The 2013 ASQ World Conference is taking place this week in Indianapolis, Indiana, and it's been a treat to see how our software was used in the projects highlighted in many of the presentations. As a supporter of the conference, a key event for quality practitioners around the world, Minitab was proud to sponsor one of the presentations that seemed to get a lot of attendees talking. Scott Sterbenz, a Six Sigma leader from Ford Motor Company, delivered a presentation entitled "Leveraging Designed Experiments for Success," which explained how to make designed experiments succeed with examples learned from experiments done in a variety of situations.
In statistics, DOE refers to the creation of a series of experimental runs, or tests, that provide insight into how multiple variables affect an outcome, or response. In a designed experiment, investigators change more than one factor at a time, and then use statistical analysis to determine what factors are important and identify the optimum levels for these factors. It’s an efficient and economical way to improve almost any process. DOE can be used to create and analyze many different kinds of experiments, and Minitab's DOE tools can help investigators identify the best experimental design for their situation, based on the number of variables being studied and other conditions.
One of the examples Sterbenz talked about involved a Design of Experiments project he led at Ford. Shortly before the 2011 Ford Fiesta was supposed to make a very high-profile debut, a cosmetic problem with the vehicle’s carpet arose. Given that the redesigned Fiesta embodied the idea that an affordable car needn't skimp on high-quality interiors, the issue needed to be solved, and fast. Sterbenz and the company’s Body Interior Six Sigma team met that challenge with a fractional factorial designed experiment that could give them the information they needed in only 34 runs.
When data from the 34 runs were analyzed in Minitab, the results revealed complex interactions between the different settings on the equipment used to make the carpet. The interactions explained why previous adjustments to individual settings had failed to find a way to eliminate the problem. This designed experiment not only provided the team with a list of significant variables and interactions, but also with equations to show how the inputs affected the responses. Even better, the results showed that optimization settings for eliminating brush marks did not have an adverse effect on the plushness. Incredibly, the entire project took 12 days, from the time the problem was defined to the point where the solution was in place and the process was under control.
Sterbenz is an avid bowler, and he also shared an example of how the U.S. Bowling Congress (USBC) used a designed experiment to assess whether or not to modify one of its existing specifications for bowling balls. The team was looking at the requirements surrounding a ball's static weight, and had identified six distinct factors to assess. Testing each factor one-by-one would be prohibitively expensive and time-consuming, so the USBC team used Minitab’s DOE tools to create a designed experiment in which they could change more than one factor at a time, then use statistics to determine which ones have significant effects on a ball's path along the bowling lane.
When the team used Minitab to analyze their data, in addition to obtaining an average of the shots and graphing the ball path, they also got a big surprise. Previous research had identified three distinct, mathematically predictable phases of bowling ball motion, and the analysis showed that a ball's static weight appeared to have very little effect on these phases. But analyzing the newly collected data with Minitab revealed that a ball’s static weight could result in a previously unidentified fourth phase of motion. This DOE not only demonstrated the necessity for the existing specifications for static weight, they also enhanced the organizations understanding of the factors that affect a bowling ball's path and opened up new areas of study for the USBC.
In an hour, Sterbenz demonstrated how DOE can be used to improve everything from the carpet in an automobile to our understanding of bowling-ball physics, but the range of challenges that might be addressed through designed experiments is literally unlimited. It's a good bet many of the people who attended Sterbenz's presentation left with ideas about how they could apply DOE in their own organizations. What about you?
Have you used a designed experiment to solve a quality challenge?