Machine downtime is a critical area of concern for manufacturing companies, where maximizing productivity and controlling costs are paramount. When a piece of equipment goes offline, production is delayed, labor hours are lost, and profits take a hit.
Analyzing equipment downtime helps organizations understand not only why their machines fail but also how to prevent them and therefore, reduce the frequency and duration of interruptions. In this article, we’ll walk through the basics of effectively calculating various machine downtime metrics and discuss the best ways to track machine downtime and prevent future outages.
What is machine downtime?
Machine downtime is any period when equipment is unavailable for use, either because of an unexpected malfunction or breakdown or a planned event like maintenance or changeovers. Downtime events prevent work from proceeding as planned and can lead to inefficiencies such as production delays, missed deadlines, and reduced output.
To manage and reduce downtime effectively, it’s helpful to understand the two different types of machine downtime:
- Unplanned downtime which occurs any time a machine breaks down unexpectedly or encounters a problem that requires immediate attention, commonly caused by equipment malfunctions, software issues, or human error. Unplanned machine downtime is often the most disruptive because it halts production without warning.
- Planned downtime, which includes scheduled maintenance, inspections, upgrades, or other events scheduled in advance, is important for the longevity of the machine. However, it still results in lost production time, so it is critical to take steps to minimize their frequency and duration.
Key metrics for analyzing machine downtime and how to calculate them
Effective downtime analysis only takes place when teams accurately track and calculate key maintenance metrics. Here are the most critical metrics, along with how to calculate each:
Mean time between failures (MTBF)
MTBF, also referred to as downtime frequency, tells you how often machine failures happen. High MTBF can indicate recurring issues that need to be addressed, whether related to maintenance processes, operator training, or equipment reliability.
How to calculate MTBF:
MTBF = Total Operating Time / Number of Failures
- Total operation time is the total amount of time the machine is operating during the time period being analyzed.
- Number of failures is the number of times the machine or system failed during that same period.
Use MTBF if you want to understand expected operational time between failures, or predict maintenance needs.
Mean time to repair (MTTR)
Calculating the MTTR, or downtime duration, helps teams measure how efficient their response and repair processes are. A lower or shorter MTTR, means that the failure was resolved quickly and production was resumed with fewer delays and losses. The goal is to minimize MTTR as much as possible by implementing systems to quickly identify, troubleshoot, and fix the problem.
How to calculate MTTR:
MTTR = Total Downtime / Number of Failures
- Total downtime is the cumulative amount of time the machine was down for repairs over a given period.
- Number of failures is the total number of times the asset failed and needed repair in that same period.
Use MTTR if you want to evaluate and improve maintenance, repair, and troubleshooting procedures.
Downtime costs
Downtime costs capture the financial impact of downtime, including factors like lost productivity, labor costs, and any other repair expenses. Downtime cost data is especially helpful for determining priorities and justifying investments in new or replacement equipment and preventive maintenance programs.
How to calculate downtime costs:
Downtime Costs = (Lost Production Time × Hourly Production Value) + Labor and Repair Costs
To accurately calculate the cost of downtime, you must add the cost of any lost production time to the labor and repair costs. For example, if a machine that typically produces $500 per hour (in production value) is down for three hours, the production loss is $1,500 plus any repair and labor costs.
Measure downtime costs if you want to quantify the financial impact of equipment downtime.
Availability
Availability measures the percentage of time a machine is operational compared to its scheduled uptime.
How to calculate availability:
Availability = (Scheduled Uptime – Downtime) / Scheduled Uptime × 100
- Scheduled uptime is the amount of time the machine was scheduled to run
- Downtime is the number of hours in downtime the machine experienced
This formula gives you a percentage of the machine’s operational availability. For example, if the machine was scheduled for 100 hours and experienced 10 hours of downtime, its availability was 90%.
Measure availability if you want to understand how efficiently your organization is using its production time.
Overall equipment effectiveness (OEE)
Overall equipment effectiveness combines availability, performance (speed), and quality (output accuracy) to provide a holistic view of equipment effectiveness. A higher OEE means the equipment is functioning optimally.
How to calculate OEE:
OEE = Availability × Performance × Quality
To calculate OEE, you need to understand how to calculate availability, performance, and quality. See our full guide on calculating OEE.
Measure OEE if you want understand how well your equipment is performing overall, inclusive of product quality and speed, as well as time efficiency.
Step-by-step guide to analyzing machine downtime
Understanding the important metrics and maintenance KPIs to track will help your team better analyze machine downtime. If measured and reported to stakeholders in a systematic way, it will also lay the foundation for ongoing performance improvement, providing the ability to see the impact of both small and large improvement initiatives over time. Here’s how.
