Managing Manufacturing Down time

Downtime is an important metric for factories to track, as it can have a significant impact on production performance. By understanding the causes of downtime and tracking it carefully, factories can take steps to reduce its occurrence and improve overall efficiency.

There are a few different ways to track machine down time in manufacturing. The most common way is to use a throughput sheet. This is a simple form that lists all the machines in a factory, and their respective down times. The sheet is usually a paper-based process where operators and supervisors fill out the form by hand to track downtime issues and submit it for collation.

Another way to track down time is by using data collectors and sensor systems. These devices are placed on each machine and collect data on its performance. This data can then be used to calculate the amount of downtime for each machine. While there are more advanced systems, many simply record the data internally and require technician or operators to download the information for review.

Finally, many factories now use software programs like Manufacturing Execution Systems (MES) to track machine down time. These programs often integrate with data collectors, sensors and IIoT solutions to provide more accurate information. These manufacturing execution systems keep databases of historical production performance as well as able to alert managers in real time when the machine has issues. By tracking machine down time digitally, factories can improve their overall efficiency and productivity over paper-based processes. They also can act more swiftly to issues when they develop


How downtime effects overall production performance

Downtime can have several effects on production performance. First, it can directly reduce the amount of work that a factory is able to produce. This is because machines are not able to operate when they are down. Down machines can waste operator time set aside for production and consume maintenance staff in the process.

Second, downtime can also lead to indirect effects on production. For example, if one machine is down, it may cause a bottleneck for other machines downstream. This can further reduce the factory's output. This bottleneck may also cause other issues of over production before upstream processes are notified of the issue. The stoppage may cause scheduling issues upstream as processes need to be adjusted for the stoppage.

In addition to reducing throughput, downtime can also impact other important metrics. For example, it can increase the amount of scrap and rework material generated. This is because machines that are down (or are in the process of failing) are often not able to produce parts to specifications. As a result, these parts must be scrapped or reworked, which increases costs. Down time can also cause delays in delivery, as parts that are supposed to be shipped may not be available if the factory is not running at full capacity.

Getting ahead of downtime issues

The goal of reducing downtime is not to be faster at fixing it when it happens, it’s to get ahead of it before it happens.

That’s where manufacturing data collection systems shine. These systems like manufacturing execution systems can record the vast amount of data a manufacturing operation’s sensors create. They can also help present that data in a manner that turns it into valuable information a manager can use to understand what’s happening right now and plan for the future.

Our MV2 MES was designed from the start to provide just this sort of service. Its database is built to collect and present this information in real time. It’s also able to combine this information with data from human operations, quality, Kanban and inventory functions. This holistic approach to manufacturing data provides a complete picture of shop floor operations.

If you’re looking to get ahead of downtime situations in your production operations, contact us today. Our highly skilled integrators can help you understand the benefits your particular operations can achieve with an MES like MV2.

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