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?

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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.

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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.

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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.

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Finally, you could even start to compare quality parameters between different lines.

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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.