Competitive bidding has become an integral part of the project management responsibility in many industries. If the bid is too low, the company may have to incur the cost of the overrun out of its own pocket. For a small firm, this overrun could lead to financial disaster (Kerzner, 2006). Perhaps the most difficult projects to estimate are those that involve the development and manufacturing of an important quantity of units. Experience curves are based on the old adage that practice makes perfect. A product can always be manufactured better and in a shorter time period not only the second time, but each succeeding time (Kerzner, 2006).
      Organizations have often used learning curves to predict the improvement in productivity that can occur as experience is gained of a process. Thus learning curves can give an organization a method of measuring continuous improvement activities. If a firm can estimate the rate at which an operation time will decrease then it can predict the impact on cost and increase in effective capacity over time (Greasley, 2009).
      Learning curve theory is based on three assumptions. First of all, the amount of time required to complete a given task or unit of a product will be less each time the task is undertaken. A second assumption says that the unit time will decrease at a decreasing rate, and finally the reduction in time will follow a predictable pattern (Jacobs, 2009).

Application of Learning Curves
      The table for process performance data for the metric identified in the Pizza Store Layout Simulation is as follows
Table 1
Process performance data for the metric identified in the Pizza Store Layout Simulation
S. No. |Weeks |No of Customers for Group of 2 |No of Customers for Group of 4 |Avg. Wait Time(Min) |Avg. Queue Length |Profit ($) | |1 |0 |70 |106 |11.32 |3.04 |1,054 | |2 |1-2 |73 |103 |4.93 |2.52 |1,327 | |3 |3-4 |71 |105 |5.51 |2.68 |1,439 | |4 |5-6 |70 |106 |5.35 |2.62 |1,539 | |5 |7-8...