Is it possible to quantify the return on investment from Predictive Maintenance? We certainly think so. But it’s all a question of balance - and an approach that makes greater use of Artificial Intelligence (AI) and Industry 4.0.
Senseye’s industry-first The True Cost of Downtime report shows that using Predictive Maintenance tools at scale to reduce unplanned downtime would deliver a $432 billion boost to the world’s leading industrial companies.
Our study of 72 multinational manufacturers, detailed in the report, showed that large industrial facilities lose 323 production hours a year to machine failures. The average cost of lost revenue, financial penalties, idle staff time, and restarting these lines is $532,000 per hour, amounting to $172 million per plant annually.
By extrapolating our findings across Fortune Global 500 (FG500) industrial companies, we’ve calculated that these companies are losing 3.3 million hours in production time annually to unscheduled downtime and taking a near $1 trillion financial hit - equivalent to 8% of their annual revenues.
While planned downtime via preventative maintenance allows firms to undertake vital maintenance activities during periods when there is no production impact, there’s a cost to holding inventory and spending effort and parts doing more maintenance than is necessary.
So, where is the balance? The key is to move from Preventive to Predictive Maintenance. If maintenance teams can listen to machine health, they can service them before they break down, but without the need for exhaustive and wasteful preventative maintenance schedules.
Our studies show that even organizations that have introduced some level of condition monitoring - an essential first step towards Predictive Maintenance - enjoy higher levels of OEE (Overall Equipment Effectiveness). Reported OEE levels are, on average, 6% better at organizations that do some condition monitoring than those that do not use this at all.
However, it’s with the implementation of more sophisticated AI-driven machine health monitoring and Predictive Maintenance capabilities that our large-scale manufacturing clients have been able to see a return on digital innovation.
In our experience, we’ve seen an 85% improvement in downtime forecasting accuracy along with a 50% reduction in unplanned machine downtime. Clients have also achieved a 55% increase in maintenance staff productivity and a 40% reduction in maintenance costs using predictive maintenance technologies like Senseye.
What does this mean for the FG500 industrial organizations in our study?
From the returns seen from our clients, we estimate that the widespread use of advanced, AI-driven machine-health monitoring and Predictive Maintenance could save FG500 manufacturers 1.7 million production hours a year and deliver a 4% productivity boost worth $432 billion.
- For FG500 automotive manufacturers, we estimate the use of Predictive Maintenance would recoup over 200,000 lost hours and deliver a 10% boost worth up to $281 billion a year
- Across all FMCG manufacturers in the FG500, we’ve calculated it is possible to save 750,000 production hours a year and deliver a 2% boost worth $17.5 billion
- Heavy Industrial companies in the FG500 could save over 600,000 lost hours a year and achieve a $113 billion boost
- FG500 Oil & Gas producers could save 106,000 hours a year in their refineries alone and deliver a $23.4 billion boost
The global market for Predictive Maintenance is growing fast. Yet as our research shows, there’s still a considerable amount that multinational industrial companies and manufacturers can do in this area.
While there are significant production and financial gains on offer, it is also worth considering the maintenance efficiency improvements on offer and the benefits from the shift in mindset that accrue from adopting cutting-edge AI-driven techniques and progressing towards Industry 4.0.
Find out more about the benefits of Predictive Maintenance and the return on investment it can deliver in our report The True Cost of Downtime.