Welcome to the blog series written specifically for you, the Quality Professional. In this series we’re going to talk about how you view and use statistical process control (SPC) data, new ways to look at quality projects in your workplace, and the changing perspectives about quality data. Along the way, we’ll look at new and exciting ways in which you can achieve quality improvement in your operations, and how you can get the most from your SPC solution.

Look at Your Enterprise from Every Angle

Quality professionals and upper management commonly separate SPC from business improvement activities. It’s because the daily firefight associated with quality issues drains so much time. As a result, it’s difficult to step back and strategically use quality data. I believe strongly that SPC should be used to strategically drive business improvement activities. The finest SPC deployments I’ve encountered have done so.

I’ve found that huge business gains are the result of a holistic view of quality wherein data is used to evaluate operational performance. By evaluating the holistic big picture, large-scale opportunities for improvement can be identified. In turn, these opportunities can be leveraged to pave the path to transforming business performance. But you’ll need help. Most SPC systems focus on the shop floor without providing you that holistic view of quality.  However, modern quality solutions should help you do just that.

Okay, a quick word about “holistic.” We’re not talking about new age, chasing butterflies, lying down in a field to become one with the planet here. We’re talking about using technology (in this case, quality technology) to its fullest potential—to look at your enterprise in its entirety so that systemic improvements can be made that can improve your business as a whole.

Modern SPC Solutions Can Help You See Your Operations Holistically

Let’s consider the huge quantities of SPC data already gathered on the shop floor by your operators. That SPC data is available to you right now. A modern, cutting edge quality solution can help you, the quality professional, roll up and aggregate data.

*Click the image to view a larger version.

And that aggregated data can give the business leaders of a plant, a region, or an entire enterprise the ability to quickly see problem areas (what we at InfinityQS call “red cells”—see the example graphic below) and react accordingly.

These red cells are the places where quality opportunities exist. Business leaders can prioritize red cell improvement activities and then unleash Six Sigma teams to tackle them in order of significance.

By looking at your data in this way, you are not separating SPC from quality improvement. And you are not separating SPC from Six Sigma activities. In fact, it’s the opposite: You’re using SPC data from the shop floor to drive large-scale improvement activities.

My opinion is that Six Sigma projects should be driven by the data your operators collect every day on the shop floor. Not only is that data a part of a holistic quality endeavor, it actually drives holistic quality initiatives.

SPC is Not Just a Control Chart

What’s the power of a software-as-a-service (SaaS) quality solution? Well, it’s many things, but the aspect of a SaaS solution that stands out to me is that all that quality data is in one place.

So, if I’m an operator in plant number 1, on production line number 16, I only see my data…and that’s great. It keeps me focused on what I need to do to control my process. But data being collected by all the other operators is also saved in the same data repository—even if those operators are in plants on the other side of the world. With SaaS quality solutions, it’s a “one-stop-shop” when it comes to summarizing and analyzing data.

To be clear, SPC is not just about using control charts on the shop floor. It’s about the information gained from that data. And those insights shouldn’t just be relegated to the place on the shop floor where control charts are used. The data should (and can) be repurposed to provide vital information to management regarding where to cut costs, improve quality, and enhance product reliability.

If I’m a plant manager for that same company, and all the data resides in one place, I can easily roll up the collected data across all my shifts and production lines, allowing me to view the big quality picture for my plant. I can see those red cells when I aggregate the data. I can pinpoint the problem(s), and I can prioritize my plant’s improvement activities accordingly.

Now imagine that the same company has 14 plants and that the operations director wants to determine where to cut quality costs. Using a SaaS quality system, the operations director can aggregate data across all plants. As a result, opportunities for quality improvement can be identified and overall performance can be enhanced by engaging Six Sigma resources and letting them do what they do best—make bottom line improvements for their employers. Hey, the data has already been gathered on the shop floor, so why not use it to view overall quality performance?

So, you see, SPC is not just about control charts. It’s not just about focusing on the problem with product B on line 2 (although, that’s clearly important). It’s also about expanding your view. It’s about using the information in your quality data to help you make large-scale improvements. It’s about rolling up all the data across your enterprise and fixing the systemic problems that you never knew you had. And you do have them. You just can’t see them if you don’t have a holistic view of quality.

The Right Solution is a Game Changer

Having the right SPC solution in place is just a first step. You should be able to apply your SPC analysis holistically across your plant and your enterprise to truly enjoy the great benefits that SPC can provide.

In our next blog, we’ll take a closer look at the data we collect. Data is easy to collect. So easy, in fact, that we can be overwhelmed by all we take in. A unified data repository and the right SPC tools can help you get your arms around all that data.