In manufacturing, downtime is when your equipment is not in operation during normal production hours. Downtime is the opposite of ‘availability’, which refers to machine uptime. Availability is one of the three component metrics of overall equipment effectiveness (OEE) and measures the time that a machine is in operation relative to the planned production schedule. Clearly, measuring downtime is important to understanding how your machines are contributing to productivity.
Downtime can be planned or unplanned. Either way it affects your bottom line as it means less time that your equipment is turning out parts. Downtime is a fact of life in manufacturing, but ultimately the goal is to minimise it.
In terms of sources of unplanned downtime, such as machine failure, the aim is to reduce both instances and duration of downtime. Such events can have a significant impact on the time and cost accrued in getting products out the door.
Of course, with planned downtime there can be positive correlates, such as when a machine is being upgraded for enhanced performance. In these cases, manufacturers will want to a) streamline any such upgrades to reduce the duration of planned downtime, and b) evaluate the cost of such downtime relative to the productivity improvements that the downtime enables.
To make improvements to availability, it’s essential to have visibility of downtime and its causes. One method to achieve this is downtime tagging. This is where you categorise and tag periods of downtime according to various causes or reasons, for example ‘no material’, ‘no operator’, ‘setup’, and ‘maintenance’. Of course, to start tagging downtime you need to perform machine monitoring to have a log of the times your equipment was down.
Once you’ve logged and tagged all your downtime over a given range of time, you can analyse it with a simple bar chart like a Pareto chart. A Pareto chart can show the accumulated downtime for each category. This will give you easy-to-analyse insights into the most common causes of downtime, ordered from greatest to least impact on downtime An overlaid line on the chart also shows the cumulative percentage of the total downtime.
Now you have a neatly prioritised list of what to focus on. By targeting the biggest contributors to downtime first, you can make gains sooner and increase your equipment’s availability—a key factor in your factory’s overall productivity and bottom line.
In a normal manufacturing environment, there is typically a lot of data to process, and it’s often hard to know where to begin to make needed improvements. Using a simple method like downtime tagging as a starting point can be a great way to see through the fog.
Want to learn more ways to ensure your machines are driving productivity? Check out our blog about calculating your CMM’s OEE
