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Is Statistical Process Control Still Relevant?

Introducing our series on SPC in a digital era

Published: Wednesday, October 25, 2023 - 11:03

Today’s manufacturing systems have become more automated, data-driven, and sophisticated than ever before. Visit any modern shop floor and you’ll find a plethora of IT systems, HMIs, PLC data streams, machine controllers, engineering support, and other digital initiatives, all vying to improve manufacturing quality and efficiencies.

That begs these questions: With all this technology, is statistical process control (SPC) still relevant? Is SPC even needed anymore? Some believe manufacturing sophistication trumps SPC technologies that were invented 100 years ago. But is that true? 

We the authors believe that SPC is indeed relevant today and can be a vitally important aid to manufacturing. (SPC can be used outside of manufacturing, and to great benefit, but we keep our focus on manufacturing.)

As quality professionals and statisticians, are we biased in our view? Possibly. After visiting hundreds of manufacturing plants around the globe, though, and witnessing their unending manufacturing challenges and opportunities, the evidence is overwhelming: SPC is an important strategic tool in the quest for improved quality and reduced costs. We also postulate that SPC has more potential uses and benefits today than ever before.

We intend not only to peruse this theory, but also to discuss SPC in the context of today’s sophisticated shop-floor technologies.

What follows this introductory article is a series of articles written specifically to introduce readers to what we’ve learned from working with modern companies. We wish to share the extraordinary technologies applied on those shop floors, as well as how SPC can be seamlessly applied—and even automated—in those challenging environments.

Some believe manufacturing sophistication trumps SPC technologies that were invented 100 years ago. But is that true? The answer is no. In fact, today’s technology makes SPC a brilliant strategic tool for improving competitive position because it can now be deployed more easily, faster, and more effectively. 

Following are some of the topics to be covered in our articles, where we’ll mix SPC’s uses that are enabled by today’s technologies with more traditional uses that are as relevant as ever.

1. Using SPC in today’s digital world: Today’s manufacturing companies are replete with computers, information systems, networks, and cloud technologies. As a result, a wide variety of options confront organizations for how to deploy SPC. These options provide deployment flexibility, allowing teams to deploy SPC precisely how they choose, fitting virtually any shop floor. From plant-based deployments to multiregional and cross-divisional deployments, anything is possible. Compared to the paper-and-pencil days of just a few years ago, ample opportunities exist for generating massive returns on SPC dollars—and it’s never been easier to do.

2. How to manage high-volume data streams with SPC: Today’s shop floors are littered with IT systems like MES, ERP, PLC data streams, and related data-based systems. We’re drowning in data these days. How should we manage it all? Are your IT investments providing you with the information you need to better manage your manufacturing processes? Or are your data untapped, dying a quiet death in some anonymous database? Our experience shows that companies rarely get the value they need from these systems. Terabytes of data, filled with information that can drive improvements and cost savings, are often ignored because there’s not enough time or resources for review. It’s a massive waste. We’re well aware of organizations having made huge savings from data they literally had never even looked at. If you’re not leveraging those data (and it’s almost certain that you’re not), you aren’t maximizing returns on your data collection investments. SPC can help greatly. We’ll show you how.

3. How SPC can communicate quality information across the enterprise: Frustrated with data silos—what we refer to as data islands—and the inability to access those data? That’s exactly what companies tell us. But it doesn’t have to be that way. We’ll share powerful strategies for centralizing data and sharing it across your organization so that quality levels go up and costs go down. 

4. How to create huge cost reductions by repurposing SPC data: Most organizations think that a control chart is the only tool in the SPC arsenal. They’re wrong. Yes, data are typically captured on the shop floor, and yes, control charts can be created from those data. But did you know that the same data can be repurposed for generating powerful, substantive reports for engineers, managers, and stakeholders? These summarized, aggregated reports typically generate enormous financial benefit. You can’t afford to ignore the incredible information these reports can provide. 

5. How to measure data bias and inaccuracies: Most companies don’t question their data, but they would be wise to do so. Big decisions are made based on what the data say. But how certain are you that your data are actually telling you the true story? We cover strategies for ensuring data accuracy so your decisions have the highest probabilities for success. 

6. How to instantly communicate shop floor issues across the enterprise: SPC systems can also be used for important communications. Has something significant changed in the manufacturing process? Has a failure of some type occurred? Or has there been trouble at all? This information (and more) can be automatically communicated to anyone in the world, even if you’re traveling, and even if you only have a cell phone. Use modern SPC systems to keep your finger on the pulse of shop floor quality, no matter where you are. 

