Controlling Quality of Production
Everybody seems to disagree about just why so many parts have to be fixed or thrown away after they are produced. Some say that it is the temperature of the production process, which needs to be held constant (within a reasonable range). Others claim that it is clearly the density of the product, and that if we could only produce a heavier material, the problems would disappear. Then there is Ole, who has been warning everyone forever to take care not to push the equipment beyond its limits. This problem would be the easiest to fix, simply by slowing down the production rate; however, this would increase costs. Interestingly, many of the workers on the morning shift think that the problem is “those inexperienced workers in the afternoon,” who, curiously, feel the same way about the morning workers. Ever since the factory was automated, with computer network communication and bar code readers at each station, data have been piling up. You have finally decided to have a look. After your assistant aggregated the data by 4-hour blocks and then typed in the AM/PM variable, you found the following note on your desk with a printout of the data already loaded into the computer network: Whew! Here are the variables: · Temperature actually measures temperature variability as a standard deviation during the time of measurement. · Density indicates the density of the final product. l Rate indicates the rate of production. · AM/PM is an indicator variable that is 1 during morning production and is 0 during the afternoon. · Defect is the average number of defects per 1000 produced. Naturally you decide to run a multiple regression to predict the defect rate from all of the explanatory variables, the idea being to see which (if any) are associated with the occurrence of defects. There is also the hope that if a variable helps predict defects, then you might be able to control (reduce) defects by changing its value. Here are the regression results as computed in your spreadsheet.37 Discussion Questions 1. What are the “obvious conclusions” from the hypothesis tests in the regression output? 2. Look through the data. Do you find anything that calls into question the regression results? Perform further analysis as needed. 3. What action would you recommend? Why?