Tips and Techniques for Statistics and Quality Improvement

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

Adam Russell is a Global Operations Master Black Belt, Tate & Lyle.

Tate & Lyle deploys Continuous Improvement (CI) tools, including Minitab and Salford Predictive Modeler (SPM), to address challenging engineering and manufacturing problems. Our company operates large-scale, continuous flow processes serving the Food & Beverage industry.

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Confidence intervals show the range of values we can be fairly, well, confident, that our true value lies in, and they are very important to any quality practitioner. I could be 95% confident the volume of a can of soup will be 390-410 ml. I could be 99% confident under 2% of the products in my batch are defective.

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You might be thinking, six weeks and you’re still not telling us what happened? When you are solving a manufacturing or development problem you might hear the same thing from your leadership – when will we get the results?

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In our continuing effort to use experimental design to understand how to drive the golf ball the farthest off the tee, we have decided each golfer will perform half the possible combinations of high and low settings for each factor. But how many times should each golfer replicate their runs to produce a complete data set?

 

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Editor’s note: As we prepare for the inaugural Minitab Insights golf tournament in Scottsdale, Arizona on September 12, we are taking a look back at The Minitab Blog archives with some posts on using Minitab to understand how to improve our game. In this first installment, we examine how solving an age-old problem in golf is much like process engineering problems. Stay tuned each week as we cover the next step in our...

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The role of quality professionals is more critical today than it ever has been. Quality is a top driver at any company, regardless of size or industry. It’s a major factor when it comes to production numbers, profits and more. However, maintaining and improving quality is no easy task.

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If you collect and analyze real data for a living, the idea of using simulated data for a Monte Carlo simulation sounds a bit odd. How can you improve a real product with simulated data? 

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As we collect more and more observational data from our processes, we may need new tools to provide meaningful insights into this information. You can add modern-day machine learning techniques alongside traditional statistical tools to analyze, improve and control your processes.

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Dramatic cost savings. Lead time and inventory reductions. Improved transactional processes.

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Editor's note: If you would like to see these tools presented in a webinar, visit our On Demand Webinars and look for "5 Critical Tools for Your Lean Deployment."

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