Those that have taken my Black Belt or Green Belt training know that my two favorite examples of data analysis often come from biking and bowling, as they are two of my favorite activities outside of working in the automotive industry. During the summer, I ride my mountain bike about 15 miles to work, and I record my riding time each and every ride. Yes, because I'm nerdy about things like that. (I also have a record of every single certified bowling game I have rolled since 1992, but that is for another day.) My goal every time I ride into work is to beat my best time, so I am riding hard. But are my riding times the same every time I ride in? Or course not! There is variation in those times that can be attributed to controllable common cause, non-controllable common cause, and special cause. Controllable common cause would include things like the air pressure in my tires, chain lubrication, and my energy level. Non-controllable common cause includes things like wind direction, wind speed, air temperature, and traffic. What about special cause? When I ask for examples of special cause, my students usually only think of ones that slow me down. Some common and interesting examples--trains, funeral procession, goose crossing, construction detour, flat tires, and so on. But one I also like to mention is dogs. On more that one occasion, I have been chased by an unleashed dog. What happened to my times on those rides? They were special cause--but in a good direction! If I were chased by a dog every time I rode into work, my average times would be much faster. So the next time you detect a special cause that affects your results in a good direction, see if there's a way you can incorporate that special cause for your benefit. Special cause isn't always a bad thing!
Scott C. Sterbenz, P.E.
ASQ Six Sigma Forum