Measuring machine downtime: How big is the problem?
mai 27, 2024
Mis à jour le: mai 30, 2024
5 min de lecture
On the subject of machine downtime, there are two essential elements that you need to pay attention to. You may already have some idea about the first - how is it affecting your business? When a piece of equipment is not functioning properly, it can significantly affect production output and potentially impact revenue.
The second - how is it affecting other similar organisations, and how do you compare? There isn’t a standard way of collecting, interpreting, and presenting this information. Therefore, making a comparison between the two can be challenging!
Assessing the global situation
Machine downtime affects every manufacturer of complex goods, in every country, around the world. There have been many different attempts to survey the causes and wider impact of machine downtime. These investigations have varying but typically gloomy headline statistics, including:
In 2023, ABB conducted a global survey and discovered that 69% of plants have monthly unplanned outages. These outages come at a hefty price, with an average cost of US$125,000 per hour¹.
RS Industria and the Institution of Mechanical Engineers calculated the average cost of an hour of downtime. Across both small and large businesses, they found that it is £5,121 in the UK².
Siemens found that in the year 2021 – 2022, unplanned downtime cost Fortune Global 500 companies 11% of their turnover. They also found that an hour of downtime in a large automotive plant costs more than US$2m³.
While the methodologies and the results vary, these reports and others agree on a few key conclusions:
Unplanned downtime is more expensive than planned downtime.
The duration of downtime is falling with time while the cost is rising rapidly.
Modern approaches to equipment downtime analysis and prevention, such as predictive maintenance, are effective at reducing costs.
How to measure downtime in your business
Any time a piece of machinery isn't operating when it ought to be is known as machine downtime. However, the question is: how do we measure this?
Is it more appropriate to measure downtime in hours, days, or weeks? Or would it be more suitable to measure it with regard to the financial cost? And, could this value be expressed as a percentage?
The truth is that there are many different ways to measure how machine downtime affects your business. You can also measure equipment downtime in the opposite way, as equipment uptime or availability. The two measurements are extremely comparable to one another, with each one being the inverse of the other.
The first step to measuring machine downtime in your business is to get the necessary data. Traditionally operators would record production runs and stoppages by hand in a log book. More modern factories will use an array of sensors along with asset management software. This is to automate data recording and equipment downtime analysis.
Overall Equipment Effectiveness (OEE)
One popular method of measuring downtime is Overall Equipment Effectiveness (OEE). This calculation combines availability, performance, and quality. The purpose of this is to provide valuable information about the efficiency of a production process or piece of machinery.
Overall Equipment Effectiveness (%) = Availability (%) x Performance (%) x Quality (%)
To accurately measure downtime, it is essential to take into account all three of these factors. Having a machine up and running constantly with 100% availability is pointless if the output it generates is unusable.
Availability (%) = Actual production time / planned production time
Performance (%) = (Ideal cycle time x production count) / actual production time
Quality (%) = Quality assured production count / total count
The result is a percentage measurement of equipment effectiveness, where the closer the value is to 100% the better. Measuring machine downtime in this way produces a single value, but this can be too simplistic.
It does not show what the causes are, or the timeline that led to the equipment downtime. It does not reflect what has been unsuccessfully trialed and what the ultimate solution was. It may not include or reveal the hidden costs of machine downtime.
Machine downtime can cripple production, but it doesn't have to be a constant battle. By measuring downtime effectively, you gain valuable intel. Tools like OEE can reveal hidden inefficiencies. Invest in prevention; a data-driven proactive approach can significantly reduce downtime and increase your bottom line.
Remember, even small improvements in uptime can lead to major gains. Take charge and watch your machines become reliable partners in success.
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