IoT meets Industry (Part 2)
The potential of IoT in manufacturing is clear. Unplanned downtime and reactive maintenance are undesirable cost drivers. I read that in car manufacturing, every hour of downtime can equate to $2m in losses. Fortune calculated that IoT could deliver maintenance savings of 25%, reduce unplanned downtime by 50% and extend the life of machinery by years. Sounds good?
With the increasing automation and computerisation of factories throughout the 90s and 00s, much of the core infrastructure is already in place. Many firms are now ‘upgrading’ this infrastructure to be IoT-ready – just look at the customers of ThingWorx and Xively to see who’s leading the charge. The benefits come from the analysis of the data and more fundamentally the prognosis of future machine degradation.
Big, asset intensive, industries have had good reason to implement predictive maintenance regimes for years and they’ve relied on complex and bespoke systems to meet that task, not to mention a small fortune on sensor and data acquisition technology. They don’t even think twice about using teams of diagnostic systems engineers to interpret the data, as they are highly technical organisations by nature. However, these high barriers have meant that smaller business or those in the adjacent sectors have been simply locked out. Moreover, this legacy technology fundamentally won’t scale as the Industrial IoT sweeps through the manufacturing sector and millions of small and medium sized companies come on-line.
This is also though where IoT adoption is really exciting and will play a big role in lowering the barriers as it forces down hardware prices and democratises data access. So the question is more likely to be when and not if prognostic capability will be within reach. That leaves the data science discipline…and I’m tired of reading about the ‘sexiest job of the 21st century’. These guys are on $250k..ok, yes, I’m super jealous – but that’s not the point.
I’m not disputing that this discipline isn’t core (at least at the moment) but as the IoT evolves how can it possibly meet demand based on this dependency for human analysis? I believe that working out clever ways of automating many of the data science tasks is absolutely fundamental, even critical, to a successful IoT and especially data science heavy solutions such as the prognostics required to make predictive maintenance a reality. Without that, some really cool technology will remain only available to the ‘big boys’.
Stay tuned for next week’s final blog on this topic.