Your production line just went down and with every tick of the clock dollar signs fly before your eyes. You tell your crew to get it back up and running as fast as possible. The part you need is out of stock so you take a similar part, make it fit and hit the power switch. Does this sound familiar?
But what if you could have seen it coming? What if you could have been prepared? Some types of downtime are unpredictable but there are other types we can see coming by collecting data and using tools like a Preventative Maintenance Program. The first step is identifying the type of downtime you are experiencing.
Repeat Offenders – These are predictable causes of downtime.
Example: Parts wear out, chains stretch over time.
Once in a Blue Moon – These are downtime issues related to variables that you aren’t monitoring or don’t even know are there.
Example: A power outage shuts down your machine in the middle of a cycle and now it won’t power up properly or your raw material supplier makes a change to their process.
The Dreaded “Oops” – Mistakes caused by human error.
Example: The Operator loaded the wrong tool and the machine crashed.
The “We should have seen this coming” – Hindsight is 20/20.
Example: When training a new Mechanic you have to factor in more downtime on repairs due to lack of experience.
“Gremlins” – Sometimes teams come with two bonus team members: Somebody and Nobody. They don’t get a paycheck yet they always seem to be around.
Example: “Somebody else must have flipped that switch.” “Nobody adjusted the pressure last night.”
Changeovers – Changing your machine setup in order to change product type.
Example: Going from part A to part B requires fixture and tooling changes.
Once you have identified the types of downtime, you can track them using software such as MachineMetrics.
Gathering data is the key to solving problems. Your “gut feel” can be way off base. Using MachineMetrics will help you hone in on those problem areas that might otherwise be elusive. If Changeovers are your biggest source of downtime you can look at reducing the time it takes to perform one. You can also take it a step further and use the downtime data to look at which changeovers are the biggest pains. You may be able to plan longer production runs of a certain product to minimize the number of changeovers to that product type.
If unplanned downtime based on machine issues is causing headaches, you can implement a Preventative Maintenance Program. Using data you can predict when a part will fail and replace it before it does. If the data shows that multiple parts of a machine will likely fail within weeks of each other and it takes hours of disassembly to gain access to that machine element you can save time by replacing them all at once. Planned downtime may seem counter-intuitive but it saves time in the end and helps you stay on schedule.
If you see massive shift to shift variation in downtime you may have a personnel issue on your hands or they may just need retraining. All too often we put a band-aid on something to get a production line going again without taking the time to find and address the root cause of the problem. By getting to the root cause of the downtime we may be able to stop it in the future.
The first step to solving any problem is defining the problem. With MachineMetrics, you can create the arrest warrant for your usual suspects of downtime and lock them up for good.
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