The Six Sigma methodology is not new - it has been around since the 80s. What is interesting is its long shelf life. It continues to be relevant to modern day operations despite rapid technological advancements. A data-driven approach, it helps reduce defects in products and services, while increasing customer satisfaction through continuous process improvement1 2. As the key focus of Six Sigma is process improvement, organizations can apply Six Sigma principles across various initiatives and activities, including data science, business intelligence (BI) systems and data quality. Every process has one or more independent input factors f(Xs) that are transformed by a function to produce an output. Six Sigma is calculated as Y = f(x) + Ɛ where ‘Y’ is the desired outcome or the result of the process which depends on the f(Xs). Here Ɛ represents the error factor or how accurately the f(Xs) are transformed, to create the outcome.
To ensure accurate results using the Six Sigma formula, it is important to focus on the causes, i.e. the f(Xs) and not on the result ‘Y’. By identifying causes and negating them, users can automatically improve the result as the objective of Six Sigma is to improve process performance by reducing variance. The more variation a process has, the lower will be the quality of the output. However, to achieve process improvement breakthroughs, it is critical to minimize the input variable variance in order to minimize the output variable variance.
Marrying Six Sigma and analytics for optimal process performance
Let’s take a deep dive into how Six Sigma and analytics work together to improve quality outcomes in the age of Machine Learning (ML) and Artificial Intelligence (AI).
Measuring phase: The first step is to determine data quality for accuracy and precision, and clean it for further use. In a typical scenario, governed data quality initiatives are conducted at a global or regional level, as opposed to specific analytics projects.3 The next step involves descriptive analytics that answers the question “What happened?” This step explores the data to visualize and understand what the data is saying. To do this, we use the Y = f(X) + Ɛ equation.
Analyzing phase: Once users understand the variables and features, it is easy to diagnose factors and root causes that affect the problem outcome. The variables are represented as the Xs in the Y = f(X) + Ɛ. equation. Often, in ML, the number of features can be reduced without affecting the result through the analyzing phase of Six Sigma.
The next step in the process involves using predictive and prescriptive analytics to answer questions such as “What could happen?” and “What should we do?” For successful results, it is important to select the right models and conduct several experiments to determine which model produces the best results. When applied to ML, Six Sigma ensures that a process reaches its target with accuracy (centering), and precision (spread) for optimal and sustainable process performance. For example, in the game of archery, if a target is reached without variance control (the spread around the target), there is no guarantee that the shots will be centered. However, if variance is reduced, it will result in a much tighter cluster (see figure.1).