SIX SIGMA STATISTICAL TOOLS AND TECHNIQUES

The Six Sigma methodology offers a comprehensive six sigma statistical tools and techniques (toolkit of tools, charts, diagrams, methods), and processes for implementing continuous improvement projects. To achieve a successful result, you must select the appropriate tool for the job, just like you would in any toolbox. Six Sigma is a process for improving quality that uses sophisticated statistical tools and statistical analysis.

The essential statistical tools and their efficient usage in a Six Sigma project will be the only subject of this article. Statistical tools and analysis use all standard project lifecycle phases – define, measure, analyse, improve, and control (DMAIC), with the effort and analyse steps receiving the most excellent attention. This lecture will cover the most regularly used Six Sigma statistical tools, as there are over 100 statistical tools and unlimited permutations of those tools.

Six Sigma:Statistical Tools and Techniques-The phase should be Defined.

Understanding the problem and the project’s desired goal emphasises the defining phase of every Six Sigma programme. As a result, non-statistical approaches are used extensively throughout this period. One tool that can assist you with problem statements is Pareto analysis. A Pareto chart is a simple graph that shows the most common problems. Consider the Pareto principle, the ’80/20 rule,’ according to which 20% of your problems are by 20% of your issues. While the Pareto is most typically employed in the measure and analyse phase, it may also be quite helpful in the define phase for narrowing down a specific issue. For example, the top reasons for callers placed on hold at a customer support centre depict in the sample chart. Due to research, almost half of the calls got cancelled, and more than 80% were for three reasons! The Pareto chart helps identify problem areas and possible solutions early on in a project.

Six Sigma:Statistical Tools and Techniques-Measure phase.

The measure phase quantifies the issue at hand and provides preliminary data analysis. The first tool we’ll utilise is descriptive statistics, a report that summarises the data surrounding your case. The average, mean, and mode measures of problem frequency (also known as a central tendency) include the descriptive statistics and the data variability (known as dispersion). The Six Sigma team will be able to determine the scale of the problem (limited or broad), the frequency with which it occurs, and whether or not a problem exists.

The Pareto chart can appear to further identify and define your problem characteristics in the measure phase. A histogram also seems to represent the frequency distribution data in a graph. For example, the histogram would display how many callers waited 10 seconds, 20 seconds, 30 seconds, and if you measured how long it took to answer the phone for a customer service centre in 10-second intervals. The histogram analysis examines the distribution (normal or abnormal) and the frequency with which the results fall outside your limitations or goal answer time. Consider the histogram to be a baseline measure that will appear to identify areas for improvement.

The measuring system analysis is one of the fundamental approaches used throughout the measurement phase (MSA). The MSA will decide if your data is dependable as a decision-making tool by looking at its correctness and how it is acquired and measured. Because Six Sigma is a data-driven approach, all measurements must be reliable. Therefore, this analysis relies on repeatability and reliability.

Six Sigma:Statistical Tools and Techniques-Examine the Analyse Phase

The analysis phase is the most statistic-dependent step of the Six Sigma DMAIC process.

These statistical techniques are concerned with the process’s stability and capabilities. To begin, we must determine whether the process under investigation is ‘under control’ and work so that we can rely on the data observations (the stability of the process). The next step is to determine whether the process statistically supports the intended requirements.

Six Sigma:Statistical Tools and Techniques-Control Phase

The Six Sigma team will examine the control chart for the target data to determine the stability of your process. For argument’s sake, assume that we desire to get better and the time it takes our customer service centre to answer the phone. We can tell if the operation is consistent and under control by looking at the control chart. This project’s control chart demonstrates a lot of variation, but the procedure is under control. All calls fall between the upper and lower control levels (targets), although several miss the mark.

We need to check if the process can support our targeted changes now that we know our data is reliable and the process is under control. In our case, we’d evaluate if our customer service centre could handle calls in 30 seconds on average, but no more than 60 seconds overall. We would do a process capability analysis to accomplish this. Process capability analysis is a useful statistical tool for determining the current status of an activity and its subsequent capabilities.

In our scenario, around half of the calls fall outside the upper service level of 60 seconds, failing to meet their targets. Statistical research also suggests that the centre cannot fulfil those goals in its current state. It is statistically impossible for them to perform the targets you have set unless something changes. The process capability values (Cp/Cpk) given on the chart determine this. According to a more complex computation, to accomplish our goals, that number must be greater than 1.33. However, we’ve just demonstrated that they require assistance; we’re here to help!

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