In the world of manufacturing, downtime is defined as any situation where a particular piece of equipment on a factory floor is not running. This can encompass both planned downtime events, like when shifts are changing over or if you’re going through a period of planned maintenance, or unplanned events like sudden outages as well.
For most businesses operating in this sector, downtime is the single biggest cause for lost production time – and lost revenue – that they’re dealing with. According to one recent study, the average cost of downtime across all industries in 2016 was approximately $260,000 every hour. To make matters worse, that was a massive 60% jump from just a few years earlier, in 2014.
The major issue is that these facilities have become increasingly dependent on newer and more advanced technology and when those tech-based assets go offline, it has the potential to take the entirety of a production with them. If equipment is offline, operators can’t work. Expensive parts need to be purchased and delivered. Products may get delayed. Reputational damage may be caused. The list goes on and on.
All of this also illustrates the importance of downtime tracking – something that many businesses were still taking for granted even as recently as a decade ago.
But it would be a mistake to assume that downtime tracking is about only telling you when a particular machine has gone offline. Yes, it’s possible to get automated alerts in these situations so that you can snap into action as quickly as possible. Downtime tracking tells a far bigger story than that, however – and it’s one that it is in your own best interest to pay attention to.
The Narrative at the Heart of Downtime Tracking: An Overview
To get a better idea of the true story that downtime tracking is trying to tell you, it’s important to examine the two types of downtime that exist in a manufacturing environment: planned and unplanned events.
Planned downtime events happen all the time – particularly when it comes to regularly scheduled maintenance. Maybe you’ve identified that a piece of equipment isn’t operating quite as well as it should be so you’re taking it offline for a few hours to change some parts. Maybe you know that it’s been long enough to where regularly scheduled maintenance is now critical. Even something like product changeover would be an example of planned downtime, as once you take into consideration the setups and adjustments that need to be made you’re still looking at a period where that machine isn’t operating.
The thing to understand is that planned downtime always still comes at a cost and sometimes these events take far longer than you think they do. It’s always in the best interest of both organizational leaders and your operators to make sure these happen as quickly as possible. If a product changeover takes too long, it’s eating into the amount of potential revenue you could be generating in a day. Every minute that planned maintenance takes longer than you expected it to is a minute that you’re losing money – not to mention a minute than an operator is getting paid for a situation where they literally don’t have what they need to do their jobs.
Therefore, downtime tracking in this situation would be crucial because it would tell you exactly how long these events are taking, helping to illustrate areas for potential improvement. Are shift changeovers taking far longer than you expected? Once you know that to be the case, you can begin to dive deeper into why it might be happening – thus allowing you to correct the problem as soon as possible.
The same is true of unplanned downtime, albeit from a slightly different perspective. Unlike planned downtime, unplanned downtime usually has no expected timeframe attached to it. These events can occur without warning and can last indefinitely, which is why people tend to be more concerned with them than their planned counterparts.
For the sake of example, let’s say that a critical piece of equipment goes offline due to the failure of one specific component. Obviously, in this situation you would know the “why” – a component failed and you now need to expedite the process of fixing it. But there are still questions that you don’t know the answers to that downtime tracking can help shed light on.
Were there any warning signs in advance of the downtime event that this might be occurring? Is this a component that fails on a regular basis? What is the root cause of the issue and how do you stop it from happening again? These are the types of questions that you can only answer if you’re tracking everything and anything, which is why downtime tracking is so invaluable.
In addition to unplanned downtime events due to part failures, downtime tracking can also help contextualize things like machine jams. If a machine jams, an operator obviously needs to be present to fix the issue. If they’re not currently by the machine, they need to be located and reassigned to that location. Downtime tracking can help with resource allocation by always making sure that people are in the right place at exactly the right time.
The same is true of unplanned downtime events that are caused by issues like poor maintenance practices. If machines aren’t inspected and maintained on a regular basis, all you’re doing is increasing the chances that they are eventually going to break down. Not only does this harm the productivity of the factory floor, but it also potentially creates an unsafe working environment, too. Therefore, embracing downtime tracking helps clue you into these small problems ahead of time – all so that you can do something about them before they become much bigger and more expensive ones later on.
Downtime Tracking Best Practices
Of course, even the best downtime tracking solution is only as good as the data you feed it – which is why if you really want to leverage all of this to your advantage, there are a number of critical things to keep in mind.
Chief among these is the idea that you need to be able to define a reason for any problem that you’re experiencing in a consistent, structured way. A lot of times, your downtime tracking solution will do this for you – it will use error codes and other data collected by the machine itself to quickly define a reason for the stoppage.
Operator notes should also be present, however, to help again provide as much context as possible. This should include not only the reason for the problem itself, but what steps were taken to correct it. This is key in terms of historical reporting, as at some point you’re going to want to look back and examine the recurring issues that you’ve been facing. The only way you’ll be able to draw actionable conclusions from that data is if it is as detailed as possible, which is why defining a reason and providing notes is of paramount importance.
Likewise, downtime automation should be practiced to help avoid the manual entry of information as much as possible. There are still organizations that are attempting to track all downtime and related events by hand and, make no mistake, they’re doing themselves a disservice.
Manual entry is prone to human error, which is the opposite of what you want in this situation. You want to be able to trust the data and draw actionable conclusions from it. Likewise, an operator may deem a downtime event to be “so insignificant” that it isn’t worth noting at all. Maybe they were able to get that machine back up and running again quickly, and that’s great – but you still need to be aware that the problem happened to begin with. It could be a small warning sign that some bigger issue is lurking on the horizon and you won’t be able to heed that warning if you weren’t aware it happened at all.
In the end, while it’s absolutely true that downtime tracking is a viable way to get alerted to an issue with a piece of equipment immediately after it happens, this is also only a fraction of its full potential. The more you use downtime tracking software, the more data it collects – which means the more valuable it becomes.
Soon, you can start using it to compare the efficiency of one piece of equipment to the next. You can examine the relationships between production lines or even certain shifts on a given day. If yours is a manufacturing enterprise with more than one location, you can even compare how certain facilities are doing under similar contexts.
All of this comes together to form a complete story of now only where your organization is, but how far it has come and where it might be headed. This in and of itself is invaluable in terms of staying competitive and more.