Tips and Techniques for Statistics and Quality Improvement

Blog posts and articles about using Minitab software in quality improvement projects, research, and more.

Bruno Scibilia

I practiced quality improvement statistical techniques in manufacturing for many years, and I look forward to sharing some of what I've learned about quality improvement with you!
Bruno Scibilia

Definitive Screening Designs (DSDs) are a new class of Designs of Experiments (DoE) that have generated a lot of interest for product and process optimization. They are available in Minitab Statistical Software.

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The potential benefit of the data stored on servers is huge. Banks, insurance companies, telecom companies, manufacturers — in fact organizations from across all industries need to make good use of the data they own to improve their operations, better understand their customers and find competitive advantage.

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Suppose that you plan to source a substantial amount of parts or subcomponents from a new supplier. To ensure that their quality level is acceptable to you, you might want to assess the capability levels (Ppk and Cpk indices) of their manufacturing processes and check whether their critical process parameters are fully under control (using control charts). If you are not sure about the efficiency of the supplier...

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Choosing the right type of subgroup in a control chart is crucial. In a rational subgroup, the variability within a subgroup should encompass common causes, random, short-term variability and represent “normal,” “typical,” natural process variations, whereas differences between subgroups are useful to detect drifts in variability over time (due to “special” or “assignable” causes). Variation within subgroup is...

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Genichi Taguchi is famous for his pioneering methods of robust quality engineering. One of the major contributions that he made to quality improvement methods is Taguchi designs.

Designed experiments were first used by agronomists during the last century. This method seemed highly theoretical at first, and was initially restricted to agronomy. Taguchi made the designed experiment approach more accessible to...

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Have you ever wished your control charts were better?  More effective and user-friendly?  Easier to understand and act on?  In this post, I'll share some simple ways to make SPC monitoring more effective in Minitab.

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There may be huge potential benefits waiting in the data in your servers. These data may be used for many different purposes. Better data allows better decisions, of course. Banks, insurance firms, and telecom companies already own a large amount of data about their customers. These resources are useful for building a more personal relationship with each customer.

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Businesses are getting more and more data from existing and potential customers: whenever we click on a web site, for example, it can be recorded in the vendor's database. And whenever we use electronic ID cards to access public transportation or other services, our movements across the city may be analyzed.

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This is an era of massive data. A huge amount of data is being generated from the web and from customer relations records, not to mention also from sensors used in the manufacturing industry (semiconductor, pharmaceutical, petrochemical companies and many other industries).

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In my last post, I discussed how a DOE was chosen to optimize a chemical-mechanical polishing process in the microelectronics industry. This important process improved the plant's final manufacturing yields. We selected an experimental design that let us study the effects of six process parameters in 16 runs.

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