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 that data, you better make sure you measure the right things. But you also must trust in the accuracy of the data—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
Real-World Data Collection for Real-Time SPC Data Analysis
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 system. 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: Ineffective SPC Data Collection
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 lost altogether or damaged.
- 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 are expensive.
I once worked with a manufacturer whose quality system generated so much paper that they employed 3 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 spreadsheets. 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 is trapped, forever imprisoned on paper—and you’re unable to use it to benefit your manufacturing operations.
The Problem with Spreadsheets: Ineffective SPC Data Analysis
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 is 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 they are using spreadsheets or paper-based quality systems to gather data, the critical information companies need to better manage their plants is inaccessible and impossible to leverage for improvement.
Learn how SanDisk enabled data collection from disparate system to give real-time information to operators, quality engineers, process engineers, and management. Read the case study.
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.
And 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; and
- 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), pre-existing databases, and manufacturing execution and enterprise resource planning 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 associating information (such as purchase order numbers, lot codes, and other descriptive fields) to data that is 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 blog 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.
Read the other blogs in this series:
Optimize SPC Data Collection: Mastering Quality #1
Stay on Track with SPC Process Alerts and Notifications: Mastering Quality #2
What’s Your Priority? Use SPC to Maximize Your Impact on Quality and Costs: Mastering Quality #3
Analyzing Quality Data with the Right SPC Tools: Mastering Quality #4
Reporting & Communicating SPC Data for Change: Mastering Quality #5
Would you like to see how these techniques can be applied to your organization? Schedule a live demo
, customized to your organization and requirements.