Capacity Management Game
In the Capacity Management game, students can buy and sell machines at each of the three workstations. They can also change the way Station 2 (the testing station) is scheduled. They can choose first-in-first-out (FIFO), give priority to step 2 or give priority to step 4. The purpose of this assignment is for students to utilize queuing concepts and forecasting methods to manage capacity. This game takes 7 days. In the simulation, customer demand is random and the students are told that demand is expected to grow at a linear rate for the first several months, stabilize, and then decline at roughly a linear rate. Customer orders that are not filled within the quoted lead time incur a late penalty. If the order is too late, then it will not generate any revenue. When the game begins there are 50 days of history and Station 1 is already near 100 percent utilization. Thus, the students are faced with a tradeoff between capacity and waiting time. They can buy machines to reduce waiting time in order to meet the quoted lead time; however, they don’t want to buy too many machines because the machines are expensive. I find that most students figure out that there is a tradeoff between capacity and waiting time. However, many teams wait until the lead times become so long that they are making little or no revenue before they buy machines. Since these reactive teams generally do not do as well as proactive teams, students learn that it is better to extrapolate station utilization by forecasting demand in order to determine when utilization will approach 100%. In my MBA class, I found that several teams went a step further by estimating the amount of cash that would be lost to delays if they did not purchase a machine and comparing this lost revenue to the cost of a new machine. These teams found that it was better to accept some lost revenue during the peak months of demand rather than buy another machine.