Mario's Pizza Simulation Mario's Pizza Scenario- in

Mario's Pizza Simulation Mario's Pizza Scenario- In The Mario's Pizza Simulation, Mario opened a pizza restaurant in Palm-Springs, California in 1950 with his wife. The current location is in a busy indoor mall in which there are a number of business and shopper clientel. Mario has notice that over the past several months, customers have had to wait an inodinante amount of time for their orders.

This has resulted in him hearing dissatisfied comments about his service, and even customers leaving the restaurant prior to ordering food because they are so frustrated with the line, and the time. Mario wants to keep his customers happy, to grow his business, and to remain successful. He knows that if he does not make drastic changes, he will lose market share, and eventually dwindle down the entire business.

In speaking with guests, his senior staff, and looking at other restaurants, he knows the waiting time must be reduced from nine minutes to under 8, as low as possible under 8 to keep clients satisfied. According to the Pizza Store Layout simulation, Mario's needs to ensure that he has a process that will balance between demand for service, capacity for the cooks and system, and a realistic ROI based on traffic. Taking into account these three issues, Mario can more than likely turn the situation around.

Mario's Pizza and the Learning Curve Concept- The learning curve process is a very useful for a small business like Mario's. Currently, one can apply the LCP in a number of ways: who perform repetitive tasks, how much time does this take, does the time it takes to complete the task reduce its efficacy? And, if improvement decreases as more units are produced, what happens to a consistency that will allow for the organization's needs to be met? (Human Behavior Central, 2004, 32; Operations Management for Competitive Advantage, 2006).

The idea is to have more staff on daily where his customer service can be more efficient. In this simulation I compiled some process performance data, which is as follows: I had to choose whether or not to change the current serving staff, cooks, and the amount of two person and four person tables to have to provide the optimal service without cutting into the profits.

Simulation Weeks # of Tables for 2 # of Tables for 4 Average Wait Time Average Line Length Profit 1 0 70 11.67 3.21 2 1-2 71 6.46 2.56 3 3-4 71 5.53 2.67 4 5-6 4.93 2.88 1985 5 7-8 92 3.45 2.68 2081 What, we might ask ourselves, changed between the weeks to almost double profits? Several measures may be graphed to give us a scale on which to base our observations: Average wait time per customer, average wait time, and profit per customer.

Thus: 2 4 Ave Profit Total Profit Topper Topper Wait Customers Per Client 0 70 11.67 $1,054.00 $5.99 1 to 2 71 6.46 $1,120.00 $6.36 3 to 4 71 5.53 $1,380.00 $7.84 5 to 6 4.93 $1,985.00 $8.27 7 to 8 92 3.45 $2,081.00 $8.71 Between week 0 and the final simulation, weeks 7-8, we note the following: Average wait time, despite serving more customers, dropped by 8.22.