyer is confident they can easily improve the situation. It’s one of the telltale signs of a poorly run or suboptimal operation and has to be avoided at all cost. If your sights are not yet set on this domain, chances are other people’s are!

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