Five Costly Mistakes to Avoid When Applying SPC

The good news: They’re preventable

Published: Thursday, December 10, 2015 - 15:59

In daily conversations, I field questions from plant managers, quality managers, engineers, supervisors, and plant production workers about the challenges of applying statistical process control (SPC) methods. Following are the five most prevalent and costly mistakes I witness in the application of SPC.

No. 1: Capability before stability

Capability is a critical metric, and capability statistics are often an important part of your supply chain conversation. Your customers want assurance that your processes are capable of meeting their requirements. These requirements are often communicated as tolerances or specifications.

Customers often specify a Cpk or Ppk value that you must meet, and because they put such importance on this value, these capability statistics may become your primary concern in quality improvement efforts. Although they may be important, sole reliance on Cpk values is premature. The first issue to be addressed is getting to a stable, predictable process. Building control charts into your analytical process on the front end can prevent costly mistakes such as producing scrap, shipping unacceptable product, or even being forced to issue recalls.

No. 2: Misuse of control limits

Producing control charts does not guarantee accurate process feedback. There are many subtleties with the application of control limits that are easy to get wrong. Here are a few common errors:
• Computing wrong limit values with a home-grown tool. Time and time again I have seen examples where the numbers are just wrong, often resulting in audit failures. If you use a home-grown tool for SPC, proceed with caution.
• Never computing static control limits. The decision to compute control limits should be a deliberate one, even if your SPC software automatically computes limits for you.
• Never re-computing control limits. If you reduce variation over the course of a year, then the control limits you computed in January will not reflect how the process is running in December. A deliberate re-computing of the control limits to establish the “new normal” is in order.
• Waiting to have enough data to compute control limits. Whether you have a small amount of data or a great deal of data, computing “baseline” control limits will almost always provide benefits. There are many guidelines, such as waiting until you have at least 25 subgroups gathered over a normal course of production. If you don’t have much data yet, reasonable control limits can be computed even with small amounts of data.
• Confusing specification limits with control limits. Specifications, also known as tolerances, tell you what your customer requires. Control limits reflect how your process behaves. I often see line charts with horizontal specification lines at the upper and lower specification values. This type of chart might provide value in some situations, but it should never be confused with a control chart.

No. 3: Not assessing the measurement system

If you are applying SPC, you are measuring things. Do you know how well you are measuring? Measurement systems analysis (MSA) is a critical area that is easily overlooked when you are focused on SPC, but even the best application of SPC tools can be undermined when the ability to measure things is uncertain.

No. 4: Failure to manage the measurement system

In addition to the MSA tools, you need to properly manage your measuring devices. How well do you manage your measurement equipment? What is the calibration interval? What steps are checked during a calibration? What is the history of calibration for a given device? What master gauges are used for the calibration, and have those devices been calibrated?

Software applications designed for this purpose, such as PQ Systems’ GAGEpack, can help to assess and manage measurement systems.

No. 5: Wasting time creating and viewing too many charts for too few signals

Technology has made it easier to create and deploy SPC charts on anything and everything. Although this can have many advantages, the amount of time spent by valuable workers doing repetitive, nonvalue-added, SPC-related work can be costly. If you need to monitor dozens or even hundreds of SPC charts, you need to seek methods of scaling your SPC application. Consider the time it might take to do these steps:
1. Find the chart of interest
2. Display the chart
3. Analyze the chart
4. Decide if action is needed or not

Why invest an employee’s time and attention to look at hundreds of charts—most of which are stable or in control? Utilizing an automated approach can amplify your ability to pay attention to key metrics without dragging quality workers away from their more important activities.

Dr. Donald Wheeler describes SPC as “a way of thinking with some tools attached.” Often, when I see these mistakes, the root cause is too much focus on the tools of SPC and not enough focus on the SPC way of thinking. The common thread among the five mistakes is an underlying need for more education.

Through continuing education these SPC errors can be avoided. For more information about how to prevent them, please register for the PQ Systems–Quality Digest webinar, “Five Common Mistakes Applying SPC and How to Avoid Them,” occurring on Dec. 17, 2015, at 2 p.m. Eastern and 11 a.m. Pacific.

About The Author

Steve Daum

M. Stephen Daum is director of development for PQ Systems. Prior to assuming responsibility for development, Daum was the lead programmer on PQ’s statistical software products, a position he took in 1985. Daum has more than 20 years of experience with control charts and control charting software and has shared that experience through presentations, training, and educational sessions for organizations throughout the United States, England, and South Africa.