I have daily conversations with manufacturer plant managers, quality managers, engineers, supervisors, and plant production workers about challenges when using statistical process control (SPC). Of the mistakes I witness in the application of SPC, I’d like to share the five most prevalent; they can be costly.
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 usually communicated as tolerances or specifications.
Customers frequently specify a process capability index (Cpk) or process performance index (Ppk) value that you must meet. Because they put such importance on this value, capability statistics may become your primary concern in quality improvement efforts. They may be important, but 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 setting the stage for a dreaded recall.
No. 2: Misuse of control limits
Producing control charts doesn’t 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 the following 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, aka 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: Measurement system issues
If you are applying SPC, you’re measuring things. Do you know how well you are measuring? This is a critical factor that is easily overlooked when you are focused on SPC. Even the best application of SPC tools can be undermined when the ability to measure things is uncertain.
In addition to having measurement systems analysis 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’s 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. 4: Delegating SPC work to one employee or a small group of employees
In many organizations, SPC is not yet internalized and normalized as a part of doing business. This becomes a problem when the person tasked with SPC leaves. The system they put in place may get less attention, and charts on key quality metrics may not get refreshed.
No. 5: Not leveraging technology to scale your SPC efforts
Technology has made it easier to create and deploy SPC charts on anything and everything. While advantages to this abound, the amount of time spent by valuable employees doing nonvalue-adding, repetitive, 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 whether action is needed.
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 more important activities.
Often, when I see the five mistakes listed in this article, 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 these mistakes is an underlying need for more education. Through continuing education, this SPC way of thinking can become embedded in the manufacturing culture.