Every blood donor of a large blood bank has to go through five process steps. These steps are Registration, Screening, HB Test, Donation and Refreshment. At the end of the process, that often takes around an hour, feedback forms are available for the donors. In one week, 210 donors have returned these forms with their satisfaction score for each process step. This satisfaction score is measured using a six-point scale. The desired rating is either 5 (satisfied) or 6 (very satisfied). …
Three teams compete in our CHL business simulation, CHL Red, CHL Green and CHL Blue. After completing Day One, it looks like the teams show a very different performance (Figure 1). Although the means look very similar, the variation is strikingly different. This is surprising, since all teams start with exactly the same prerequisites. To test this assumption of different variability among the teams, the Test for Equal Variances is deployed.…
Consider a production process that produced 10,000 widgets in January and experienced a total of 112 rejected widgets after a quality control inspection (i.e., failure rate = 1.12%). A Six Sigma project was deployed to fix this problem and by March the improvement plan was in place. In April, the process produced 8,000 widgets and experienced a total of 63 rejects (failure rate = 0.79%). Did the process indeed improve?
The appropriate hypothesis test for this question is the two-proportions test. …
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.…
Three methods for dissolving a powder in water show a different time (in minutes) it takes until the powder dissolves fully. The results are summarised in Figure 1.
There is an assumption that the population means of the three methods Method 1, Method 2 and Method 3 are not all equal (i.e., at least one method is different from the others). How can we test this?…
In some situations, Lean Six Sigma practitioners find a Y that is discrete and Xs that are continuous. How can we apply regression in these cases? Black Belt training indicated that the correct technique is something called b inary logistic regression or binary regression. But this tool is often not well understood.…
The two-sample t-test is one of the most commonly used hypothesis tests in Data Analytics or Lean Six Sigma work. The two-sample t-test offers the statistics for comparing average of two groups and identify whether the groups are really significantly different or if the difference is due instead to random chance.…
Measurement error is unavoidable. There will always be some measurement variation that is due to the measurement system itself.
Most problematic measurement system issues come from measuring attribute data in terms that rely on human judgment such as good/bad, pass/fail, etc. This is because it is very difficult for all testers to apply the same operational definition of what is “good” and what is “bad.”…
Lean Government. Even to the seasoned Lean practitioner, the idea of a Lean government sounds far-fetched. Governments are traditionally seen as the epitome of bureaucracy, and the guardians of red tape, incomprehensible forms and endless queues. But there are workable Lean strategies for governments seeking to reduce waste and become more efficient. Eight are outlined here.
Perhaps considering the eight ideas can spur government change agents to study Lean literature for potential improvement applications and in the longer run, start a Lean revolution in governments.…
Rejecting a null hypothesis when it is false is what every good hypothesis test should do. The “power of the test” is the measure of how good a test is. It is the probability that the test will reject Ho when in fact it is false.
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