At regular intervals, company managers are asked to provide their forecast for the next period(s). While some dread this exercise – and it is a tough ask for them to put together this forecast – others can almost pull the numbers on request. Why? Because they have good visibility on current and past performance, on current and past conditions and are well informed on forecasted evolutions in the market. Why is that so difficult for other? In short: lack of visibility. So many companies put KPI’s in place but then forget to make it easy to monitor these KPI’s.
For equipment, remote monitoring can be put in place and it is even more important with moving than with static assets (RCM: Remote Condition Monitoring) as reaction times are de facto longer due to the remoteness of the equipment in case of an unplanned event. I was therefore, to say the least, surprised to find out that most rail companies still have very limited visibility on their assets’ whereabouts, let alone the assets’ condition! With increasing pressure on punctuality and efficiencies, things have to change rapidly. Some process improvements has been put in place. Some signalisation has been upgraded and, finally, some cross-European initiatives have been put in place (such as ETCS – European Train Control System and ERTMS – European Rail Traffic Management System). In some cases, the lack of punctuality has been solved by… adapting the train tables to the observed performance of the trains (really!)…
I’m often asked why visibility is so important. Let the following graphic illustrate this:
PastedGraphic-1
Only a small part of total downtime is actual repair time! Obvious as this seems, time and time again when executives check the numbers, they’re flabbergasted by this finding. Turns out they’ve all been investing tons of money in improving the repair process and very often they’ve forgotten to get the diagnosis and parts/mechanics logistics in order. I was once told by a rail operator that in order to get better remote diagnostics, in case of a failure they asked the train driver to ‘make a picture of the control screen with his smartphone and send it back to central’. May sound funny but I actually think this was a great idea! And, in effect, it cut a big chunk off their diagnose time.
How do we take things one step further? Once we have (digital) visibility, we can use this data to make predictions on assets’ condition. With this information, it is possible to avoid (some, not all) remote failures, which leads to less downtime, higher punctuality and service levels, lower maintenance costs, etc. How so? A number of conditions need to be fulfilled:
– the predictions need to be actionable (i.e. they should tell you what to do, not just give you an abstract statistical forecast)
– the predictions should be based on accurate and up-to-date data
– the predictions should be accurate enough to warrant an intervention
– the predictions should provide enough lead time to get the repair done (planned, mechanic and part in place, etc.)
Given these conditions, RCM can lead to CBM (Condition Based Maintenance). While RCM is a mature technology, it is not yet generalised; very often, trains are quite capable of capturing their condition through sensors which feed an on-board diagnostics system, but all too often, the capability to offload this data from the train is lacking. CBM as an approach to maintenance is equally mature but even less widespread, mainly due to the lack of data and processes. However, it is well-known that CBM increases equipment performance which, in rail, results in increased punctuality, less breakdown and, ultimately, higher capacity.