It will immediately relieve procedural congestion as well as improve operational performance to augment daily profits.
We propose certain changes in operations to assist us in increasing our profit margin. In the old layout, there were 14 tables that could seat 4 people each. In the new process, there will be 10. Furthermore, the old process had no two-seat tables, but the new process has 8 two seat tables. As with the old process, the new process will have 4 people on wait staff, but for the new process we will increase kitchen staff from 2 in the old process to 3 in the new. These changes are aimed at increasing our profit margin.
Graphical analysis of our data will help us determine whether our theory about process efficiency was not correct. It seems part of our theory was correct and part was not. As predicted, the new process immediately out-performed the old process. The data shows the new processes netted about $378 more per day than the old process on average. However, the staff learning curve does not seem to affect the data at all. Visual analysis of trends in the data does not seem to reveal a significant curve function. It appears only linear. A graphical representation of the data is presented below.
We considered an alternative process configuration for the operation of Mario’s Pizzeria. Our primary metric was our daily profit margin. Our aim was to reconfigure the factors in our process to maximize our profit potential. We designed a new operational configuration to test against the old system. The data shows the new processes netted about $378 more per day on average than the old process. Visual analysis of trends in the data does not seem to reveal a significant curve function. It would be an impractical use of man hours to apply advanced statistical techniques because the new configuration achieves our marginal