Applying_the_Learning_Curve_Theory

Applying the Learning Curve Theory OPS/571 Applying the Learning Curve Theory The fundamental thought behind the learning curve theory is the improvement in the performance of a process due to repetition of tasks executed by individuals or organizations over and over again. According to Chase, Jacobs, and Aquilano (2006), the learning curve theory is based on three assumptions as follows: 1. The amount of time required to complete a given task or unit of a product will be less each time the task is undertaken 2. The unit time will decrease at a decreasing rate 3. The reduction in time will follow a predictable pattern The Pizza Store Layout simulation utilizes the family-owned Mario’s Pizzeria to demonstrate the learning curve theory. In this simulation, the goal is to reduce the waiting time in the restaurant by optimizing the processes for the peak time in the restaurant to maximize the benefits. The learning curve would play a critical role here, taking into consideration the training time that will go into implementing methods that will reduce wait time and improve productivity. These methods include the purchase of the new automatic order taking system Menu Point and the new Plax Ovens. Learning Curve Concepts to Test Alternative Process In order to maximize the benefits and optimize the peak time processes, the first few weeks were spent observing the operations of the restaurant in order to improve the amount of time spent waiting in the queue at Mario’s Pizzeria. The process performance data for the performance metrics identified in the Pizza Store Layout Simulation is as follows: Scenario Number | Weeks | Customers for Group of 2 | Customers for Group of 4 | Average Wait Time (Min) | Average Queue Length | Profit | 1 | 0 | 70 | 106 | 11.67 | 3.21 | 1054 | 2 | 1-2 | 71 | 105 | 6.46 | 2.56 | 1120 | 3 | 3-4 | 72 | 104 | 5.38 | 2.58 | 1509 | 4 | 5-6 | 67 | 109 | 4.34 | 2.46 | 1665 | 5 | 7-8 | 91 | 148 | 3.15 | 2.81 | 2061 | The learning curve can be developed by many curve fitting methods like arithmetic tables, logarithmic scale, or other methods. Average Wait Time In the review of the simulation results shown above, there is a distinct improvement in the average wait time from the first week through the eighth week. It has decreased at the end of the process after applying the optimization strategy and addressing the bottleneck in the process. An analogous interpretation, using the same tabular format that was used for displaying the average wait time and queue length, can be made for the profit gained by the enhancements made to the process. Profit In reviewing the graph above, there is a considerable increase in profits for the customer from the first week through the eighth week from the Mario’s Pizzeria simulation. By changing the different parameters, utilizing Menu Point and the Plax Oven, the shortfall in the process was addressed. Conclusion The operating characteristics such as the customer queue length, the time spent waiting in line, the number of customers in groups of two, the number of customers in groups of four, and the profit was of top concern to the manager of Mario’s Pizzeria. By analyzing the data associated with average queue length, average wait time, groups of two people, groups of four people, and profit, it was easy to determine where the bottlenecks in the process were and how to correct them. References Chase, R.B., Jacobs, F.R., & Aquilano, N.J. (2006). Operations management for competitive advantage. Retrieved from https://portal.phoenix.edu/classroom/coursematerials/ops_571/20110125/. University of Phoenix. (2002). Pizza Store Layout Simulation [Computer Software]. Retrieved from University of Phoenix, Simulation, OPS571 website.