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Eston Martz
Published: Tuesday, October 25, 2016 - 15:41 If you were among the 300 people who attended the first-ever Minitab Insights conference last month, you already know how powerful it was. Attendees learned how practitioners from a wide range of industries use data analysis to address a variety of problems, find solutions, and improve business practices. For those who weren’t there, here are five helpful, challenging, and thought-provoking ideas and suggestions that we heard during the event. Joel Smith of the Dr. Pepper Snapple Group used the assessment of different beers to show how applying the tools in Minitab can help a business move from raw voice of the customer (VOC) data to actionable insights. His presentation showed how to use graphical analysis and descriptive statistics to clean observational VOC data, and then how to use cluster analysis, principal component analysis, and regression analysis to make informed decisions about how to create a better product. Graphs are often part of statistical analysis, but a graph might not be the only way to visualize your results. Think about your audience and your communication goals when choosing and customizing your graphs, suggested Rip Stauffer, senior consultant at Management Science and Innovation. He showed examples of how the same information comes across very differently when presented in various charts, and when colors, thicknesses, and styles are selected carefully. Along the way, he also illustrated Minitab's flexibility in tailoring the appearance of a graph to fit your needs. We hear all the time about the effect of quality improvement methods on manufacturing. But what about using statistical analysis to boost sales? Andrew Mohler from global chemical company Buckman explained how training technical sales associates to use data analysis has transformed the company's business. Empowering the sales team to help customers improve their processes has enabled the company to provide more value and to drive sales—boosting the bottom line. In the quality improvement world, we tend to think that transforming an organization’s culture so everyone understands the value of data analysis only brings benefits. But Richard Titus, a consultant and adjunct instructor at Lehigh University who has worked with Crayola, Ingersoll-Rand, and many other organizations, highlighted potential traps for organizations with a high level of statistical knowledge. These include trying to find data to fit favored answer(s); working as a “lone ranger” independent of a team; failing to map and measure processes; not selecting a primary metric to measure success; searching for a “silver bullet”; and trying to outsmart the process. T. C. Simpson and M. E. Rusak from Air Products illustrated how using a traditional P chart to monitor a transactional process can lead to problems if you have a large subgroup size. False alarms or failure to detect special-cause variation can result from overdispersion or underdispersion in your data when your subgroup sizes are large. You can avoid these risks with a Laney P' control chart, which uses calculations that account for large subgroups. Learn more about the Laney P' chart. Tomorrow I’ll offer five more quality tips from the conference. Quality Digest does not charge readers for its content. We believe that industry news is important for you to do your job, and Quality Digest supports businesses of all types. However, someone has to pay for this content. And that’s where advertising comes in. Most people consider ads a nuisance, but they do serve a useful function besides allowing media companies to stay afloat. They keep you aware of new products and services relevant to your industry. All ads in Quality Digest apply directly to products and services that most of our readers need. You won’t see automobile or health supplement ads. So please consider turning off your ad blocker for our site. Thanks, 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 master’s 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. That’s why he writes for Minitab’s blog: “I’ve 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.”Five Powerful Insights From Noted Quality Leaders
Minitab Insights conference gathered problem solvers from diverse industries
You can get more information from voice-of-customer (VOC) data
Consider multiple ways to show results
Quality methods make great sales tools
Data-driven cultures have risks, too
When subgroup sizes are large, use P' charts.
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Eston Martz
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