How Do I Conduct a Machine Downtime Analysis?
If you’re experiencing loss of productivity because of downtime, you are looking for answers. How much downtime am I experiencing? What is causing it and why? How much is it costing me? An accurate analysis of your downtime will increase capacity and throughput because your machines will be running more often.
A downtime analysis isn’t easy if you aren’t tracking the right things, but this blog entry will outline the basics for an effective downtime analysis and will answer the question of “How to log and track machine downtime?” and “How to analyze unplanned and planned machine downtime?”.
How To Track Downtime Correctly?
If you are tracking it at all, congratulations. But just to revisit this, you should be tracking downtime in this way:
– By Machine/Cell/Line (What machine is down?)
– Whether it was unplanned or planned (Was the downtime scheduled?)
– Downtime by Minute (How many minutes was the machine down?”
– By Shift and Operator (Who was running the machine?)
– By Category (What is the specific cause of the downtime?)
Useful but not necessary
– By Department (Who brought the machine down? Maintenance? Production Support?)
– Cost of machine downtime (Did the downtime cause other machines to go down or stop production? If so what were the number of units that went unproduced and what profit did it cost?)
How To Log Machine Downtime?
Differentiate between planned and unplanned. Roundup and track downtime by minutes to simplify your data collection. Many manufacturers choose an arbitrary threshold of a few minutes so they only focus on significant events. This is understandable when looking at downtime hour by hour, but over the course of a shift or a full week, these “micro stops” or “short stops” can add up.
With these things identified, you should be able to start analyzing your daily downtime by focusing only on the downtime events that impacted productivity and cost significantly.
How Do I Start Analyzing Machine Downtime?
Start with a 24-hour period from your latest downtime logs or downtime report. This will focus your analysis on a manageable time period and provide you with enough data to gain insights and prepare for the next day’s starting shift. Start with unplanned downtime, since while planned time can still be reduced, it is the unplanned downtime that can create cascading issues to performance and impacts productivity unexpectedly.
Look hour by hour to understand when and how long downtime occured. This will provide you with an understanding of what might have caused or exacerbated downtime. Was it caused in the confusion of a shift change or an unscheduled operator break?
What Is Causing Your Machines To Go Down?
Look at the machines which were down during the shift. Was the downtime unplanned or planned? Was it brought down by maintenance or operations? Catalog Downtime so you can compare categories.
Why Use a Pareto Chart to Analyze Downtime?
Drill down to your top 5 or so biggest chunks of downtime. A helpful tool at this point is a Pareto chart, so named because Vilfredo Pareto identified the 80/20 rule and created the chart that bears his name. By plotting your downtime in a Pareto you can determine the 20% of issues that cause 80% of downtime. These are the critical areas you need to address.
Our example Pareto chart shows downtime in minutes and it is easy to see that 80% of our downtime is caused by 3 reasons:
- End of Shift Cleaning – 75 minutes
- Short Stop – 46 minutes
- Dial-In – 40 minutes
With End of Shift Cleaning taking up most of our time reducing End of Shift Cleaning will have the greatest time impact on our production. But is this the right thing to focus on?
In this table we can see 3 things:
- Number of downtime events by reason
- Total duration in minutes
- Average duration in minutes
We have a lot of short stops and almost the same amount of time in Dial-In. Our production teams may be able to completely eliminate both of these problems and give us back 86 minutes of production time.
How To Mitigate Downtime By Department?
Sub-divide your downtime if it is initiated by the operator, production support or maintenance. This will allow you to pinpoint who is responsible for the downtime and avoid the blame game. If unplanned downtime was caused by an operator error or a series of unplanned micro stops, then use it to flag that behavior and better understand what is happening.
For unplanned maintenance, could the downtime have been avoided if the operator was better trained? Flag your unplanned maintenance time if it was justified or not. This will lead to better qualifications of downtime later on.