Design of Experiments:
Six Sigma strives to forestall process variation because variation hinders a process’s ability to reliably and consistently deliver high-quality products or services. the look of Experiments (DOE) method allows quality teams to together investigate multiple potential causes of process variation. DOE is additionally is additionally called Designed Experiments or Experimental Design and begins by identifying the key factors that would cause process variance. The Designed Experiments tool contains three elements. for instance, if the DOE were used on the method of creating a pizza the weather would come with the following:
- Factors – These are inputs to the method. Factors are studied as either controllable or uncontrollable variables. Factors within the pizza example include the oven, dough, sauce, and toppings.
Levels – These are the potential settings of every factor. the degree within the pizza making process are the temperature of the oven, the cooking time and therefore the amount of sauce and toppings used.
Response – this can be the output of the experiment. DOE strives for a measurable output that's influenced by the factors and their differing levels. The response or output from the instance is how the pizza tastes.
Select the Factors
There are often variety of inputs in an exceedingly process that may affect the output. The factors that are most relevant to the tip result are those most vital to DOE. These factors are often selected by the project team in an exceedingly brainstorming session. In ordinary resources where time and budget are finite, the team should limit the experiment to 6 or 7 key factors. These factors are controlled by setting them at different levels for every run.
Set the Levels
Once the factors are selected, the team must determine the settings at which these factors are going to be run the experiment. the instance of cooking a pizza demonstrates that some factors are measured in numbers, like oven temperature and cooking time. Some factors are qualitative like which toppings are used; they're measured in categories and are converted into coded units for rectilinear regression analysis. The more levels that are identified for every factor the more trials are going to be required to check these levels. to make sure that an optimal number of levels are selected, specialise in a variety of interest. This range includes settings utilized in the traditional course of operations and also may include settings of more extreme scenarios. The greater the difference in factor levels the simpler it becomes to live variance.
Evaluate the Response
The response is that the outcome of the experiment. Outcomes are most helpful in improving the method after they are often measured in quantitative terms instead of in qualitative attributes. A response that's quantifiable makes the experiment similar temperament to the extra scrutiny of simple regression techniques. Design of Experiments allows inputs to be changed to work out how they affect responses. rather than testing one factor at a time while holding others constant, DOE reveals how interconnected factors respond over a large range of values, without requiring the testing of all possible values directly. This helps the project team understand the method way more rapidly.