When Manufacturing Data Analytics Ends Up Making You Mad
Imagine, the founder of a manufacturing data analytics company telling you about how analyzing your machines and analyzing your manufacturing data to increase production could ultimately end up really bumming you out? That’s exactly what I’m prepared to do (and in the process, tell you how you could end up improving manufacturing production with the right analytics.)
It’s true, I speak as an expert at a lot of major manufacturing events and the “data” is overwhelmingly telling me this is the case (data = what good manufacturers tell me).
Most manufacturers start their journey towards better data by saying, “We just need to know this simple handful of data“. The theory being that those simple things would provide some pretty dramatic improvements.
It makes sense on paper, but it always ends with manufacturers being really frustrated. Then, they do one of two things…
2. Something that costs them too much money
How to Collect Simple Manufacturing Data to Increase Production
Collecting simple manufacturing data isn’t that hard. Most manufacturers seeking to understand the simple stuff — like when the machines are up, when they are down, when they are producing to schedule, and when they underperforming — run into the same problem.
They do the bare minimum to achieve fairly accurate representations of this information and then ask why?
Why are we producing less during the second shift?
Why is downtime higher in the evenings?
Why did we produce 50% less according to schedule on Tuesday the 9th?
Using free — or very cheap — tools to understand basic OEE metrics and manufacturing KPIs leads to overwhelming frustration. That’s where I come back to my two bullets above.
Why Simple Manufacturing Data Drives Leadership Crazy
Not being able to understand why something isn’t the way it’s supposed to be could drive anyone crazy, but it becomes even worse once you know exactly what’s wrong but don’t have the ability to dig into why it’s happening (which hurts your ability to increase production).
This is a common scenario I see manufacturing floor managers run into all the time with manufacturing executives. They now have data showing some of the errors occurring on the plant floor, but they can’t figure out why.
It’s almost always stuff that the people working on the floor can tell you about in one-off scenarios, but cumulatively, it is not really being recorded in any way. This results in those simple metrics highlighting the accumulation of all these little things in reports that leadership sees.
Why were the machines down Tuesday around 3 pm? Maybe unscheduled maintenance, maybe the shift was short-handed and needed to perform a fast changeover, maybe Jerry went on a break???
Leadership wants to know why, and this usually means one of two decisions is rendered:
1. Data is deemed not helpful and abandoned (money down the drain in many different ways)
2. The company wants to understand why and thinks they will need a lot of custom tools and configurations to do so
Doing Manufacturing Data Analysis Without Spending a Stupid Amount of Money
So, let’s pretend that neither option sounds good. Good idea. Neither are good.
How do you get the data you want without spending ungodly amounts of cash on something like Tableau since traditional manufacturing software solutions are ridiculously expensive?
Well, step 1 is avoiding business intelligence solutions like Tableau. It’s not that you cannot configure these tools to deliver exactly what you need to know… they can tell you everything you want to know… but…
- These tools do not acquire data. They rely on existing databases. Companies must deploy new data acquisition tools in each factory and synchronize that data into Tableau (i.e. buy more software).
- These tools do not automatically contextualize any data. The existing data must be processed to add production standards, shifts, part numbers, etc. (consultants or internal employees will rack up hours doing this).
- Any KPIs have to be built from scratch in Tableau, nothing comes out of the box (this can really take a long time if you think your business is different from other manufacturers).
So… you probably want to hear the part about not spending a lot of money. Well, it is a bit shameless on my part, but that’s why I built Mingo. To solve this exact problem.
Manufacturers want good data without buying any additional software, configuring anything using consultants or internal IT staff, and they would prefer it not to be a big costly hassle.
Mingo aside, that’s the whole point of industry-specific, manufacturing analytics solutions.
Why Data Analytics Systems Need to Fit Manufacturers
Out of the box, manufacturers should be looking for an analytics solution that can tell them WHY. Why were machines down, why are the machines producing less… etc.
Look for a solution that fits your budget that will allow you to understand a simple question like:
I see the machines were down and we didn’t produce to capacity on Tuesday between 3-5 pm, why?
You want to be able to see things like changeovers, machine error/alarm codes, and trends in performance during similar time periods.
What About All My Manually Entered One-Off Data?
This is actually one of the things we have found that has helped manufacturers the most recently. When trying to digitally capture the nature of how your shop floor is running, it is nearly impossible to start without being able to record all the weird stuff that happens on a nearly daily basis.
Being able to input, capture, and record manual data is really important, and something that is often missed even by the most expensive and sophisticated systems.
If you’re just starting out, make sure you find ways to capture and record events from the people that actually have boots on the ground.
Make the Data Work for YOU
And to start collecting the data needed to improve manufacturing production, both automatically and manually, you need the ability to make that data work for YOU.
The ability to take an analytics tool, like Mingo, out of the box, gives manufacturers complete control of their data so it can be utilized to its full potential. But, it doesn’t stop there. There’s also the ability to take what you need and make it work for your factory.
Technically, it’s customization, but not the kind of customization you’re thinking about. You won’t need the help of fancy (and expensive) consultants or the IT team, but rather, you choose customization based on the needs of your company.
Customization in manufacturing analytics? What does that mean? Isn’t that exactly what you said to avoid earlier? Well, no. In the next few paragraphs, you’ll learn why you should use customization in manufacturing analytics and how to implement it in the correct way.
