Everyone who has faced a production problem with a need to solve the problem by using production data can relate to the notion of a brain teaser. Brain teasers have three parts: (1) the situation, (2) available data or other supporting information and (3) questions that various workers want answered for continual improvement. Recommended solutions follow in the next issue and on the Web at Quality Online (www.qualitymag.com).

Stacey works as the day-shift maintenance supervisor in a bakery that makes hamburger buns for a large restaurant chain. Part of his job is to coordinate the preventive maintenance schedule for all of the equipment, some of which is offline too often. The dough press, for instance, which flattens dough balls into the shape of a bun, has been troublesome and must be shut down for maintenance every two weeks without fail. Two to three days after maintenance, however, the production department experiences problems with the dough sticking to the press and creating double and sometimes triple-decker buns. A production worker has to then be stationed at the press to pull the stuck buns apart. In a particularly bad run, this leads to a lot of waste and a loss of production.

Stacey suspects that this machine needs extra maintenance periodically, but he is not permitted to shut it down because the line must keep running to meet daily goals. He decides to see what he can learn about the press using production data.

Available data
Because Stacey doesn't have historical data that documents the frequency of equipment problems, he decides to check the press several times during the day shift to gage the extent of the sticking problem. He sets up a routine to go by the press four times during the shift and record the number of times a bun sticks to the machine during a 10-minute period. He keeps a sheet of the time and the number of "stuck" buns for the two-week period between the preventive maintenance work. His data are in the table, Number of "Stuck" Buns at the Press.

1. How should Stacey analyze these data?

2. What can Stacey learn about the overall behavior of the press by analyzing the "stuck" bun problem?

3. How can Stacey use these data to determine a better maintenance schedule for the press and still meet product targets?

Answers to December Brain Teaser

Q: If Virginia uses the percent deviation from the target value for the short runs produced on Reactor 11, what does the analysis of the data show with respect to the behavior of Reactor 11?
A: An average and range chart of the percent deviation from target has two ranges above the upper limit for product D. This suggests that the variability of product D may be higher than that of the other products, as seen in the chart, Percent Deviation from Target for Reactor 11.

Q: What assumption is made when using the percent deviation from target?
A: Because the specifications were quoted as a percent deviation from target, + or - 5% from target, Virginia assumed that the variation of the layering process would be a percentage of the target. She has a wide range of target values for her process, but the standard deviation of the measurements will not always vary as a percent of the target. In this situation, the standard deviations calculated from the data values for each product vary from 5.5% of the target value for product C to 11.5% for product D.

Q: What would be a more appropriate way to analyze the data?
A: If the variability of the data values is different for the different target values in the reactor, the best way to analyze the data and understand the behavior of the reactor is to plot the deviation from target divided by the estimated value of the standard deviation for that specific product. These values are called Zed values. The resulting chart, Zed Chart for Reactor 11, shows that the process is predictable. Because the specifications are written as a percent of target, the capability analysis would need to be done for each product separately. This situation illustrates the difference between analyzing data to understand the behavior of a process versus analyzing data to determine if the product meets specifications.