I have the benefit of having seen how thousands of manufacturers collect machine data. Everything from the most efficient manufacturers to the least. As you can imagine, I have seen a lot of strange stuff, and a lot of really “interesting ideas” along the way.
As a whole, manufacturers are usually sort of self-conscious about this subject. The folks on the shop floor understand how accurate/inaccurate the metrics the execs look at are but have very little idea of how to improve these things day-to-day. And, the execs generally struggle with understanding just how accurate the data really is; eliminating their ability to actually change the things that might matter.
The truth is that many aren’t doing as bad as they think they are, while quite a few others are actually doing much worse than what they’ve estimated. It all really comes down to machine data collection.
The Worst Machine Data Collection Method Is (Drum Roll)
So, I mentioned in the title that I would answer this question. I won’t let you down.
Every manufacturer is different. Almost every manufacturer I speak with tells me something that is unique about their business. Something they do differently than everybody else. A reason why they HAVE to have different standards, processes, etc.
However, across the board, there is one way one to collect machine data that I have seen time and time again fail manufacturers. Manual data collection.
Why Manual Machine Data Collection is P
robably Costing You
Well, first, let me clarify exactly what I am condemning. A very large amount of manufacturers still collect some data manually. In many cases, this is required to some extent, but a heavy reliance is almost always a red flag if you’re concerned about leaning out costs and decreasing things like downtime.
Let me outline the exact scenario I commonly see unfolding. A manufacturer may have paper job travelers going through the shop determining when people are working, when they aren’t, how much scrap was produced, etc. Then, they record it all in Excel (or some kind of custom spreadsheet) and then find a way to make it actionable later on.
But, this is kind of a lie that everyone tells themselves. Most hope this data will one day be able to be used for something actionable, but in almost every case I’ve seen… it’s not. Or, it is, but it is inaccurate and wholly unhelpful.
Where’d All My Machine Data Go?
Unfortunately, manufacturers almost never use that manually collected machine data to enact significant change on the shop floor. It’s too hard. The data just sits in spreadsheets – often ones that don’t have a common format or a common collector (making all the data itself vary wildly; assuming it’s even somewhat accurate, to begin with).
This DOES cost money, increase downtime, and it negatively impacts everything from production to quality. Almost everyone at every level inside the business knows this but no one has the visibility needed to actually fix the problems.
When no one has visibility into that machine data, those numbers are only discussed at production meetings, all the time is lost. The details of what happened disappear and the opportunity to fix problems go away. When everyone has visibility into accurate, detailed data, then you can start to identify chronic issues and solve them easily. This only comes with the people tasked with solving the problems that can understand the data the way that the people identifying the problem can.
The greatest obstacle to change that I see organizations have is… that they believe that their data could only be collected manually, OR that any other machine data collection methods would be too big of a hassle or another major technology project.
It’s Time to Start Small
I have one piece of advice for manufacturers that are in one of those two scenarios. It’s the same thing I’d tell my best friend or even my wife – you know if they ran a manufacturing plant.
1.) Our data has to be collected manually:
No, it doesn’t. At least not all of it. Even if it does, it needs to be centralized.
I’ve never met a manufacturer that needed all data manually collected. The key is to start small and automate the really arduous stuff first. Start there, see what kind of results you get.
See how the automated data differed from your manually collected data, previously. Take a look at the man-hours that you’ve saved and what other metrics are positively affected by this.
In most cases, these types of simple projects take no time to implement and are incredibly cost-effective. The ROI usually fuels additional projects in this direction.
If you do collect it manually, work on centralizing and standardizing the data and making it visible to all the people that matter. Collecting the data can end up being a huge waste of time if you can’t draw insights from it.
I know manufacturers that collect data manually and then input it into an analytics software just so they can have it contextualized for different people inside the business.
2.) It will cost a fortune, be complicated, and everyone will hate it:
But, also maybe not!
The truth is, people hate complexity and executives hate high costs and long waits until ROI is achieved. Traditionally, automated data collection projects have gotten sort of a bad wrap for this.
The secret is not jumping straight out at a full-on super project. Don’t go out and buy Tableau or try to wrap this project into a big manufacturing software implementation endeavor. That’s asking for a pretty high cost and a fairly complex/lengthy implementation.
Approaching data collection by itself can prove to be inexpensive and far easier to accomplish quickly. The key is getting that data into some kind of form that gives everyone visibility into the data so they can make changes when they actually matter (before things get bad or worse).
Mingo, for example, collects data directly from the machine and automatically translates it into dashboards by job role, function, and more. These kinds of solutions usually offer ROI in weeks not months.
Wrapping It All Up
If you collect machine data manually, there’s probably some serious bucks to be saved. It’s just best to start small. This is how smart manufacturers introduce themselves to concepts like the IIoT and digital transformation. Even getting the data centralized into a single source and contextualized for everyone inside the business can offer big benefits.
Small steps are the keys to avoiding major costs and complex, costly projects. Trust me, I used to run a major consulting firm and we saw a lot of those. Identify some data that you’re fairly sure could be collected automatically, find a simple manufacturing analytics application that can help you collect and contextualize the data, then evaluate the ROI. This can help fuel larger projects in the future – that people will be happy to adopt.