Five More Critical Six Sigma Tools: A Quick Guide |

Quality Digest198 day(s) ago

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Five More Critical Six Sigma Tools: A Quick Guide |

Quality Digest198 day(s) ago

The Six Sigma quality improvement methodology has lasted for decades because it gets results Companies in every country around the world and in every industry have used this logical step-by-step method to improve the quality of their processes products and services And theyve saved billions of dollars along the way ADVERTISEMENT However Six Sigma involves a good deal of statistics and data analysis which make many people uneasy Individuals who are new to quality improvement often feel intimidated by the statistical aspects Dont be intimidated Data analysis may be a critical component of improving quality but the good news is that most of the analyses we use in Six Sigma arent hard to understand even if statistics isnt something youre comfortable with Just getting familiar with the tools used in Six Sigma is a good way to get started on your quality journey In my last column I offered a rundown of five tools that crop up in most Six Sigma projects Here Ill review five more common statistical tools and explain what they do and why theyre important in Six Sigma 1 T-tests We use t-tests to compare the average of a sample to a target value or to the average of another sample For example a company that sells beverages in 16-oz containers can use a 1-sample t-test to determine if the production lines average fill is on or off target If you buy flavored syrup from two suppliers and want to determine if theres a difference in the average volume of their respective shipments you can use a 2-sample t-test to compare the two suppliers 2 ANOVA Where t-tests compare a mean to a target or two means to each other ANOVA which is short for analysis of variance lets you compare more than two means For example ANOVA can show you if average production volumes across three shifts are equal You can also use ANOVA to analyze means for more than one variable For example you can simultaneously compare the means for three shifts and the means for two manufacturing locations 3 Regression Regression helps you determine whether theres a relationship between an output and one or more input factors For instance you can use regression to examine if there is a relationship between a companys marketing expenditures and its sales revenue When a relationship between the variables exists you can use the regression equation to describe that relationship and predict future output values for given input values 4 DOE design of experiments Regression and ANOVA are most often used for data that have already been collected In contrast design of experiments DOE gives you an efficient strategy for collecting your data It permits you to change or adjust multiple factors simultaneously to identify whether relationships exist between inputs and outputs Once you collect the data and identify the important inputs you can then use DOE to determine the optimal settings for each factor 5 Control charts Every process has some natural inherent variation but a stable and therefore predictable process is a hallmark of quality products and services Its important to know when a process goes beyond the normal natural variation because it can indicate a problem that needs to be resolved A control chart distinguishes special cause variation from acceptable natural variation These charts graph data over time and flag out-of-control data points so you can detect unusual variability and take action when necessary Control charts also help you ensure that you sustain process improvements into the future Conclusion Any organization can benefit from Six Sigma projects and those benefits are based on data analysis However many Six Sigma projects are completed by practitioners who are highly skilled but not expert statisticians A basic understanding of common Six Sigma statistics combined with easy-to-use statistical software will let you handle these statistical tasks and analyze your data with confidence About The Author For Eston Martz analyzing data is an extremely powerful tool that helps us understand the world which is why statistics is central to quality improvement methods such as lean and Six Sigma While working as a writer Martz began to appreciate the beauty in a robust thorough analysis and wanted to learn more To the astonishment of his friends he started a masters degree in applied statistics Since joining Minitab Martz has learned that a lot of people feel the same way about statistics as he used to Thats why he writes for Minitabs blog Ive overcome the fear of statistics and acquired a real passion for it says Martz And if I can learn to understand and apply statistics so can you

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