7. How to set up your processes more optimally to get the most out of them: Use SPC techniques to reduce the variation in key quality characteristics so you can adapt product recipes—the quantities of raw materials used in production—to drive material costs to a minimum. This will be the first discussion in the series. 

8. Just how good can your current process be? We’ll discuss the unique insights obtainable from your process and product data if you know how to let the SPC voice of the process inform you.

Incredible improvements in information technology, coupled with big reductions in IT costs, give manufacturing firms the opportunity to inexpensively modernize their statistical process control systems.

Not only is SPC a brilliant strategic tool for improving competitive position, but it also can be deployed easier, faster, and more effectively. More powerful than ever, SPC is indeed relevant in the 21st century. Join us in the coming weeks for our series of articles about how your organization can benefit greatly by modernizing a 100-year old technology: statistical process control.

Who are we hoping to reach with these articles? Those working in or around manufacturing: managers, engineers, and shop-floor technicians who are seeking new or better ways to continually improve quality and drive down costs.


About The Authors

Douglas C. Fair’s picture

Douglas C. Fair

A quality professional with more than 35 years of experience in manufacturing, analytics, and statistical applications, Douglas C. Fair is the former chief operating officer at InfinityQS International, an SPC software company. At InfinityQS, he spent 25 years helping manufacturers around the world deploy SPC and benefit from its use. 

Fair holds a bachelor’s degree in industrial statistics from the University of Tennessee, and a Six Sigma Black Belt from the University of Wisconsin. He’s a regular contributor to various quality magazines and has co-authored two books on industrial statistics: Innovative Control Charting (ASQ Quality Press, 1998) and Quality Management in Health Care (Jones and Bartlett Publishing, 2004).

Scott A. Hindle’s picture

Scott A. Hindle

Scott A. Hindle has been using data to study and improve processes, and actively working in the field of SPC, for close to 15 years.


Of course it is

Even if it weren't (in manufacturing), it would still be in service, where volumes are lower and slower for the most part, and automation isn't as widely used. But of course it is still relevant, for the reasons you enumerated in this article. 

I had a guy try to tell me a couple of years ago that his automated systems measure every part, and adjust the machine accordingly. I told him that that would be OK if the measurements were going into an algorithm like a control chart, so the automated adjustments wouldn't just constitute high-speed tampering and driving variation up quickly. 


Thank-you...thank-you....thank-you for a great article addressing Over-Datafication ( a term I coined several years ago).  I worked in the Pulp and Paper Industry for 47 years.  It always amazed me that vendors of data collection systems often used ferquency of data as a selling point.  "Wouldn't it be fantastic if we could get a measurement every 10 seconds instead of every 10 minutes?".  Of course, this comes five years after they sold us the current system with "Wouldn't it be fantastic if we could get a measurement every 10 minutes instead of once per hour?"  etc., ect., etc., ad nauseum.  Over-Datafication occurs when the data is collected much faster than the process can change and the data becimes severely auto-correlated, making it useless for analysis of the "voice of the process".  I once mentored a Six-Sigma Black Belt candidate doing a project in a pulp mill.  He was taking automated data (temperature) of a process stream which needed to be controlled within a few degrees.  The data collection frequency was every 30 seconds.  However, the Control Chart based on this data turned out to be useless in helping the operators control the temperature of this process stream.  It turned out that the data was very auto-correlated - the data were nowhere near independent of each other.  We changed the data frequency to every half hour and the Control Chart became a great tool for the operators.  Temperature variability was reduced over 50% and temperature excursions outside the specification limits became almost non-existant.  Due to Over-Datafication, many industries are drowning in data, but starved of information.  Data is useless if it cannot be turned intoo information. Note:  Auto-correlated data makes Control Charting very difficult because the calculated upper and lower control limits become way too tight and most of the data is outside thos limits.


Thanks, Steve, for the valuable comments and the term (not heard it before). Hopefully our upcoming Part three will be a good one to put a bit more meat on the bone to your comments on how to use data in a way that makes sense, bring useful knowledge and information to the operation. Keep a look out for this one and let us know your thoughts/ideas to it.

Let's Reconnect

Scott, Let's reconnect:  gandydancerz@hotmail.com