Validating sampling plans
The before-data usually consists of one of three levels – no data, occasional data or lots of data.If the data does not exist, this is a potential project killer or could at least extend project cycle time significantly because the team will have to define, collect and validate its own data.Examples of raw data collection include: on tags, in log books, entered into data bases, scribbled on surveys, interpreted from phone conversations or automatically tallied by machinery.2. The answer to this question is typically a front line employee: operator, clerk, waiter or other. Combined with family of measure (Fo M), the answers can be used to create a handy reference sheet. Every team compares “after-data” to “before-data” to validate process improvements.If the data is scanned or automatically tallied by machinery or computer, then a simple entry of the method employed is adequate.3. The comparison can take on many forms and involve many different statistics.A fourth way to improve a process is to make it more capable, which was the early history and original purpose of Six Sigma.All four of these methods employ comparative experiments to validate process improvements.It is for these reasons that it is imperative teams learn how to employ histograms to graphically display central tendency and variability and to conduct and interpret normality checks.
However, it can quickly become more complicated if assumptions are violated, control groups are not employed and nonparametric statistics are required.A project team needs useful decision trees, additional software skills and a capable mentor (Black Belt, statistician or Master Black Belt) to help guide the team to create a valid process improvement validation strategy using comparative experiments, due diligence and good research methods.This section of the DMP only includes one question.Every main project metric should be at least displayed on an individual chart or median chart since both can handle counts and measures. What is the main purpose of the process improvement team?
There are essentially four ways to improve a process.The team should consider four points to help them lay out a process and validation strategy: Visualizing means is quite natural for most team members but looking at variability, skewness and outliers for the first time can be new and insightful.Is the team satisfied with the variability in its main project metric? What summary statistics are appropriate to describe the main project metric?Most teams by habit default to the mean; however, this has two problems – it does not include variability and it is not the right summary statistic for skewed data sets, which should be described by the median and percentiles.