Step 1: Identify and categorize downtime events
Start by identifying and recording each downtime event. Capture as much detail as possible, such as the specific machine affected, start and stop times, the reason for the downtime, and any patterns that you observed.
The right details will help you accurately categorize each downtime event. Divide events into root cause categories–like mechanical failure, operator error, or supply chain delay to make it easier to spot trends and identify areas of improvement.
Step 2: Collect and organize data
Consistent, organized machine data is a must for effectively analyzing downtime. Use a standardized system to log downtime events, either through a Computerized Maintenance Management System (CMMS) or another tracking method. Having clear, accurate, and well-structured data will assist with better trend identification and quicker analysis.
Step 3: Calculate key downtime metrics
With accurate data on hand, your team can calculate whichever metrics will best serve your organization’s goals. Metrics like MTBF, MTTR, downtime costs, and OEE provide insight into how often downtime occurs, how long it lasts, and its impact on operations, allowing you to determine which areas need the most improvement.
Tools like a CMMS are great for generating reports and automating the calculation of these important metrics.
Step 4: Analyze patterns and trends
Once you have calculated your chosen metric or metrics, look for patterns in the downtime data. There could be spikes in frequency, recurring causes, or seasonal variations. Visual tools like charts and graphs can help you spot trends like these.
If specific machines, operators, or shifts consistently experience downtime, these insights can help in reallocating resources or updating maintenance protocols to reduce downtime frequency.
Step 5: Identify root causes and improvement opportunities
Root cause analysis (RCA) is absolutely necessary for determining underlying issues that lead to downtime. Tools like the “5 Whys” and fishbone diagrams (Ishikawa diagrams) can help systematically break down problems.
Identifying the root cause allows organizations to implement targeted corrective actions, whether it’s improving maintenance schedules, investing in operator training, or updating equipment.
Step 6: Set improvement goals and track progress
Once you’ve identified areas for improvement, set measurable goals. For example, you might aim to reduce unplanned downtime by 10% within six months or decrease the average downtime duration by a specific percentage. Use your CMMS or other tracking tools to monitor these goals and note progress as you take steps to improve them, making adjustments as needed to stay on track.
Best practices for tracking machine downtime
Data can be fickle. If it is not gathered accurately or consistently, any metrics or reports you try to pull from it will be incomplete or inconclusive. And even when the system is working, one data point or metric only gives you insight from one angle. Here are several best practices to follow to ensure you get the most out of any data analysis, which is especially crucial for something as important as downtime.
Standardize data collection methods
Consistency is key. Standardizing data collection across teams and machines ensures that information is complete and comparable. A standardized downtime tracking form or digital template in a CMMS can be helpful for consistently logging downtime events.
Record downtime events in real-time
Logging downtime as it occurs minimizes data inaccuracies. Real-time data entry can capture important details about the event that might be missed if recorded later. Mobile CMMS access can enable operators to log downtime from the factory floor immediately which also helps improve communication to the maintenance team and speed up repairs.
Track planned and unplanned downtime separately
Recording planned and unplanned downtime separately helps clarify which downtime events are avoidable. It is also important to track these metrics separately because the actions you take to reduce each kind of downtime will be different. This distinction provides a clearer picture of maintenance effectiveness and allows for better prioritization of maintenance schedules and improvement efforts.
Include duration and impact data for each event
Recording both the duration and the impact of each downtime event (e.g., lost production hours or reduced output) helps assess its cost and prioritize solutions. This data is crucial when calculating downtime costs and setting goals for reducing downtime’s financial impact.
Regularly review and validate machine downtime data
Schedule regular reviews of downtime data to ensure accuracy and consistency. Validating data with team leads or supervisors can help confirm details and fill any gaps. Regular reviews also enable continuous improvement by keeping your team informed of downtime trends and progress.
Track multiple metrics, but not too many
Each metric discussed above will give you only a partial view of your full downtime picture. That is why it is important to choose a complementary set of metrics to follow to provide a broad view of the underlying issues related to downtime.
However, it is also important to not choose too many metrics, which can be distracting and cumbersome to report. Choose one particular aspect of your operation and its machine downtime that you would like to learn more about, and select two to three metrics that can give you focused insights on that topic. Then, as you improve and move on to other initiatives, your metrics can be adjusted as well.
Leverage a CMMS to improve equipment downtime tracking
A CMMS is an invaluable tool for managing downtime data. It automates data collection, categorization, and analysis, which saves time and reduces human error. CMMS systems often feature analytics and reporting functions that can provide actionable insights and help you stay on top of downtime goals.
In addition, features like predictive maintenance scheduling can help reduce machine downtime by proactively addressing issues before they lead to breakdowns.
Curious about getting started with a CMMS? Schedule a free demo with Limble CMMS or contact our team to learn more.
