Apply_the_Learning_Curve_Theory

Apply The Learning Curve Theory Tricia Williamson University of Phoenix Apply the learning curve theory The basic principle behind the learning curve theory is the development in performance process from repetitiveness of tasks completed by individuals or groups. The theory has three assumptions, which are: 1. The time needed for task completion decreases as it is repeated 2. The percentage of improvement decreases with corresponding increases in volume 3. The rate of improvement is predictable over a certain amount of time. It has been shown that in the process of performance, production units are doubled in reduced amounts of time after certain periods of implementation. The slope of the curve is calculated by the difference between the rate of learning and numerical value of one hundred. If the timing between the doubling of units is decreased by 10%, then it will be a 90% learning curve. The table for process performance data for the metric identified in the Pizza Store Simulation is in attachment one. Below is the plot for each week denoted by S.No. on the X-axis and Average wait-time in minutes on the Y-axis. Clear-cut improvement in average waiting time was shown. The wait time has lessened from 11.67 to 3.45 minutes after applying optimization strategy and eliminating the blockage in process. By doing the analysis of the curve, the average waiting time has lessened to almost half at the end of fourth week. The approximate slope of curve for week eight can be calculated by (11.67- 3.45)/(8–0) = 1.0272 meaning average waiting time is lessened by 1.0272/11.67 = 0.088~0.09. The rate of learning is nine percent that means it is a 91% learning curve. [pic] Similar analysis can be applied to the profit gained by the improvement in process. Below is the plot for each week denoted by S.No. on X-axis and Profit on Y-axis from the table below. [pic] A large improvement in profit from week one until week eight of the simulation is shown. The improvement was from $1,054 to $2,081 at the end of the process after changing parameters of optimization strategy and eliminating the blockage in process. The analysis curve shows profit has almost doubled. The slope of cuve for week eight can be claculated by (2,081– 1,054)/(8-0)=1.28375 meaning profit is increasing by 128.375 each week. The percent increase in profit is 1.28.375/2,081=0.061~0.06 per week. So the rate of learning is six percent, which equals a 94% learning curve. Cumulative average theory can be used to optimize and estimate the cost of restaurant staff, rent, pizza cost and oven operating charge. [pic] (Source: http://fast.faa.gov/pricing/98-30c18.htm ) This equation, Y calculates the mean value of cost using utilization of X quantities of different units. This equation average costs represents the cumulative units of X operating units. So the total cost of producing X units will be calculated by X multiplied by the cumulative average cost. In the simulation, the average cost of the number of pizzas eaten can be calculated for the eight week production using the aveage costs for running the restaurant each day, showing the rough estimate for optimizing the price of pizza to maximize the profit. We have seen how service facilities and the utilization affected the waiting lines. These lines form when cutomers arrive faster than can be serviced. Maintaining lines is not about shorter or longer queues, but about a balance between demand for service and capacity of the system to provide service. As a manager, concern should be about operating characteristics such as line length, number of cutomers, time spent in line, and service facility utilization. An effective way of analyzing a line problem is to relate these characteristics to their costs. A long wait at a restaruant is enough to upset any customer. Several experiences in a row may result in a loss of cutosmers and sales. So learning curve theory is important in estimating cost of the waiters, kitchen staff, types of ovens, and table size to maximize the profit. References Baloff, N. (1971). Extensions of the learning curve some empirical results. Operations Research Quarterly, 22, 329-340. Retrieved from http://www.jstor.org/pss/3008186 Federal Aviation Administration. (n.d.). The Learning Curve. Retrieved January 31, 2010, from http://fast.faa.gov/pricing/98-30c18.htm