5 Most Important Machine Metrics to Prevent Machine Downtime

5 Most Important Machine Metrics to Prevent Machine Downtime

Downtime is the leading cause of lost production time for most manufacturers, negatively impacting the bottom line. A 1% gain in efficiency may not seem like much, but it can contribute thousands or even millions of dollars to a manufacturer’s bottom line.

Here we will detail the top five machine metrics that every plant should monitor to reduce costly downtime.

1 – Overall Equipment Effectiveness (OEE)

OEE = Availability x Performance x Quality

Overall Equipment Effectiveness (OEE) is the gold standard for measuring production efficiency in manufacturing. Simply put, it reveals what percentage of manufacturing time is truly productive. A score of 100% for OEE means that you are making only good parts as quickly as possible without any stopping time.

The three measurements that makeup OEE include:

  1. Availability: Availability refers to the availability of the machine or cell for production at the designated time.
  2. Performance: Performance is measured by the amount of waste caused by running below optimal speed.
  3. Quality: Quality looks at how much time was wasted making a product that doesn’t meet quality standards.

This metric is one of our favorites. It provides a comprehensive evaluation of the productivity of a manufacturing tool or an entire production line. It is an excellent method for monitoring equipment output and ensuring that the factory is operating at top efficiency.

If you want to learn more about OEE, check out this great whiteboard explanation.

2 – Machine Uptime and Downtime

Machine uptime and downtime also called “run time” is the amount of time a machine is working in each time frame. This metric indicates the number of time machines are not functioning, whether due to planned maintenance or unexpected tool changeover, as well as those induced by shift changes.

Real-time monitoring of machines allows managers to pinpoint the source of problems before they have a significant impact on production or revenue. In this example, a filling line was malfunctioning, resulting in an OEE of 60.57%. The Acumence Downtime Graph below was used to see how each machine on the line was performing during a shift.

Figure 1 Downtime Graph Report

Figure 1 Downtime Graph Report

The case packer was found to be a major bottleneck in the process, and downtime was found to be the main reason why. In this case, most of the case packer downtime was caused by problems that can be avoided or fixed.

Figure 2 Event Shift Analysis Report

Figure 2 Event Shift Analysis Report

3 – Spoilage and Scrap by Defect and Machine

Material losses may take the form of waste, scrap, defects, and spoilage. Problems with the machine’s upkeep, configuration, or tools are common causes of scrap. For instance, if tooling maintenance is neglected, it can lead to significant spoilage and decreased output.

While most of the industry has a handle on recording manufacturing scrap costs, relating those costs to a machine failure is seldom done.

For example, the rate of spoilage for a bodymaker often goes up as the tooling nears the end of its useful life. When this happens, it may be time to do maintenance and replace the tools. By running a report that shows spoilage and scrap by fault and machine, operators can get a better idea of which machines might need to be fixed before they cause unplanned downtime.

Spoilage and Scrap by Defect and Machine

Figure 3 – Spoilage and Scrap by Defect and Machine

4 – Downtime by Fault & Root Cause

The first step toward identifying unused capacity is tracking it using real-time data. A distressing situation, such as a lengthy outage of important machinery, garners most of the attention. However, it is the frequent, brief periods of downtime that generate the most inefficiency.

For example, a downtime by fault and root cause report could show the main reasons for short-term downtime, like a piece of equipment’s in-feed track getting stuck, as shown in the picture below. Through this method, maintenance workers can identify which pieces of equipment have the longest periods of downtime and for how long, and then develop a data-driven plan to address the issue.

5 – Asset Utilization

Asset utilization assesses how much equipment, machinery, product lines, or the entire operation is working in comparison to its potential. Higher asset utilization typically equates to the higher overall efficiency and profit margin. Asset utilization differs from OEE in that it considers all losses, not just those directly connected to production or manufacturing.

In any manufacturing setting, improving asset utilization is a great way to reduce defects, maximize output, and extend the life of machinery.

Data Is Essential to Minimizing Downtime

Unexpected downtime in production cost factories a lot of time and money, but it doesn’t have to be that way. With real-time machine monitoring from Acumence, manufacturers can cut spoilage, limit downtime, and stay ahead of the competition. Let’s chat to see how we might be able to help you optimize your production and maintenance process!