Statistical Process Control (SPC) is a commonly used technique for identifying faults in your production line, and ensuring that the final product is within acceptable quality boundaries. As the name suggests, it relies heavily on statistical methodologies to give you an adequate overview of the current state of your production facilities, and when applied correctly, it can be a very powerful tool for maximizing your output and reducing various kinds of waste. Unsurprisingly, itâ€™s commonly used in lean organizations.
It all starts with gathering all the data that youâ€™ll need in your statistical analysis, and nowadays you have plenty of options for that thanks to modern technology. Itâ€™s quite easy to fit your whole production facility with tiny sensors that capture all sorts of important data, and then funnel that into a node that either collects and aggregates the data, or processes it immediately.
Keep in mind that you can go quite far with data collection, and you must always be careful to not overextend your investment in this part of the business. This will not only result in wasted money, but it will also overburden your actual analysis process and make it much more complicated than it needs to be. And that alone can be a huge detriment to the quality of the analysis, therefore itâ€™s crucial to minimize the data collection process as much as your current situation allows you to.
Setting Appropriate Control Limits
Control limits are one of the most important concepts in SPC, and itâ€™s critical that they are set at appropriate levels to minimize incorrect results. This will take a certain amount of experience with your own specific field and the type of product your company makes, and you may also need intricate knowledge of the machines used in the whole process. Sometimes the manufacturers of different production machines may provide you with readily available data for those limits, but more often youâ€™ll have to determine them yourself for your specific use case.
The point of these limits is that no production process is perfect, and there will always be some variation in the output. In many cases though, these variations can be acceptable as they donâ€™t degrade the quality of the final product. Once youâ€™ve set the right limits, youâ€™ll be able to see the important outliers in your production data more easily.
And sometimes, youâ€™ll have to redefine those limits along the way â€“ not just when youâ€™ve changed something about the production process, but also when the market itself goes through some changes and forces you to adapt. In some cases this might even mean relaxing the quality control requirements slightly in order to momentarily improve the output capacity of the facility, but care should be taken with this approach to avoid overdoing it.
Reevaluating Your SPC Implementation
Sooner or later you will need to make some changes to the way youâ€™re running your SPC, typically as the company grows and its requirements shift to a new direction. Itâ€™s important to regularly reevaluate the way youâ€™re collecting and processing your data, and you should do your best to get your colleaguesâ€™ input on this as well. People on other levels of the organization may be able to see certain details that are not as obvious to you, and getting as much feedback as possible on your SPC can be extremely valuable.
Of course, you should also be careful to not overdo this, and if your current analysis produces good results in terms of product quality, then you should focus your efforts on another area of the organization. But never lose focus of the current state of your SPC.
SPC can be a very powerful technique when applied correctly, but itâ€™s not a â€œfire and forgetâ€ solution. In fact, itâ€™s quite the opposite and can be somewhat demanding in terms of maintenance and attention, but the final results are more than worth it. The impact of a proper SPC implementation on your organization can be incredible, and itâ€™s one of the first steps you should take if youâ€™re having problems with the consistency of your output, or its overall quality.