Railway Maintenance going predictive? – or why you should not ask consulting firms

Since everybody with any useful degree, such as myself with a simple business degree, can raise his or her opinion about some basic mechanics, I can do that too, with this simple comment on a report that was recently published.

Railway maintenance is a topic that is very dear to me, hence I often read what is being published about it. Once in a while, really odd flowers spring into the picture such as this report by the global consulting firm McKinsey that you can access under this link: Mckinsey Report.

Methodology – what methodology?

First something about biases: The basis of the report has been to a) ask 25 international rail COOs and executives in charge of maintenance, b) client experience (I assume on a strategy level), c) a survey with 400 manufacturing executives across industry countries.

Yes, you made the right assumption. Based on this basis, I can also “predict” what in the report about railway maintenance and predictive maintenance will be written – and yes again, this assumption is fully correct.

So in principle, if you ask the industry “leaders” what they think, you will hear what has been known in the industry and fully avoid any Out-of-the-box-thinkers. The 24 pages report goes therefore not into the actual industry mechanics and where there is disruptive potential but solely focuses on the already know world on the horizon. To quote an America president “sad”.

Sensors vs. the actual thing

Yes again, we all read the commentaries that explain how we can use lots of sensor data to gather information about a specific equipment and predict its failure. This is fully correct, but only relevant if the failure rate of the sensor is lower than that of the actual equipment.

Now in railway maintenance, equipment is mostly designed for a lifetime of 30-40years+. So far, I have not seen many sensors that can match that (expect for a few very specific sensors that use basic physics or electronics).

Proprietary IoT solutions?

It seems very odd to me, that this consulting firm mentions actual ERP products (in this case SAP. They must get a kick-back since an open-source or open platform would be for the purpose of an IoT solution including data analytic not only cheaper on the long run (TCO) but most likely also on the short run (cheaper implementation).

…but good recommendations over all

Still, the chapter about recommendations is useful and points also in my opinion into the right direction. Still, it is embedded in the “industry” philosophy of slowly changing aspects and does not utilize the drive start-ups could have. If one really wants to be successful in this industry, one should build a start-up between mechanics, electricians and a few Software and data driven people…

 

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