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BPR, Lean Six Sigma, Operations

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 during the project meeting. The carefully prepared graphs unveil a remarkably shorter time for the whole process, from customer request to delivery of results. “We have applied a hypothesis test and the result is significant with a p-value of flat zero!” They sound like they know what they are doing. When asked for the change in the process, they all give different answers. When asked what the root cause to have achieved this effect was, their smiles fade. “We actually only implemented some Quick Hits. They turned out to have a greater effect than we thought. Isn’t this a nice surprise?” They asked. Or, is it just a Hawthorne?

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BPR, Cases, Innovation, Lean Six Sigma

Continuously Innovating Highly Effective Processes

Just some weeks ago, I filed my tax in Singapore. It took me about twelve minutes at my computer at home on a Sunday afternoon in April. It was not straight forward, I needed to make some amendments and additional inputs to what IRAS had already prepared for me. Yet, it was really easy to understand, very effortless to do and I have the strong feeling I did not make a mistake. Twelve minutes. Really.

"Chi-Square
Data Science, Lean Six Sigma

Making Sense of Chi-Squared Test – Finding Differences in Proportions

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).

"Test
Data Science, Lean Six Sigma

Making Sense of Test For Equal Variances

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.

"Kano
Cases, Customers, Data Science, Lean Six Sigma, Manufacturing

Make Use of Your Survey Data – Kano It

Nearly all medium-size and large companies spend hundreds of thousands or even millions on customer surveys every year. Customer survey results serve to amend strategies, design new products and services and focus improvement activities. Gathering customer survey data is only the first step. The second step involves making best use of the expensive data, analysing them, drawing business relevant conclusions and making important decisions. How are we doing in this step?

"Two
Data Science, Lean Six Sigma

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 (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.

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