What I Love About ProFicient

InfinityQS’ Quality Intelligence solution ProFicient™ has been on the market in the manufacturing quality software space for about thirty years. It’s been a stalwart. A world-class statistical process control (SPC) system. There is love everywhere for the new thing, the latest bauble, but where’s the love for the tried and true, the thing that carries the load, does all the yardwork? I was thinking it’s about time for ProFicient to get some love.
 
I Love ProFicientAnd there is so much to love about this software giant. (Okay, clearly this isn’t Romeo & Juliet, or even Jack & Rose—from Titanic; but it is possible to love a software product…at least I think so.)  In a nutshell, I’d say I love ProFicient because it feels at home in so many environments. There, I said it. And I’ll expound on that.
 
There are many reasons for my love of ProFicient, including the way it handles data, and all the wonderful things you can do with data with this tool; the power of sampling that ProFicient puts in your hands; the incredibly robust analysis of which you’re capable with ProFicient; and the flexibility it allows. Let’s begin our lovefest with the fantastic relationship that ProFicient has with data—my favorite topic and the backbone of solid SPC…the realm over which ProFicient rules.
 

Data and ProFicient

Let’s begin at the beginning. One of the many things I love about ProFicient is the data entry screen—with which every user of this powerful tool is so familiar.
ProFicient Data Entry Screen

Manual Data Entry at Its Finest…and Most Flexible

As an operator, as you key in data values, it’s great that you receive instant feedback about whether the value you entered is good or bad, relative to the spec limits. That’s clear in the image above. Or, if your organization would rather not have operators viewing values relative to spec limits (usually because that encourages operators to input “golden” values), you can simply hide that feature. So, clearly, how you handle that option depends on the nature and culture of your organization.
 
The data entry screen appears to be so simple, and yet offers—and does—so much. For example, all the contextual information is just right there when you open it: part, process, test characteristics…all lined up right there. Any other aspects of the data you might be interested in—lot numbers, tags, and more—are all at the bottom. The “spreadsheet” view makes it seem so simple and uncomplicated.

So Handy

Without ever leaving this data entry screen, the user can access documentation, SOPs, and more. One feature I really like is that the person who configures the tool can also insert links that operators can use to reach out to videos you have made and want to share—training and otherwise. How cool is that? How-to videos are a great way to share knowledge and expertise.

Custom Fit

Anyone involved in data collection on the factory floor knows that there’s a lot to it: gauges set up on workstations, the logical flow of data collection, and more. Your SPC software must be able to handle the many unique sequencing situations that arise for data collectors.
 
The person who configures the software may not be familiar with the data input sequencing and simply arrange the test characteristics in alphabetical order or left to right as they read some engineering schematic. They don’t live it, so they don’t necessarily know it. But ProFicient enables the inputs to be flexible, depending on the needs of the shop floor operators.
 
In addition, ProFicient offers operators many other options, including:
  • Variable and attribute data in the same collection
  • Missing values; skip features; collect out of sequence
  • Change entities being collected during the collection (part, process, lot, shift, tags)
The ability to change entities during collection is particularly powerful. Let’s say you’re in the middle of a long sequence—like 45 measurement values (not unusual). You look at the data screen and realize that you’ve assigned the wrong lot number, or the wrong machine (whatever, you messed up somehow—we all do). The operator can simply click in the data entry screen and, without losing any data, make the change to the lot number or machine, and then save the data. Being able to change things on the fly like that is great.
ProFicient Flexibility
All in all, I just love the manual data entry experience that ProFicient offers—having a “fluid,” flexible software that adapts to the natural conditions of a shop floor is empowering.

Gage Server

Obviously, keyboard entry is not the only way to input data. There are all kinds of data collection methods. And there are all kinds of gauges out there—and some of them date back decades. There’s everything from old stuff that comes in via RS232, to new devices that sense data and send it in via IP addresses. And there’s a whole InfinityQS Gage Serverhost of stuff in between that comes in through Open Platform Communications (OPC) servers. InfinityQS Gage Server can read practically any data collection device with an output.
 
And ProFicient can handle it all. The software has maintained legacy data needs and stays current with modern technology. I love that openness and flexibility.

Fully-Automated Data Collection

Fully-automated data collection—where there's no human intervention whatsoever—is pushed into the system as the data become available. ProFicient can read directly from Programmable Logic Controllers (PLCs) and pull data in from other databases. It can tap into different streams of data that are constantly flowing on the factory floor and just pull samples automatically. It takes all the guesswork out of automated data collection. I love that about ProFicient.
ProFicient on the Factory Floor

Sampling Strategies

So, when we think of SPC and sample size, we think about the number five (or three). But, in reality, this is not always the case. Let’s say I'm measuring three test characteristics on a part, and every half hour I'm going to measure this part, while it's running. Something may happen that keeps me from making certain measurements—a gauge is broken, or something similar—and I can’t quite create a full sample size.
 