In our world, customization is a feature of Mingo that lets the user have complete control of data in the manufacturing environment. So, don’t get customization confused for something that requires extensive knowledge of coding or expensive consulting. Put simply, Mingo doesn’t require either of those things to give manufacturers what they need to meet their goals.
When we talk about customization in manufacturing analytics, it means getting the data the is most important to you and that will help you improve production. This will look different for every company, which is why we believe that being able to customize data is so important.
The way one company views data on a dashboard may not work for another for a variety of different reasons. The goal is to ensure every manufacturer can use the software to gather and contextualize data to its full potential, enabling data-driven decisions and an increased profit.
How Do You Customize Manufacturing Analytics to Improve Production?
The idea is to provide full control of your dashboards, giving you the ability to use business intelligence (no, not that other expensive software) in a way that makes sense for your company. Using both the pre-built and customizable dashboards is very specific to the data you need and use to reach your goals, which will vary for each manufacturer.
Do you want data displayed in different units? You got it.
You want your dashboard display to be organized in a way that will better suit your line? You got it.
The options for customization in manufacturing analytics are made to fit each manufacturer’s needs.
We don’t believe that companies should have to conform to a set standard, but instead, believe that manufacturing analytics should adapt to the individual needs of each of our customers.
So, Where Do I Start?
Customization in manufacturing analytics is seemingly limitless. But just because you have a wealth of customization options does not mean you should start by implementing all of them at once. The last thing we want to do is to turn the ability to customize into something that ultimately hurts your manufacturing process and inhibits your ability to increase production.
Start small and then work your way up. Set basic goals and customize the data you’re viewing to ensure you are seeing if those goals are met, one step at a time. And when they are, celebrate the small wins. Then, continue to customize to your needs to work towards bigger wins with more metrics, data, and machines as it makes sense.
The process of starting small will help work your company towards winning the day.
We want the flexibility of our system to help you move forward, not backward. Too much customization at the beginning could cause confusion for both everyone involved.
Customization provides the ability for manufacturers to see data needed, when its need, and in what format.
“I Just Want Simple Manufacturing Data Analytics.”
We all do. But what you likely really want is data presented in a simple way, that allows you to dig down deep into details as needed.
This is what you should strive for right off that bat.
Projects that spend the bare minimum to capture top-level data are almost always long-term failures. Those systems are quickly outgrown (usually day 1), and then manufacturers are forced to make one of those two awful choices — or if at this point, they considered a third choice, a manufacturing analytics solution.
A Real-World Example: Versatech Uses a Single Data Point to Drive Change
Recently, we finished a comprehensive case study with Versatech – a full-service engineering, manufacturing, and consulting company – who recently launched a company-wide initiative to leverage data more effectively to reduce overtime and improve manufacturing production and efficiency.
In the following sections, we’ll highlight their manufacturing engineering efforts, approaches, and results inside of the overall initiative.
Manufacturing Production and Analytics
“We wanted to use data better to look at downtime… make sure machines are running properly… and focus on improving machine uptime and reducing downtime”, said Scott Strutner, Manufacturing Engineer at Versatech.
Versatech elected to implement Mingo Manufacturing Analytics to aid in the process of tracking uptime, scrap rates, and machine performance in real-time, all to increase production. They were also using the technology to monitor OEE trends over time, by cell, machine, and more.
“From my seat, I use Mingo as a tool to see if the changes being made are making a difference. It does allow us to see where OEE and downtime is trending…”
Manufacturing Engineer, Scott Strutner
Ultimately, Versatech wanted greater visibility into what was happening on the shop floor. They wanted to see machine performance and uptime in real-time so they could adjust and affect problems as they occurred; not after the time for savings was lost.
They also wanted to be able to see OEE and manufacturing production trends over time. This department used Mingo dashboards to easily see how changes they were making would affect OEE – both positively and negatively – without manual tracking or retroactive guessing.
Better Data, Better Results
By leveraging the dashboards and centralized data inside of Mingo, the manufacturing engineering department was able to better track machine performance and OEE trends and was able to increase their OEE by +30% in a matter of months.
Mingo dashboards allowed Versatech to view high-level stats they needed to be track and the ability to look at the history of their fluctuations; allowing them to look at what caused positive or negative impacts at a very granular level. This allowed them to look at last month in comparison to the current month, do trend analysis, track total downtime, and increase production.
“Overall, I think Mingo has had a massive impact. It is a great tracking tool, but I think the bigger impact is that it helps us easily focus the feedback from production on issues and areas for improvement ”
The Next Step
This positive experience using data made Versatech more excited about the possibility of using machine data to improve production and business practices, with the use of manufacturing analytics.
Versatech CEO, Chad Hill, was quick to point out that one of the next steps for leveraging data on the shop floor would include using data inside of Mingo to impact on/off state utilization of machines. This would allow them to optimize machine efficiency at an even greater level than they are today.
“Use it (Mingo data) and integrate it into your daily/weekly processes,” Strutner emphasized.
The full case study can be found (and all the associated processes and cost-savings information) here.
And remember, the goal of analytics isn’t to do nothing or spend too much money on complicated, overly expensive situations. The goal is to improve production with good data, without buying additional software, configuring anything using consultants or internal IT staff, or with major costs.
And, when you’re ready to get started improving production with analytics, check out the 6 questions we ask manufacturers before implementing a manufacturing analytics solution.