Statistical process control is a methodology aimed at improving the manufacturing process by using statistical means to measure and control quality. The goal is to reduce waste and ensure the highest level of quality in the production of discrete parts. This pertains to any process that a manufacturer follows to transform raw material into a finished product, such as the use of mills, lathes, grinding machines or stamping.How SPC HelpsWhen using SPC throughout a manufacturing process, part measurements are acquired and then plotted on a chart to visualize if inconsistencies are present. For instance, are you trending out of tolerance, either up or down? Is a part becoming less round or rougher over time? Is the position of the part changing?In addition to identifying inconsistencies, statistics themselves become part of an equation that takes into account all measurement data and then outputs a scorecard. This allows you to set a threshold regarding the level of quality and Cpk that you need to have as a manufacturer, whether determined internally or by the requirement of a customer. The scorecard tells you how close you are to meeting that threshold. In this way, SPC metrics enable manufacturers to decide if action is needed for a problematic machine or process. For example, say you have a Cpk threshold of 1 and you are running a process with a current Cpk of 0.9. You can leverage SPC data to identify where the problem lies, whether investigating the Cpk of each machine or looking into the data of specific operators, shifts or other variables to recognize and solve the root cause.This allows you to fix issues in real time. If you are making 1,000 parts a day and part dimensions become larger or smaller over the course of that day because a cutting tool is wearing out, SPC graphical data will show that. Knowing this information as it happens permits you to immediately change the cutting tool so parts quickly return to being produced at the correct size. This reduces waste and saves money, given you are not waiting until the end of the day to make the discovery.Minimizing VariationIn any machining process, measurement variation will occur. Natural or expected variation in part production, like that caused by machine vibration, is referred to as common cause variation. Unexpected variation is called special cause variation, in which it can be traced to an assignable cause.The MeasurLink pre-control chart is an easy way for operators to visualize inspection data at the time of manufacture. Here, they can see that a pattern is occurring, which may cause problems later. With SPC tools like this, companies can react quickly to keep part quality high. Image courtesy of Mitutoyo America

The magnitude to which manufacturers experience common cause variation is based on the quality of the machine, the production environment and the machine operator skill level. What causes this type of variation is not always easy to identify, but the resulting deviations, if centered on the target dimension, should remain within tolerance. Conversely, special cause variations can be tied to specific causes, such as operator error, machine malfunction, faulty part material or insufficient cutting tool fluids.

Use of SPC likely will reduce most variations, save for occasional special cause cases. This means that production should more consistently remain within tolerance and control measures.

Manual Processes

SPC can be used in both automatic and manual manufacturing processes. This is helpful for smaller shops that rely on manual machining and measuring devices. For example, at a shop with different levels of automation and therefore different variations, you could have someone turn the dials on a manual mill to remove material or you could use a CNC machine where someone loads a part, but the automated machine accomplishes the milling. In either case, you might have an operator manually inspecting while data collection is automated through digital tools. Here, SPC still can help improve quality control and measurement by using statistical means to consolidate and evaluate data.

Applicable Industries

Although SPC has roots in the automotive industry, the method is used in varying degrees in a number of highly automated industries, including oil and gas, medical and aerospace. SPC is most common in high-volume, low-mix manufacturing in which only a few part numbers are being produced and machines can be automated to complete repetitive tasks.

However, in high-mix, low-volume situations, automating operations becomes more complex. Operators are vital in these conditions, but that increases the chance of human error. In this case, SPC serves an important function in identifying when human error comes into play and whether more operator training or improvements to the manufacturing process can make a difference by lessening the opportunity for mistakes.

Looking Ahead

For people torn regarding whether to move forward with SPC, ask yourself these questions: What is the cost of bad quality at my shop? What is trying to reduce rejection rates and scrap worth to me? What return on investment can I get from SPC?

In the end, it is about identifying root causes and finding ways to limit those issues so you can save money in the long run and not lose valuable time that could be spent on other business needs. If SPC looks to be one of the ways to accomplish those goals, it is worth considering.

While the adoption of automation in manufacturing continues accelerating, operators and other staff members will remain involved in the production process. SPC will play an important role because it helps identify patterns in both manual and automated practices that ultimately affect the quality of measurement and the final product.