With ProFicient, I can carry on and capture what I can because it’s not useless—even if I don’t have a full subgroup. ProFicient can manage that. I love that.
 

Example of a Sampling Strategy

Let’s say we have a sampling strategy where we have multiples streams that may have a unique mean and standard deviation. It's all part of the same output of a process, but there are different process streams. A perfect example of that would be a mold with multiple cavities.
ProFicient on the Factory Floor
So, in this example, I have one press. It's pressing melted plastic material into a mold—and that mold can produce 12 parts, because there are 12 cavities in that mold. So, now I'm measuring different features on that cycle where I've made those 12 parts, wherein I may be looking at flow rate of the molten material, and I also may be looking at dimensional features from each of the 12 cavities.
 
ProFicient understands the sources of variation. We can isolate those, separate those out. This tool is very good at dealing with operations with multiple cavities, multiple fill heads, maybe multiple extrusion dies—all of which may create unique process streams. ProFicient understands all that and can separate out the process streams even though they're all coming in together. Simplifying what can be very complex. What’s not to love about that?
 
[My colleague, Jude Holmes, wrote at length about sampling. Please check out the blog here.]
 

Defects and Defectives

We’ve talked about variable and attribute data, and we know that ProFicient can handle both kinds in the same collection. In addition, there are two kinds of attribute data: defects and defectives, which have very different statistical behaviors. Well, ProFicient can handle those, too.
 
Defectives are pass/fail. Easy enough. A binary output. Defects, on the other hand have what’s called an “area of opportunity,” wherein the product may exhibit multiple nonconformities like a scratch or a discoloration, which is noted by the manufacturer, as opposed to the yes/no or good/bad of defectives.
 
ProFicient understands the nuances of manufacturing, the more sophisticated way to deal with defect opportunities, and the analysis becomes something like “defects per million” or “per thousand”—an important concept in the electronics industry.
 

Last, but Not Least, with Sampling

So, one final thing about sampling in ProFicient that I love: flexibility. If I have a sampling strategy that, upon further thought, requires an additional tag in the data collection, for instance, I can add that in on the fly. Even though I hadn’t captured that in my previous data collections for this sampling strategy, no problem, I can add that “after the fact” and collect it going forward. ProFicient makes sampling, and controlling sampling, a breeze.
ProFicient Sampling

Analysis

Since ProFicient is an SPC product, of course we do control charts. And the control charts library is vast. When you first learn about SPC, you are introduced to seven different types of charts: X-bar-R, X-bar-S, X moving range, c-charts, p-charts, np-charts, and u-charts. (Please see our online glossary for definitions and details, if you like.)
 
Because ProFicient was built with lots and lots of customer input, our library of control charts goes far beyond these seven. We understand that not everything can fit in one of those seven “buckets” of analysis. So, InfinityQS added different functionality to the core seven charts to allow them to model the data even better—no matter what your need.
 
It's not that the user must pick from a list of 300 or 400 charts to find out what works best for them. No, they pick from what they know, the seven basic charts, then add the different types of modifications to the charts to better model what they're doing, and the result is a very well-suited chart for that particular data model. Who doesn’t love customization? I know I do.
Control Chart

But Wait, There’s More!

And when someone wants to do something crazy, like they want to mix different parts on the same chart, well yeah, you can do that, but there are rules of engagement from a statistical perspective. We understand that. And so, we can allow you to put multiple parts in the same chart and still maintain a statistically legal analysis.
 
Control charts in ProFicient provide real-time feedback. If you’re a quality engineer, and you want to pull gold nuggets from the data and make things better for your operation, you need to go to ProFicient—for multi-level Box & Whisker plots and multi-level Pareto charts. I just love how you can go in through the data selection, throw any kind of data into these analytical tools, and then mix and match and drill into that data to determine what it is trying to tell us. Just awesome.
 

End with the Start

So, let’s flip the script and end with the start. What I mean by that is this: the last thing I want to talk about in regard to my deep and abiding love of ProFicient is how easy it is to get started. And it’s a quick process to then expand the ways in which you use the tool, too.
 
So, where do you start with a tool as powerful and flexible as ProFicient? My answer is it just doesn’t matter. That’s right! Start anywhere. How beautiful is that? Allow me to over-simplify, just this once…
 
Just pick up a stream of data and look at it. To quote Dr. Deming from a previous blog I wrote for the Manufacturing Challenges series (Data Overload): “Start with the data. Look at what the data is telling you. Analyze the data and it will tell you what direction to go.”
 
The best way to do that, I think, is to start super small and take advantage of the enterprise nature of ProFicient and grow into the next stream of data, and the next, and the next...
 
So, that’s where I’m at. Those are some of the many reasons I love ProFicient. It just feels at home in so many environments. It’s not Jack and Rose, but I’m pretty sure it’s love.
 
Find out all you can about ProFicient in the Products section of our website.
 
Steve Wise
By Steve Wise
Vice President of Statistical Methods
See Full Bio

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