Here’s The Manufacturing Data You Absolutely Don’t Need!
You don’t need all that manufacturing data.
To some, collecting any kind of machine data from the manufacturing floor can seem complex. I hear actually this a lot. “All data collection technologies have all these features, and we are too small, or our processes are too simple.” This is a really common outlook.
Other people tell me that these same kinds of data collection technologies and methods are too simple. “We need SPC (statistical process control), ERP integration, MES functionality, etc.” .. the list goes on.
So, which is it? Are some manufacturers just way behind; while others are way ahead of everyone else – technology developers included?
Both groups are wrong
The first group – the one that thinks they just need some very simple/basic data – is assuming they need a ton of data to make meaningful change in their organization and they can’t handle it.
The second group thinks they need a ton of data as well and if they don’t have it they can’t make changes, they believe more data is always better.
When I was helping us build Mingo, and began working with our first customers, I learned that both these groups are fundamentally wrong.
While Mingo can handle tons of data and make it meaningful to anyone in manufacturing, it turns out that YOU DON’T NEED ALL THAT DATA to make meaningful changes in your processes and manufacturing throughput.
Single data points can make a big difference
The reality is with a single data point you can do a lot.
For example, we are working with a stamping company that wants to measure the downtime and utilization of their presses. We can do this with a single data point.
That manufacturing data point is a cycle. As in, “did the machine cycle”. Pretty simple, right?
From that manufacturing data point, we can measure availability, cycle time, downtime, utilization, and part counts. And with Mingo, we are doing this in real-time.
The “we are too small” problem
If you fall into the first category, thinking you are too small and everything you would need to do with data would be too complex, you are probably thinking this is great but what can I do with data gathered from a single data point?
Two very powerful things:
- You can determine which machines are having the most/least downtime, this seems obvious I’m sure. But with this data, you can go to the different operators and talk to them about why this is happening. You can talk to maintenance and engineering as well. Now, you are all looking at the same numbers, the same data focusing on your biggest downtime issues and the machines that cause the most problems. You are no longer using anecdotal information or gut feel on which machine you think is the problem. You are using cold hard facts. From just 1 data point.
- Total throughput of the plant. With one data point, we don’t know exactly which parts we are producing but we know how many times the machines cycled which can give us a total throughput number for the plant. We can use this number to make sure we are meeting our production numbers on a daily, weekly, and monthly basis. And if we aren’t, just as with downtime, we can have discussions using real data from each of the machines about why we are or are not reaching our goals.
The “we are going to need lots of manufacturing data” problem
Now, for those of you who love tons of data and fall into the second group I described above, the group that wants SPC, ERP, MES, CMMS, etc. You are probably thinking that there is not enough data to solve all the problems you have on the manufacturing shop floor.
Let’s start with a scenario.
Say you want to know exactly why the machines were down, what was happening when they went down, and measure against my real cycle times.
My answer is, you’re right you need those things.
But, you don’t need detailed millisecond-level data about what is happening on the machine to make that happen.
We only need 2 more data points: Downtime codes and standard cycle times for your parts.
This data is very easy to get. For cycle times, we can derive it from the equipment, a program change on a CNC, a recipe change, or some other setting in a PLC. The same is true for downtime codes.
We can typically derive why a system is down by the state of the machine. For cases where we can’t collect the data automatically, there are usually other methods available. For example, at Mingo we have a very simple manual data entry screen to collect it.
Make your manufacturing data purpose-driven
They call it continuous improvement for a reason. You have to continuously do it to keep improving. As Lao Tsu said: “A 1,000-mile journey starts with a single step”.
In all the cases I see, Mingo is the first step on the continuous improvement journey, and it grows with our customers until that last step 1,000 miles down the road.
This should be the goal of both of these groups of manufacturers. To get started by collecting data with a purpose. To improve production with analytics. Discover what simple data can deliver the biggest ROI and go from there.