Manufacturing Data Is Worthless Without… Context
Sort of a salacious title, right? It’s true. Manufacturing data is worthless without one simple thing.
Every year I watch manufacturers ignore important issues and spend hundreds of thousands of dollars trying to solve manufacturing challenges using data, but still, completely miss this important component.
What’s more, this data is responsible for influencing downtime, throughput, scrap rates, scheduling, and more. If manufacturers miss the mark on this, I’m afraid that all of these things are affected as well.
What am I even talking about?
Even more, when I talk to people especially tech people about Mingo they always say, “Oh, that’s like IoT for manufacturing,” and then they start asking me if I have heard of this sensor or that IoT company. Then I get asked if Amazon, Microsoft, and Google are competitors. What is interesting about IoT today is that everyone is focused on data and sensors without context.
To reiterate the point once more, data is meaningless without context.
Manufacturing Data in Context is Everything
I know that might not seem like a sexy answer at first. You’re probably thinking, “Why is context so important if I have an abundance of data?”
It might also seem like some kind of self-serving sales proposition meant to highlight Mingo. But, it’s not.
Context is fundamentally the most important thing for manufacturers to understand about their machine data and manufacturing processes. It will ultimately determine whether or not something on the factory floor is fixed, broken, or actively costing money as I type this.
That’s a huge deal.
Below, I will show you exactly what kind of context is important. It doesn’t matter how you are able to achieve this… as long as you do it.
Power Usage Spike Example
Consider this, you buy a fancy new energy monitoring sensor and put it on a mill or lathe in the manufacturing plant. You get some amazing graphs showing energy usage, like the one below.
Looking at the graph you notice that you use more power from 1 pm to 4 pm every day. Great, but now you want to know why? And you ask the following questions:
- What part are you running on that machine?
- Who was operating it?
- How many parts did we produce?
- Did we have any quality issues?
How are you going to get that information?
To get to the root of the problem, you may even need to ask even more specific, detailed questions:
- What was the load & rpm of the spindles?
- Did we produce any scrap?
- Were there any system alarms?
- Did we change tools?
- What shift was it?
- Who was operating the machine?
You can walk the plant floor and ask the operator and he might remember what happened last week. You can look in your ERP system to find out what parts should have been running on that machine. You can talk to the supervisor of that department.
Talking to all of these people, you find out that they were running the same parts they do every day, and nothing out of the ordinary was happening.
Ok, but there has to be a reason for the increase in power usage during that time. So now what? Now you go talk to maintenance and ask them why the power usage is spiking during that time period. He says he did not get a call about anything for that machine during that time.
The only way you can find out what is happening is to stand by the machine and watch the operator. And you don’t have time to do that.
The Machine Downtime Example
Here’s another example, this time reflecting machine downtime. Imagine you have a particular machine cell with higher than average downtime.
What data would you want to be able to properly solve this problem?
- How does it compare to other cells?
- When is it occurring?
- When did this start?
- What is the cost?
- Who might know what is happening?
- Have we seen this issue before?
- What other information is specific to your manufacturing business processes that could play a part?
It’s all about looking at this problem in context. Understanding history, evaluating trends, and tapping into all the sources of knowledge of an issue is critical to diagnosing it.
However, diagnosis is just one step.
Imagine you discover it is the 2nd shift causing all the problems. Perhaps that shift is understaffed and the reason that particular cell may be having longer changeovers during the 2nd shift due to staffing issues.
You could easily do the math to determine the cost of solving this problem. You could also highlight the money saved from fixing it long term.
This allows you to solve the problem and justify the costs of solving it.
But more importantly, you can now monitor the problem into the future and understand if the problem is actually fixed.
Now, a Classic Sports Analogy to Show Why Context Is Important in Everything
Before your eyes glaze over, let me show you why context matters… a lot.
Quarterback #1 in Rookie Year
- First pick in the draft
- Completion percentage = 63%
- Interception percentage = 3.3%
- Avg. yards per completed pass = 8.6
- Avg. yards per game = 233
Quarterback #2 in Rookie Year
- First pick in the draft
- Completion percentage = 56%
- Interception percentage = 4.9%
- Avg. yards per completed pass = 6.6
- Avg. yards per game = 164
Both quarterbacks won roughly the same number of games this year as well.
With this data, it’s safe to assume that Quarterback #1 is outperforming Quarterback #2.
We all can probably guess where he is headed.
If I asked you to choose which you’d pick based on this data, you’d know I was setting you up for a gotcha moment.
You’d be right.
However, if I told you that prior to selecting which Quarterback you wanted, I would provide any additional information you wanted, you would probably ask me to supply a lot more data.
Getting more data about those quarterback’s first seasons probably isn’t going to help you understand which quarterback is the best choice; as we know Quarterback #1 looks to have had all-around better stats in their rookie year.
You would probably want to know a little historical background, but you’d really want to know what happened next. How did their careers pan out? What were stats like in years 2,3, and 4? What about Pro Bowl selections? That’s the power of analytics in football.
You get it. I could go on forever.
You need contextual information.
It is super easy to be fooled when all you have to look at is data on its own, no context.
You need context. Otherwise, people and machines can trick you.
Quarterback #1 was Tim Couch and Quarterback #2 was Peyton Manning.
(Wouldn’t it have been frustrating if I would have left that out? As you can see, analytics contextualized data plays a role in nearly everything, football included.)
Why Do Manufacturers Have Data Out of Context?
A lot of manufacturers are missing this context (more than you might think).
This is partly due to outdated collection methods and partly due to how the data is stored and used on traditional manufacturing shop floors.
Often, data is manually collected or collected from individual machines and stored in multiple systems. This usually means that machine and performance data are looked at independently of overall operations.
It can be really hard to marry all of these snapshots of data together into something actionable or meaningful.
What’s more, even if this data is organized together somehow, most manufacturers only look at it retroactively — in meetings or reviews. This means all the opportunities that may have existed to fix something or prevent a costly issue, have already passed.
4 Steps of Context: Monitor, Diagnose, Solve, Monitor
These 4 steps are how you need to be thinking about manufacturing data contextualization.
Ask yourself these questions about your manufacturing business.
- Can we monitor our machines and processes in real-time?
- Can we understand problems in the context of history and trends?
- Is someone able to actually diagnose and understand these issues as they happen?
- Can we calculate what these things are costing us?
- Can we use this data to solve the problems we identify before they cost us money?
- Once solved, can we monitor this moving forward to ensure it is solved?
- Can we measure the ROI of solving this problem?
If the answer is “No” to any of these questions, you can work backward from there to understand what gaps exist in your ability to understand manufacturing data in context.
If you want to truly affect downtime, performance, throughput, and other manufacturing KPIs, this is the information you need. You need to be able to answer all of these questions.
Context Makes Data Meaningful
What you need is CONTEXT. You need a system, like Mingo, that augments the data and includes what you need to diagnose these issues.
You need all of this information to quickly and easily understand these issues without spending half of your day talking to everyone in the factory about what happened and why.
The examples above are very simple. Considering the power example, many of the experienced manufacturing people reading this will know that the tool is probably wearing out during this time and it takes more power to make the cuts. Then, the tool is changed and the power usage decreases again.
But if you think about issues with scrap, machine availability, and why it is happening, the answers get much, much more detailed and harder to figure out.
Knowing the detailed state of your equipment, who was running it, and what can help you identify trends and fix them. But without the context, this is impossible.
To learn more about how to get context and actionable insights from your manufacturing data Request a Demo Today!