Lean Six Sigma and Data Analytics

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Lean Six Sigma and Data Analytics

Data analytics is integral part of Lean Six Sigma. It can be helpful in problem solving through data by establishing the significance of the relationship between problems (Y) and potential root causes (X). Because there is a large variety of tools available, the selection of the right tools is critical.

That selection of tools depends on the data analytics task at hand, the overall objective as well as source and types of data given. Discrete data, such as counts or attributes require different tools than continuous data, such as measurements. Whilst it is possible to transform continuous data into discrete data for decision making, this process is irreversible.

Problem Solving Through Data Analytics

Problem Solving Through Data Analytics

Problem Solving Through Data Analytics

Depending on the data in X and Y, regression analysis or hypothesis testing will be used to answer the question whether there is a relationship between problem and alleged root cause. This leads to problem solving through data.

Most noteworthy, these tools do not take away the decision, but they tell the risk for a certain decision. The decision is still to be made by the process owner (example).

Steps to Problem Solving Through Data Analytics

The road map for these tests follows a certain standard:

  1. Plot the data. Since this step might help to prepare a decision, it is key to graphically analyse the data. Otherwise the statistics might be misleading. Therefore, there is a variety of data display tools available.
  2. Formulate the hypothesis. The hypothesis or model is the statistical expression for the practical problem. There is usually a null-hypothesis and an alternative hypothesis. Almost all tests target on rejecting the null-hypothesis and accepting the alternative hypothesis.
  3. Decide on the acceptable risk. Since hypothesis testing and regression tools do not take a decision but give the risk for a certain decision, it is key to define the acceptable risk (α) before conducting the test.
  4. Select the right tool, i.e. the appropriate hypothesis test for a certain situation. More often than not, a series of tests is necessary under certain circumstances. Read more about 2-sample t-test, ANOVAlinear regression, binary logistic regression, 2-proportion test.
  5. Test the assumptions. Because many statistical tools give only valid results under certain circumstances, these circumstances must be in place.
  6. Conduct the test. If all assumptions are valid, perform the test or regression on the actual data set.
  7. Make a decision. Decide whether there is enough evidence to reject the null-hypothesis and accept the alternative. Translate the statistical outcome into practical relevance.

Wide Application of Problem Solving Through Data Analytics

In conclusion, applications for problem solving through data analytics are evident in all private and public organisations without limits. Already some decades ago, companies like Motorola and General Electric discovered the power of data analytics and made this the core of their Six Sigma movement. They made sure, that problem solving was data-driven and applied data analytics wherever appropriate. Nowadays, data analytics is widely used for problem solving as part of most Lean Six Sigma projects. Black Belts are usually well-versed in this kind of data analysis.

I only believe in statistics that I doctored myself. Winston Churchill