Mountains of consulting dollars have been invested in business process optimisation, manufacturing process optimisation, supply chain optimisation, etc. Now’s the time to bring everything together and with all these processes optimised, our whole production apparatus utilisation rate becomes ever higher. When all goes well, this means more gets done per invested dollar, making CFO and investors happy through better ROA (Return On Assets). However efficient, this increasing load on the machine park comes at a price: less wriggle room in case something unexpected happens. When in the past, companies had excess capacity, this not only served to absorb demand variability; it also came in very handy when machines broke down by allowing the demand to be re-routed to other equipment.
There’s no more place to hide now, so there are a number of options one can consider in order to avoid major disruptions:

  • increase preventive maintenance: this may or may not help. Law of diminishing returns applies, especially as preventive maintenance tends to focus on normal wear and tear and parts with a foreseeable degradation behaviour. A better approach is to improve predictive maintenance; don’t overdo where there’s no benefit but try to identify additional quick wins. Your best suppliers will be a good source of information. Suppliers than can’t help; well, you can guess what I think of those.
  • improve the production plan: too many companies still approach production planning purely reactively and lack optimisation capabilities. Machine changes, lot’s of stop and go, etc. all add to the fragility of the whole production apparatus (not to mention they typically – negatively – influence the output quality as well).
  • improve flow: I’m still perplexed when I see the number of hick-ups in production lines because ‘things bumped into each other’. Crossing flows of unfinished parts is still a major cause of disruption (and a major focus point for top performers such as Toyota). As most plant managers why machines are in a certain place and they either “don’t remember” or will say “that’s the place where they needed the machine first” or even “that was the only place we had left”. Way too rarely do plant layouts get re-considered. Again, the best-in-class do this as often as once a year!
  • shift responsibilities: if you can’t (or won’t) be good at maintenance, then outsource it! Get a provider that can improve your maintenance and ideally can work towards your goal, which is usually not to have shinier machines but to get more and better output. If you really decide you don’t care about machine ownership at all, consider performance- or output-based contracts.
  • get better machines: sounds trivial but current purchasing approaches often fail to capture the ‘equipment quality’ axis and forget to look at lifetime cost in light of output. Just two months ago I heard of a company buying excavators from a supplier because for every three machines, they got one for free. This was presented as an assurance that the operator would never run out of machine capacity. In this case, it had the adverse effect as the buyer thought why they needed to throw in an extra machine if they claimed they were as reliable as the best.
  • connect your machines: this is a very interesting step. Recognising that machines will eventually fail but at least making sure you get maximum visibility on what/where. Most of the time resolving equipment failures is spent… waiting! Waiting for the mechanic to arrive, waiting for the right part, etc.
  • add predictive analytics: predictive analytics not only allow you to prevent failures from happening but, relating to the previous point, to the what/where axis, predictive analytics allows the addition of why. Determining why something failed or will fail is crucial in optimising production output. Well-implemented predictive analytics allow us to improve production continuity by avoiding unplanned incidents (through predictive maintenance) but also allows for more efficient (faster) and effective (resulting in better machine uptime) maintenance.

So which of these steps should we take? Frankly, all of them. Maybe not all at once and (hopefully) some of them may already have been implemented. Key is to have a plan. Where are we now, what are our current problems, what are we facing,…? Formulating the problem is half the solution. Then – and this may surprise some – work top down. Start with the end goal, your “ultimate production apparatus”, and work your way back to determine how to get there. All too often people start with the simplest steps without having looked at the end goal and after having taken two or three steps they find out they need to backtrack because they took the wrong turn earlier in the process.

At any step, whether it’s purchasing equipment or to install sensors or whatever, look at whether your supplier understands your goals and is capable of integrating in “the bigger plan”. The next efficiency frontier is APM: Asset Performance Management. Not individually, but from a holistic point of view. While individual APM metrics are interesting for determining rogue equipment, only the overall APM metrics matter for the bottom line; did we deliver on time, was the quality on par, at what cost,…