In this article, I will tell you some interesting facts from the third training session of the Green Belt Lean Six Sigma training. This week captured the core of the Six Sigma model focussing on the following steps: measurement and analysis.
This week was all about measuring and statistics. As we like to say in the Netherlands ‘meten is weten’ and as Trump would say, it’s the truth! This time he is actually correct, to improve a process you need to know what is going on. But before you just start conducting some measurements, you really have to think about it. The purpose of measuring is to collect data which will help you understand the performance of your processes. Nowadays, many companies do not measure in the right way; they are often too late, measurements are used for punishment or are too financially oriented.
As I mentioned in my previous article, it is all about the customer requirements. So while measuring the process you should focus on the outputs, because that is what the customer gets. A firm should align their process outputs with the customer requirements. For instance, the delivery time and the accuracy of the delivery. You can have a perfectly well performing process, but the process does not deliver the output that the customer desires. This was a nice insight what I have not really thought about before.
Another thing that I learned during this session is the awareness regarding the agreements about measuring. We got the assignment to write down ‘Tilburg University in the Netherlands’ five times, two people had to record the time spend on this exercise. It was pointed out that, before you start the exercise you should make clear agreements on what to measure; the goals, the situation, the methods used etc. The lecturer mentioned that we should be careful while using secondary data, if you do not know how the data is collected and with what purpose in mind. There is a substantial chance that the data is incorrect or incomplete, so thesis-writers BE CRITICAL.
Speaking about chances and data collection, many students (including myself) had to make a sad conclusion during the session. Despite all the blood, sweat and tears, we had forgot almost everything we had learned during statistics in the previous years. We had to perform several statistical tests (e.g. hypothesis testing) and it was really hard to remember all the procedures and steps that you have to take. Nevertheless, it was a very nice way to measure problems in a process. Using statistics to discover internal errors allowed us to make valid conclusions based on facts instead of guessing.
For example, through the use of hypothesis tests, we discovered several problems within a global supply chain network. Statistics enables the project leader to make the improvement process more efficient, because you can filter out possible causes, which do not have a significant influence on the process.
Thus, measurements lay the foundation for analyses, when the data is collected in a proper way. When taking validity, repeatability and reproducibility into account, analyses can be performed. A brainstorm session can be used to identify possible causes, consequently statistical methods are used to check their validity and importance.
Taken together, it was another interesting and challenging session. During my statistics course, I never thought about using statistics in such a manner. This makes the practical mind-set of this course so valuable. Check the quality of data, base your findings on facts and use them to make decisions, is something we have to learn for years. However, through the practical approach, I really start to appreciate this way of thinking and it once again becomes clear how important it is to understand the process. You cannot value what you do not thoroughly understand.