Situation 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. Questions 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?
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