Any manufacturer would say they care about quality (if you disagree with this statement, please let me know so that I can stop buying your product). But let’s dissect that a little. What does your quality assurance scheme achieve? What does it tell you? And what would you like it to tell you?
Even the simplest QA schemes will cover ingredients checks and process automation for most steady-state processes. You’ll be adjusting the parameters of some processes based on measurements. Then, between each stage in the process, you’ll be doing simple pass/fail checks.
These checks are vital and tell you whether the process is going wrong—and, if so, allow you to dump that production run before it disappoints your hard-won customers.
However—and this is crucial—pass/fail checks don’t tell you how right things are going!
Pass/fail data don’t show you how close to the limit you are. They don’t show trends. They don’t show you how likely you are to be able to manufacture a consistent quality product again in the future. You gain these benefits only by recording the actual values over multiple runs, over time—and storing those data in a centralized data repository. Once you start doing this, you can gain some key insights.
Overcome the limits of pass/fail checks with SPC data
When your quality data are centralized and standardized in one database, you can leverage SPC-based quality management software to dig in and work with the information.
First up, you can view a histogram distribution of data at each stage (see chart below). This tells you how frequent each value is, where the outliers are and where the majority of the values lie.
Then, you can start to look at the capability for each check at each step in the process. This allows you to answer what should be a key question for any manufacturer: “How capable is my production process of producing the right result?”
You can do this separately for each stage. You can even assign a numeric score to capability. In essence, it’s a score of how easily your histogram fits within your specification limits.
Moving on, you can look at statistically significant trends within your data. Some Pass/Fail checks just won’t show you what you need to know. If you have a number of values near the lower or upper spec limit, it might be time to personally check the ingredients/parts you’re buying.
Finally, you could even start to compare quality parameters between different lines.
Managing the amount of data required for this type of analysis on paper or spreadsheets is nearly impossible.
InfinityQS has been a leader in providing quality management solutions to all types of manufacturing industries over the past three decades. Learn how our SPC-based quality management solutions can transform your manufacturing operations.