Category Archives: Operations

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Beware the Hawthorne

“We have great news for you. Our project is delivering results already.” The team is all smiles when they give this update. The carefully prepared graphs unveil a remarkably shorter time for the whole process, from customer request to delivery of results. However, we had not changed a thing yet. How come?

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Great, We Have Improved … or Not?

Many companies spend considerable amounts of money on customer surveys every year. Customer survey results are being used to amend strategies, design new products and services, focus improvement activities and … to celebrate success. Since the impact of customer service results can be quite hefty the data driving important decisions shall be trust-worthy. And, the […]

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Making Sense of the Two-Proportions Test

Consider a production process that produced 10,000 widgets in January and experienced a total of 112 rejected widgets after a quality control inspection. A project was deployed to fix this problem. In April, the process produced 8,000 widgets and experienced a total of 63 rejects. Did the process really improve? Read..

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Working Capital Meets Lean Six Sigma

Combining Lean Six Sigma (LSS) with a thorough analysis of your Working Capital Situation can lead to a more objective selection of high impact projects. LSS may help to achieve reduction of Working Capital hidden in warehouses and processes through data-based root cause analysis. Working Capital meets Lean Six Sigma.

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Beyond HR Data Analytics – Competencies HR Professionals Should Possess

HR professionals with knowledge of Lean Six Sigma tools and HR Data Analytics can deliver better service to their customers – their leaders and colleagues. Identifying requirements and gaps, analysing, improving and innovating processes as well as ensuring the performance long-term are valuable business partners.

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Making Sense of Linear Regression

Linear regression is one of the most commonly used hypothesis tests in Lean Six Sigma work. Linear regression offers the statistics for testing whether two or more sets of continuous data correlate with each other, i.e. whether one drives another one. Additionally, it shows the influence of discrete variables, too.

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Making Sense of ANOVA – Find Differences in Population Means

ANOVA (analysis of variances) is a statistical technique for determining the existence of differences among several population means. The technique requires the analysis of different forms of variances – hence the name. It is testing whether means are different. Read about an application example here.

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Making Sense of the Binary Logistic Regression Tool

In some situations, Six Sigma practitioners find a Y that is discrete and Xs that are continuous. How can a regression equation be developed in these cases? Black Belt training indicated that the correct technique is something called logistic regression or binary regression. But this tool is often not well understood.

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Making Sense of the Two-Sample T-Test

More often than not, Lean Six Sigma practitioners have to find out whether two groups that look quite the same are really the same. If averages of two groups are in question and the data is continuous and normally distributed, the two-sample t-test offers an answer to the question. Read how to run this test in SigmaXL.

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Making Sense of Attribute Gage R&R Calculations

Measurement error is unavoidable. There will always be some measurement variation due to the measurement system itself. Most problematic measurement system issues occour when measuring attribute data by relying on human judgment such as good/bad, pass/fail. Attribute Gage R&R helps to find and reduce these errors.

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