The biggest maintenance failures of 2021

factory worker inspecting machine failure

Senseye, takes a look at some of the biggest maintenance failures in 2021. The cost of maintenance failure is indisputable. According to Senseye’s latest report, The True Cost Of Downtime, large industrial facilities lose 3.3 million hours a year, which equates to almost $1 Trillion, to machine failures.

This equates to 8% of their annual revenues and crucially, does not even factor in potential damage to trust, confidence, and reputation, all of which stand to be compromised in the event of such mistakes.

Against this backdrop, it’s perhaps no wonder that 2021 saw a number of high profile examples of maintenance failures, none of which discriminated against industry sector, or region.

When the chips are down

Take the global semiconductor chip shortage for example. While the broader sourcing and supply chain challenges have been well reported, an additional spanner was thrown into the works last year, when adverse weather in Texas caused machines to fail, and production to halt across several manufacturers. This inevitably caused further disruption to an already struggling supply chain.

woman-in-blue-inspecting-component

While extreme weather isn’t something which can be avoided of course, it’s likely that a comprehensive predictive maintenance strategy would have mitigated the impact of machine failure.

In a similar vein, 2021 also saw several incidents of excessive heat impacting equipment, causing power outages in Denver and resulting in widespread disruption. Aside from the obvious inconvenience, the consequences of such an incident include food safety from compromised freezer and refrigeration facilities, and public risk as a result of unsafe, unregulated, heating alternatives. The level of resources needed to remedy the situation is hugely costly and again, while outages occur, the risk is substantially reduced as a result of optimising maintenance strategies.

Safety first

Risk remains intrinsically linked to maintenance failures, and in September 2021, a UK food manufacturer was issued with a fine for ineffective maintenance. While many examples point to the costs in terms of loss of production, the reputational risks of incidents which expose employees to risk, cannot be underestimated.

man-operating-food-manufacturing-equipment

Not all examples are confined to a shop floor of course. Rail disruption as a result of equipment failure is not uncommon on Hong Kong’s Mass Transit Railway (MTR) line. Numerous reports in 2021 point to poorly maintained equipment being the reason behind commuter trains failing to reach their destinations on time. When you consider the volume of people who use the train line, the associated productivity losses are likely to have been vast. This puts the cost of equipment failure much higher than the organisation might have suffered directly.  

The tip of a shrinking iceberg?

While these examples represent the very tip of the iceberg when illustrating the scale of the issue, they clearly point to the different ways in which maintenance failures can manifest themselves, and the associated cost and reputational damage.

While specific types of maintenance strategies vary from company to company, one of the reasons that 2021 continued to witness such high profile failures, is that traditional, preventative maintenance models prevail in many organisations. Rather than using predictive analytics to understand variables and patterns, and pinpointing the likelihood of a particular machine breaking down at a specified time, preventative maintenance relies on routine, calendar-based interventions. As well as being unnecessarily costly, this approach falls short of delivering the reliability necessary as equipment can easily break down in between intervals. Testament to this gap is that according to ARC Advisory Group...

82% of equipment failures cannot be avoided through using traditional, or preventative, maintenance approaches.

With digital advances bringing new, easy to deploy, capabilities in the form of cloud-based, machine learning-infused predictive maintenance, to address challenges such as those outlined in the above examples, organisations simply can’t afford to risk such outages if there’s any way in which they can be avoided.

As digital adoption matures, and the potential to mitigate against unnecessary downtime is fulfilled, we look forward to a decade of fewer such examples, and a smoother, more resilient way forward for equipment-centric industries.