Data collection is the foundation for how InfinityQS quality management software
helps manufacturers significantly improve quality, reduce waste, and improve efficiencies.
Three types of data collection exist in manufacturing facilities: manual, semi-automated, and fully automated data collection. Let’s start first with manual data collection. When you think of manual data collection, it’s likely that your thoughts drift towards using paper and pencil. If so, you wouldn’t be too far off the mark. Lots of large organizations still leverage paper-based quality systems on their shop floors. That’s a big problem. Moving to digital data collection in quality software—especially through automated data collection methods—reduces errors, facilitates easier data sharing and reporting, and improves efficiency.
As I have pointed out in videos
and other blogs
, using paper on the shop floor is passé and, unfortunately, too common. Some argue it is less expensive than software (it’s not). And some just find it hard to change old paper-based habits. Regardless, I believe that using paper is the invisible dinosaur that lurks in modern manufacturing sites, creating panic, chaos, and havoc when information and reports are required by managers and customers. Using paper is expensive, archaic, and the source of lots of shop floor issues.
The Paper Problem
First, humans are imperfect. Writing things down on paper inevitably results in illegible content, transposed numbers, faked data (sadly, it does happen), and other issues.
Sometimes data collection papers are misplaced or even lost. Considering how busy shop floor operators are; they can’t be wasting time hunting down misplaced papers. Plus, these papers often do not have specification limits written on them. That makes it difficult for an operator to assess whether the data they’ve just recorded is good or bad. Instead, it’s just a data collection exercise. That can be frustrating for operators.
And then there’s the issue of managing the paper. Someone (a manager or quality professional) typically wants to review it. That’s very time-consuming and fraught with delays and transfer issues. Once reviewed, the papers need to be stored. And just how long do you keep the paper? Years? Then, once the paper has sufficiently aged, how do you discard it? Does it get shredded? Burned? Left to rot in an old warehouse? There’s no consistent answer, as I’ve seen companies manage old paper in a variety of ways. I’ve even visited companies that employ on-site librarians
just to manage the production-based paper that’s generated every day at their facilities. Some organizations use so much paper that they need a separate warehouse just to store it all. The bottom line: paper is expensive and time-consuming.
Compliance Issues with Paper
Most companies require data to be collected at specific intervals to comply with regulatory or customer-specific requirements. For example, say that compliance requirements specify that certain data must be collected every hour. Well, paper doesn't know that, and it can’t remind an operator when to write data down. The operator has to think, “Okay, wait a minute. When's my next data collection?” And then, these extremely busy people, must remember
to write down their data. Through no fault of their own, operators could accidentally miss required compliance-based data collections. Missed compliance data collections can spell trouble for manufacturers.
Lastly, it's extremely difficult to create reports from data that's written on paper. To do so, you've got to transcribe numbers into spreadsheets. Again, you're dealing with written numbers and the potential for human error. Data could be lost or mistyped. If data from the paper is illegible, what should be done with it? Plus, numbers could accidentally be transposed or erroneously entered in a spreadsheet. Even if data could be perfectly transcribed, you still have to figure out how to create reports from the data. Basically, using paper is a mess.
Manual Data Collection with Software
Let’s not discuss automated data collection just yet. Instead, let’s discuss replacing paper with a simple software interface for entering data. First, using software is far better for operators than using paper. Here's why: Usually, when you type numbers into a software product, that data is stored where it's easily accessible by lots of different people. That’s a good thing. Also, when those numbers are typed in, there's less likelihood that the numbers will be illegible. They'll be digital
numbers. A nice step forward.
However, it's still possible someone could mistype or enter erroneous numbers. However, InfinityQS software has fail-safe means of ensuring data accuracy. Our reasonable limits ensure that unreasonable
(erroneous) data is not entered. Plus, operators can view their product’s exact specification limits, ensuring that they know whether data values meet or exceed requirements.
Also, data can be easily summarized and presented to operators in a simple, easily understood format. Reports, charts, graphs, and summarizations are provided to the operator immediately following each data entry. These data visualizations are, quite simply, pictures that summarize data. The old adage, “a picture is worth a thousand words” is relevant here. Those data pictures are a means of communicating to the operator critical information that could be used to improve their processes, empowering operators to ensure the highest quality products are being made.
