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Douglas C. Fair


Optimize SPC Data Collection

Doing the right thing at the right time

Published: Tuesday, October 17, 2017 - 11:01

Want to improve something? You’ve got to measure it first. If you’re motivated to improve product quality and reduce manufacturing costs, the first step in establishing a successful statistical process control (SPC) solution is getting some data. And if you want to make good decisions from those data, you better make sure you measure the right things.

But you also must trust in the accuracy of the information—and you’ll need to prepare a convenient means of analyzing it. Otherwise, why go to the time and expense of collecting it?

With some forethought on your part, SPC data collection can set the stage for significant process and product improvements. Your SPC system can become the foundation for greatly reducing risk, waste, and defects. But if you want it to work right, you’ll need to take a hard look at the very first step.

How do you collect data?

Real-world data collection

When considering SPC software solutions, look for one that supports the way your manufacturing processes work. You should be able to collect data from the process points that matter most to your business and customers, without jumping through hoops or purchasing additional software modules or devices.

I continue to see manufacturers who use paper checklists as the foundation of their quality systems. Ask yourself: Does my company collect data this way? My experience has been that even large, leading-edge companies use paper forms to deploy quality. And that’s problematic.

The problem with paper

I’m surprised to see clipboards and paper being used for shop-floor SPC data collection today. Surprisingly, it is more the rule than the exception. This is true even though modern manufacturing plants and modern-day assembly lines almost always incorporate some level of data automation.

So why is paper still used? The common refrain I hear is that paper is “less expensive” than software. Well, it isn’t. Quality professionals also tell me that since paper has been used for so long, it has become a habit—albeit a bad one.

Unfortunately, paper-based data collection has the propensity to introduce errors into an SPC system:
• Data might be misread from the paper.
• Numbers could be accidentally transposed.
• Data written on paper may be illegible or misinterpreted.
• The paper might be damaged or lost altogether.
• While transferring data from paper to an electronic system, numbers might be misread or entered incorrectly.

Paper habits are not only ripe for error, but they also are expensive. I once worked with a manufacturer whose quality system generated so much paper that the company employed three librarians—one for each shift—just to manage it all. And what did they do with all that paper once the on-site library filled up? They’d pack it up and ship it to a warehouse. There it was organized with the millions of other pieces of paper that preceded the newest arrivals.

Librarians, paper, writing utensils, warehouses, and transportation. You can see how paper systems can, indeed, be expensive.

Worse, how do you generate summarized reports from paper-based systems? Well, first you’ve got to find the right paper with the right data on it. Then cross your fingers and hope that you can read the data. If so, then you must transfer the data to some other medium, such as a spreadsheet. It’s a time-consuming, laborious process fraught with error.

So, if you don’t go through the drudgery of transferring paper-based data to another system for analysis, then the data are trapped, forever imprisoned on paper—and you’re unable to use them to benefit your manufacturing operations.

The problem with spreadsheets

If you think that moving your paper-based system to spreadsheets is a good idea, think again. Not only are spreadsheets unwieldy and challenging for operators and inspectors to work with, they are also very difficult to manage and organize. Plus, when the time comes for monthly reporting, get ready for a headache.

Consider a manager who requests a simple summary quality report for the month. What complicates the request is that most companies create a new spreadsheet for each part number they run. Data from each part are saved in unique spreadsheets, and potentially hundreds of different part numbers might be manufactured in a month.

So, how can data from hundreds of different spreadsheets be combined to summarize a plant’s quality levels for a specific monthly report? How would you even know which part numbers were manufactured and which spreadsheets to access? It’s not just tough; it’s nearly impossible. And yet the information contained within and across those spreadsheets is exactly what managers need to make intelligent decisions.

Whether companies are using spreadsheets or paper-based quality systems to gather data, the critical information they need to better manage their plants is inaccessible and impossible to leverage for improvement.

Modern SPC data collection features

Operators need to collect data, but sometimes they can’t find the right paper form or spreadsheet. It shouldn’t be that hard. Modern data collection should support data entry on tablet devices, PCs, and even smartphones. Wireless connectivity should be all that’s required—and there should be little need to involve your IT department.

Make certain that the software you use to collect data has the flexibility to mimic data collection on your shop floor. That is, software should:
• Be configurable enough to support data collection the way operators expect to do it
• Make data capture much faster than when an operator writes on paper
• Allow operators to easily enter (without typing) the traceability fields, quality data, and other information found on paper forms
• Automatically note the time, date, shift, and operator name

Look for variety in SPC data collection technologies. You’ll want to consider electronic data-collection features for hand-held gauges, programmable logic controllers (PLCs), preexisting databases, and manufacturing execution planning (MEP) and enterprise resource planning (ERP) systems. You should be able to capture those types of data automatically, without engaging an operator.

Additionally, bar-code scanners are a fast, convenient, and inexpensive means for entering defects data or associated information (such as purchase order numbers, lot codes, and other descriptive fields) to data that are being captured by operators and inspectors.

My experience is that operators enjoy working with software that makes data collection fast and easy. If it reduces their burden and eliminates the hassle associated with juggling paper and spreadsheets, they will thank you. And if you win the support of your operators and inspectors, they’ll quickly embrace your new SPC system.

Quality management software: A cost-saving advantage

Investing in more paper and spreadsheets is throwing good money after bad. It’s no way to manage a quality system.

What you need is software that makes data collection easy and fast for operators and inspectors. Once successfully collected, the data can be analyzed to reveal critical information that can slash costs and generate big gains in quality, productivity, and efficiency.

But that discussion is for another post in this series. Until then, start by looking for SPC software that has a simple, friendly, and intuitive interface; automation; and expansive data collection flexibility to help mimic your real-life manufacturing situations.

This article was originally published by InfinityQS.


About The Author

Douglas C. Fair’s picture

Douglas C. Fair

A quality professional with 30 years’ experience in manufacturing, analytics, and statistical applications, Douglas C. Fair serves as chief operating officer for InfinityQS. Fair’s career began at Boeing Aerospace, and he worked as a quality systems consultant before joining InfinityQS in 1997. Fair earned 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).


What to measure and using the outcome

The choice of what to measure is critical: We need to challenge if the learnings from the measure will lead to a basis for action... Will a point outside the process limits (not spec limits) catalyse action? If not, worth pursuing?

I think we need a system in place where routinely collected data are used to discover what's going on, and the (working) system provides time allowance to perform investigations and also have the time to profit from the learnings by implementing the actions plans coming from the learnings. Here we need a means of focusing on the "big signals" that should give more return for the time and effort and energy invested.

Agree that the consolidation of the sheer volume of data we can collect these days becomes more and more important. Not easy but the consolidation needs to be simple and effective. Traditional approaches struggle here. When a good consolidation is achieved, higher-level priorities can be defined, again providing a basis for (value-added) action.

All the above can bring continual improvement: Who'd argue against that?