Machine Data Insights On Downtime Only Matter When This Happens…

Machine Data Insights On Downtime Only Matter When This Happens…

You have machines on your plant floor. Sometimes they are up and sometimes they are down. Hopefully, when you expect them to be up, they are up and performing the way they are supposed to; meaning performing as expected, producing an acceptable amount of scrap, etc. But, what if they’re not?

This a major part of manufacturing. Knowing when the machines are running and when they’re not – and if they are running – are they performing the way they are designed to perform? Unfortunately, this equation hasn’t traditionally been easy for manufacturers to solve.

What’s more, many manufacturers see reports on paper that they know aren’t reflective of truth on the plant floor. This means that it’s being reported that machines are up when they’re not or performing better than they actually are. This is because of things like shortstops or downtime that fails to be reported; due to manual processes or improper recordings.

So what have really good manufacturers done to get better data and increase uptime and improve performance?

machine data insights for downtime

Step 1: They understand their plant floor

One of the number one things I hear over and over again from manufacturers that have solved these problems (truly solved them) is that they fixed the “everybody is wrong problem”. What I mean by this is that often all the different departments have different data and are acting on it (or not acting on it).

For example, the plant floor may know that the 3rd shift always starts late. Additionally, the staff on that shift hasn’t been trained as well as some of the others, so they have more shortstops (breaks, etc.) than the other shifts. They may choose to under-report this to align with other shift numbers. Unfortunately, when things are manually recorded these shortstops are left off the final reports and it looks as though the 3rd shift performs exactly like the others in the data, but they don’t.

Later, when reports are pulled, some of the information will show that downtime and production took a big hit during 3rd shift for several months. When they go to see why they won’t find a reason in the data, but in reality, we know an issue exists. Furthermore, even if we did see the issue in the data, we would be finding out months too late to do anything about it.

The manufacturers that do this best understand that all the data needs to line up. That means all the machine data needs to fit two pieces of very essential criteria.

The machine data must be accurate and…

The machine data must be central to every department.

Step 2: Everyone sees the same thing

That second point at the end of step 1 is really what a lot of top manufacturers have in common. Even though automating all data collection from machines offers massive help in this, there are manufacturers that rely on manual data collection that are still able to achieve this and see the same benefits as the manufacturers that automate machine data collection.

Centralizing all of the data – making a single source of information available to every department –  can solve a lot of the problems from our earlier example. If we knew that downtime and under production were issues on the 3rd shift, and everyone had access to this data, we could see that trend developing very quickly in our dashboards; as opposed to months down the line after the business had already lost money.

We could work backward to identify the source of the trends. Providing a single system to put data into can also help streamline manual data entry processes to ensure everyone is entering data the same way. This kind of consistency can help improve accuracy as well.

Step 3: More automation and more data

Lastly, what I see manufacturers who are really excelling at this doing is automating processes and data collection. Those two things go hand and hand; as I have written about before. The more you automate the more data you need to make sure you don’t experience major issues.

Having data automated from machines and centralized in a single place for every department can provide unprecedented reactiveness inside of the organization. Manufacturers can see issues in real time, understand trends quickly, diagnose the root of downtime in a second, and improve the OEE of underperforming machine cells.

This is because centralized and automated data is more accurate and more available. If it is presented in a consumable form to staff, there are few things that offer the ability to impact a manufacturing business more – from a cost perspective than really good machine data insights.

Wrapping it all up

Most manufacturers have these problems to some degree. Understanding downtime really comes down to have good accurate data from machines, and then getting that data into the hands of the people that can solve issues that stick out. Neither of these things occur when data isn’t centralized or consistently collected.

Manufacturing analytics applications like, SensrTrx, offer major ROI for businesses that struggle with these kinds of issues. You can read one of our recent case studies here.

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