Manufacturers know that if they want to evolve their businesses and confidently face the ever-growing competition, they need products of high quality. But, as we all know, quality is not a static concept. To get a tight grip on quality, operation managers need to closely monitor manufacturing processes and amend them as needed, so that production remains stable, consistent, and error-free.
Since the introduction of modern statistics in the 1920s, Statistical Process Control (SPC) and Statistical Quality Control (SQC) play a crucial role in optimizing operations. We’re here to shed some light on these statistical methods and highlight the benefits they bring to quality improvement.
How to validate and ensure quality
Nowadays, quality is a prerequisite for selling any product. But how do manufacturers validate quality or manage to improve it? To better understand the differences and similarities between SPC and SQC, let’s first look at the evolution of quality control over time.
In traditional quality control inspectors manually approve or reject products after measuring these and comparing them with predefined target values and tolerances (specifications). Data collected during quality control speaks volumes, not only about products but also about the manufacturing process as a whole. However, it still reflects the level of quality at the moment of inspection.
In contrast to traditional quality control, Statistical Process Control (SPC) takes data to the next level. With SPC, you apply a set of statistical tools to the measured quality data to closely monitor the process and to identify potential trends. It involves a methodical approach to help reduce variation, identify areas of improvement, and predict whether a process is in control or not.
Like SPC, Statistical Quality Control (SQC) is a data-based approach to quality control. In fact, more often than not, these two terms are used interchangeably. They make use of the same analytical tools to control the manufacturing process. So, is there a difference? One might argue that a minor difference could be the use of acceptance sampling.
Statistical quality control and acceptance sampling
Organizations often forego testing every one of their products. Most often, this would involve testing too many products in a limited time frame and at a high cost.
When not each unit of production can be inspected, sampling is the only way to check if a batch of products is within specifications. A sample size needs to be representative of the whole batch.
Acceptance sampling is then the number of tests passed. Manufacturers need to decide how many products they test through sampling and the number of defects acceptable within a batch. This is sometimes regulated by ISO standards.
Just like with SPC, the data collected by sampling can then be used to run statistical analyses and gain insights.
How to future-proof quality
As we already mentioned, quality control has been continuously evolving. And most of us may wonder, what’s next? While the future cannot be predicted with certainty, automation is one of the areas that unquestionably belongs there. The quality intelligence software from AlisQI will help you to take this step towards the future and bring SPC/SQC analyses at your fingertips.
While using statistical methods is very popular in manufacturing, most manufacturers still analyze data in the traditional way. This involves searching through endless Excel sheets and most often, paying painstaking attention to detail. Regardless of the amount of data, the analyses are time-consuming and prone to error. In contrast, automating statistics with AlisQI, manufacturers have got their bases covered. The platform analyzes data and responds to nonconformities using out-of-control alarms. Additionally, it applies statistical methods to analyze trends. With real-time information, it’s much easier to react fast and be on top of things.
Conclusion
Quality is not a static concept. From traditional quality control to using statistics, manufacturers have monitored their processes and have tried to improve them. The process of measuring data and identifying trends plays a crucial role in achieving operational success. Whether you call it SPC or SQC, you use statistical methods to analyze the manufacturing process.
When it comes to validating quality, we shouldn’t get lost in acronyms – most manufacturers have their own approach towards SPC and/or SQC. To future-proof a manufacturing process, one should evaluate the sample size and acceptance rate of their test. Automation can definitely help with that.
Should there still be any unanswered questions, do not hesitate to contact us. In the meantime, visit our case studies section to read about customers who have successfully automated their SPC and the benefits they’ve enjoyed.
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