Pizza Store Layout Simulation - Assignment Example

Pizza Store Layout Simulation Mario pizzeria was incorporated in 1950 and since then it is being managed by Mr. Mario. It has become a renowned placefor eating pizza because of its authentic taste and fresh ingredients. As it is gaining overwhelming popularity, Mario is concerned about its lost sales and limited profitability because of baulking customers who generally hates to wait and leaves the system before being served. This exercise serves as an important tool for analyzing how to reduce waiting lines in order to balance the demand and capacity of the system so that it is efficiently utilized. The restaurant will be capable only if the waiting time falls within the tolerance limit set by the owner of the business. Mr. Mario is planning to hand over his business to his grandchild, but prior to that he wants to make sure that the business is conducted in a way such that it inspires loyalty among the customers and maximizes profitability. The common performance metrics that is being identified for the simulation is average waiting time per unit since it serves as the foundation of customer loyalty and profitability. Beside that it also uses other metrics such as utilization, average queue length and profitability which all are correlated with average waiting time in general. An analysis of the business revealed that the customer were dissatisfied by the long waiting period and many of them left before being served. The average waiting time in the restaurant between the peak hours was ranged between 7 and 9 minutes and it was uncomfortably close to the upper tolerance limit of 9 minutes. This implies that customer on average are willing to wait for a maximum of 9 minutes. As per the business procedures it was also found that the customers in group of 2 were served in the Table of 4 which was the root cause of inefficiency. Week No of Customers for Group of 2 No of Customers for Group of 4 Utilization for Group of 2 (%) Utilization for Group of 4 (%) Average Waiting Time per minute Average Queue Length Profit ($) 0 69 107 0 99.56 11.07 3.08 1,065 1-2 69 107 99.43 95.43 8.24 2.6 1,365 3-4 70 106 88.56 94.61 4.85 2.62 1,466 5-6 71 105 80.87 87.49 4.21 2.6 1,632 7-8 96 80.05 143 81.68 3.32 2.75 2,050 Table 1: Data for Mario Pizzeria over the period of 8 weeks When Mr. Mario handed over the business to me, the average waiting time was 11.07 minutes. Initially it was a company policy to serve all the customers on a table of 4. The customers usually tend to come in either group of two or 4. As a first step, it was suggested that the table of 4 should be distributed to two tables of 2 so that the group of two customers can be directed to the table of 2 while the group of four customers can be directed to a table of 4. During the first week, the 14 tables of four were reduced to12 tables of four and 4 tables of 2. This boosted the utilization capacity and reduced the waiting times per minute to 8.24 minutes which was within the tolerance limit. However, there was further room for improvement which was demonstrated when the 14 tables of four were reduced to10 tables of four and 8 tables of 2, which reduced the average waiting time to just to 4.85 per minutes at the end of the fourth week. It was a dramatic achievement since it was great initiative to engender customer loyalty and increase sales in such a short span of time. The manual ovens in the restaurant were not functioning at their best therefore after the distribution of tables; the next stage was to update the equipment and technology. The manual ovens were replaced with a conveyor oven from the Plax. It was also decided to buy the Menu point system. As the technology was updated, the mean waiting time reduced to 4.21 minutes from 4.85, which was another significant improvement for Mario Pizzeria. Mr. Mario was pleased with the performance as the sales were increasing and very few groups baulked thus stopping the lost sales which resulted in the past. Applying the Learning Curve A learning curve is a graph which shows the relationship between the average unit production time and the number of units produced. It demonstrates that how an organization learns over the period of time and generates economies of scale through specialization, experience and improvements that result from human capital. The learning curve theory is based on the three fundamental assumptions (Chase, Jacobs & Aquilano, 2006). It assumes that the amount of time required to complete a task will be lesser each time the same task is undertaken. The unit time (average time) will diminish at a decreasing rate. The reduction in time will follow a predictable pattern. Figure 1: Average Waiting Time per Minute (Learning Curve) It can be seen from the above graph which has been generated through the data from simulation that Mario Pizzeria has been able to decrease its average waiting time due to the effective decisions it made over the period of time. The decrease in average waiting time per minute has also led to an increase in profitability which is demonstrated from the figure below. We can calculate the learning curve rate by using the logarithmic equation. Yx = Kxn Where x = Unit number or time Yx = Time required to produce the xth unit K = Time required to produce the first unit n = log b/log 2 where b = Learning percentage Y5 = 11.07(5)n 3.32 = 11.07(5)n (5)n = 3.32 / 11.07 (5)n = 0.30 n(ln(5)) = ln(0.30) n = -0.748 n = log b/log 2 -0.748 = log b/log 2 log b =-0.225 b=60% This implies that Mario pizzeria has a 60% learning rate and it has been able to improve the processes of its business by 60% which was seen from reducing average waiting rates and increasing the profitability. References Chase, R. B., Jacobs, F. R., & Aquilano, N. J. (2006) Operations Management For Competitive Advantage (11th ed). New York: McGraw Hill/Irwin Read More