What Is the Six Sigma Methodology?
The Six Sigma methodology is a set of practices designed to improve an organization’s overall performance. It does this through the systematic enhancement of business processes. Companies commonly implement the Six Sigma method to dramatically reduce defects, maintain product quality, and increase efficiency.
Every business process is an opportunity for a deviation to occur. This is especially likely when the process involves human functions. Each deviation has the potential to turn into a defect that, in turn, may result in additional costs to correct it. Six Sigma refers to the statistical level of having only 3.4 Defects Per Million Opportunities (DPMO).
It is easy to see why businesses want to achieve the Six Sigma rating. A 3.4 DPMO would significantly reduce expenses and increase profit. In essence, the Six Sigma methodology is a way for companies to reach a Six Sigma rating. It does this through a systematic implementation of projects that are intended reduce variations and increase efficiency. The DMAIC and DMADV are two methods generally used for Six Sigma projects.
The DMAIC serves as the basis of the Six Sigma methodology. It is a five-step process composed of the following stages: Define, Measure, Analyze, Improve and Control. The first step defines the goals a company wants to achieve. Along with that, the opportunities for quality improvement, such as minimizing defects, streamlining a process, or giving better customer satisfaction, are identified as well.
In the measurement step, the company sets up performance metrics. The metrics are then used to measure and collect data. The data is then analyzed in the next step to determine the probable cause of defects and identify solutions to reduce them.
The "improve" step is when the solutions for business process improvement are finally executed. Once executed, the last step—control—checks the results of the improvement step to ensure they are in line with the company's goals. Improvements that give unfavorable results are modified, while those that comply with the Six Sigma quality process are maintained.
DMADV—Define, Measure, Analyze, Design, and Verify—is a version of DMAIC adapted for creating new products and business processes. The fundamental difference between DMADV and DMAIC is the customer input. Customer satisfaction is integrated in every step of DMADV projects. The two final steps, Design and Validate, deals with acknowledging customer feedback on product design.
What Are Control Limits?
Control limits are a tool used in graphical analysis of a production process. The control limits represent the widest variation in outcomes from the production process that would be considered statistically normal; a breach of these limits suggests a likely problem with the production process. The control limits are different from arbitrary limits that a company may set to ensure either quality or customer satisfaction.
The use of control limits simply involves measuring individual outcomes of a production process that is intended to be consistent. For example, a widget factory might measure its widgets to make sure that they are all the same size, or at least very similar. Depending on the resources of the company, it may measure every single widget or simply take a representative sample. Such a sample would have to cover every possible input variable, such as different machines, different batches of raw materials and different operating staff.
Calculating the control limit involves first calculating the standard deviation. This is a mathematical process based on a large sample of data, for example an entire batch of widgets. The standard deviation uses a mathematical formula that calculates the average degree to which a single unit varies from the overall average. In this example, it would be the average amount by which the size of any randomly selected widget varies in size from the average of the entire batch. Standard deviation therefore tells you how close to identical or how varied an entire batch is.
The control limit is plus or minus three times the average variation. If the average widget is 10 inches (25.4 cm) wide and the standard deviation is 0.1 inch (2.5 mm), then the control limits will be 10.3 inches (26.2 cm) and 9.7 inches (24.6 cm). Statistically, with any production process — not just this widgets example — 99.73% of units will fall within the control limits.
The idea of these limits is to act as a signal that the outcomes are statistically unusual and thus there may be a production problem. Any time that an outcome falls outside of the limits acts as such a signal. Therefore, if any widget is measured at more than 10.3 inches or less than 9.7 inches, it should trigger an investigation into whether there are potential problems.
It's important to remember that the setting of control limits is a purely statistical process: a product breaching the limits is not necessarily of good or poor quality. Companies will often set their own limits to monitor based on qualitative or quantity factors. The widget company might decide to aim keep all widgets between 9.7 inches (24.6 cm)and 10.3 inches (26.2 cm) as a matter of quality. As a separate example, the company might be forced to keep all widgets between 9.9 inches (25.1 cm) and 10.1 inches (25.6 cm) because otherwise they wouldn't fit into the widget packets used for delivery. Such limits, chosen by the manufacturer, are known as tolerance limits or simply specifications.