C’mon Back Restaurant in Puerto Rico has been in operation for three years and has been a popular destination for tourists and locals in the country. To better forecast future sales and plan for future growth proprietor Theotis Jones has provided the following sales data analysis and develop a forecasting model.
C’mon Back Restaurant has seen a 6% growth year over year in the first three years of operation. This is see in the adjacent chart of yearly sales per year. Looking over the data we can see that there is a seasonal pattern that occurs each month in the year along with the sales growth each year. This seasonal pattern can be viewed easily when displayed on the following time series plot.
The pattern in the time series plot …show more content…
For each month the standard error indicated by the model is 3.6%. This means that actual sales numbers should be within 3.6% of this this forecasting model. January, for example, could be within 3.6% of the values projected in our forecast. Assuming sales were $ 295 thousand in January of year four, this would be an increase from the projected sales by $8000 dollars. This increase would equate to a 2.7% change from the forecast and within the 3.6% standard error percentage.
The data in this forecast model is built the correlation between the seasonal and linear yearly growth of sales for C’mon Back Restaurant. This model assumes no other independent variable which could impact sales. As Puerto Rico is a Caribbean nation, it is subject to severe weather patterns, hurricanes. The impact of a hurricane would drastically affect the results in this model. With no data on this it has not been used in this report.
C’mon Back Restaurant has seen success over its first three years in business. Using this three-year data, we have built a forecasting model that should provide C’mon back with accurate future earning to be used in management of the restaurants future growth and
C’mon Back Restaurant has seen a 6% growth year over year in the first three years of operation. This is see in the adjacent chart of yearly sales per year. Looking over the data we can see that there is a seasonal pattern that occurs each month in the year along with the sales growth each year. This seasonal pattern can be viewed easily when displayed on the following time series plot.
The pattern in the time series plot …show more content…
For each month the standard error indicated by the model is 3.6%. This means that actual sales numbers should be within 3.6% of this this forecasting model. January, for example, could be within 3.6% of the values projected in our forecast. Assuming sales were $ 295 thousand in January of year four, this would be an increase from the projected sales by $8000 dollars. This increase would equate to a 2.7% change from the forecast and within the 3.6% standard error percentage.
The data in this forecast model is built the correlation between the seasonal and linear yearly growth of sales for C’mon Back Restaurant. This model assumes no other independent variable which could impact sales. As Puerto Rico is a Caribbean nation, it is subject to severe weather patterns, hurricanes. The impact of a hurricane would drastically affect the results in this model. With no data on this it has not been used in this report.
C’mon Back Restaurant has seen success over its first three years in business. Using this three-year data, we have built a forecasting model that should provide C’mon back with accurate future earning to be used in management of the restaurants future growth and