Sharing the Data
As mentioned, once data has been entered, it's not just accessible by operators—it’s accessible by lots of different people: engineers, managers, and quality professionals. And that’s a huge benefit.
Managers, engineers, and quality professionals want to understand how their production lines are performing. Paper cannot support these needs. Raw numbers written on paper don’t have context and cannot be readily compared with customer requirements. But when doing manual data entry, entered values can be checked against specification limits, against control limits, and against reasonable limits. By doing so, InfinityQS software ensures far greater accuracy and correctness than could be achieved using paper.
Semi-Automatic Data Collection Makes a Difference
You’re in the grocery store. You opt for self-checkout. As you start scanning the items you’re buying, you suddenly realize, “This is semi-automatic data collection!” Okay, maybe I’m the only one who does that…but the technology in grocery stores is pretty cool.
Semi-automatic data collection requires a measurement device and a human being to manipulate it. The measurement device, when actuated by the human, can transfer data to a computer. But again, the device must be manipulated in some way by a person. The types of semi-automatic data collection on manufacturing floors include scales, micrometers, calipers, densitometers, barcode scanners and lots more. What do they have in common? A means of digitally measuring and then transferring values electronically to software…after the human has pushed a button.
Using semi-automatic data collection devices is a great way to ensure accuracy and completeness of information. It’s also very fast, and helpful for operators.
Tremendous accuracy is an inherent characteristic of semi-automated data collection. InfinityQS software can connect to virtually any of your shop floor gauges.
Plus, our software ensures that your compliance and quality data is collected when you need it collected. Paper cannot enforce compliance. But with software you can set up triggers and notifications that remind the operator to collect data based on the frequency you require. And in the rare instance that compliance data collections are missed, our software triggers notifications to ensure that you instantly know what’s happening.
Fully Automated Data Collection
Fully automated data collection is performed with measurement devices that an operator does not
manipulate. For example, a manufacturer might utilize programmable logic controllers (PLC) data streams—for capturing and revealing machine speeds, feeds, and temperatures. That's a great example of a system that sits out there in the manufacturing space collecting data (or presenting data to people) all the time. Why not tap into it?
Our software can dip into those data streams and gather data on a regularly scheduled basis. Data streams can even be automatically sampled. Once data is gathered, charts and graphs are automatically updated and presented in real-time to operators and others. Automatic updating of charts is another huge benefit to an operator—it allows the operator to further reduce the amount of time required to work on the computer or manipulate a gauge. By minimizing that time, operators have more time to attend to quality issues, control their processes, and ensure compliance.
If collected data violates specification limits or generates a statistical alarm, then operators, managers, and support personnel are notified automatically. InfinityQS software sends emails in real-time so that, even if you are travelling, you receive immediate notices of quality issues.
Generally speaking, fully automated and semi-automatic data collection provide much more information to your operator, in a shorter period of time. In addition, automated data collection methods greatly improve focused process control efforts, but the benefits extend beyond the plant—to regions and to the entire enterprise. Why? Because InfinityQS software is built to roll up, summarize, and aggregate
your data, allowing you to sort, slice, and dice it any way you want—features that help you slay the paper dinosaur.
Aggregated data enables you to review quality performance across production lines, shifts, facilities, and geographical distances. This bird’s eye view
is critical for prioritizing quality improvement actions for your limited quality resources—and focusing those actions on the most important quality issues in your entire organization.
As I mentioned in a previous blog, SPC: Hunting the Big Picture and the Big Payoff
, to maximize your return on your quality system investment, “you need to focus on the big picture of what’s going on across your organization. Instead of focusing on quality problems, you need to look at data that represents quality information across multiple production lines, shifts, plants—across the entire enterprise.”
The big picture is the key. “My belief is that more companies need to focus on the big picture of extracting manufacturing intelligence
from the quality data they have already collected. It’s not hard. You just need systems that will support shop floor, enterprise-wide data collection and a means of aggregating that data and making it easily consumable and understandable by managers, engineers, and quality professionals.” That’s where we come in.
Take advantage of the technology at your fingertips today: contact one of our account managers (1.800.772.7978 or via our website
) for more information.