Highline Case Study
Define the Problem: Highline Financial Services, Ltd. offers three diverse services to its clientele. We are given the demand for the three services over a two year period - broken down into four quarters each - totaling eight quarters of data. Given this data, we are asked to forecast the following four quarters for year three and explain the observations made. However, we must assume that Highline Financial Services, Ltd. has not made any changes in advertising, promotion or competition. For this problem we look to try and gather an estimate of what the best forecasting method will be for the demand of services A, B, and C. The methods of analysis used to attain the figures include; linear regression, regression model, and forecast error analysis.
Plan the Treatment: In order to apply all of the demand forecasting methods properly and acquire the most accurate demand forecast, we must do the following…
Graph historical demand – define the key data elements.
Define the time horizon – forecast must include a time interval.
Clean the Historical data – there usually exist problems with the quality and completeness of data, “clean” (remove) all data that is found unnecessary to the forecast.
Select a forecasting technique or multiple forecasting methods.
Make the forecast.
Execute: In order to make the best forecast possible, it is imperative to understand the past demand in order to better see what type of data and trends to look for. Linear Regression attempts to model a relationship between two variables; the dependent variable (y) and an explanatory variable (x). Linear Regression allows for a visual look into the linear trend of a forecast. It formulates the best-fitting straight line for the plotted data. Linear Regression allows for a visual view in determining whether the trend is ascending (positive) or descending (negative). Figure 1.1 (Service A), Figure 1.2 (Service B), and Figure 1.3 (Service C) are shown below.
Figure 1.1
Plan the Treatment: In order to apply all of the demand forecasting methods properly and acquire the most accurate demand forecast, we must do the following…
Graph historical demand – define the key data elements.
Define the time horizon – forecast must include a time interval.
Clean the Historical data – there usually exist problems with the quality and completeness of data, “clean” (remove) all data that is found unnecessary to the forecast.
Select a forecasting technique or multiple forecasting methods.
Make the forecast.
Execute: In order to make the best forecast possible, it is imperative to understand the past demand in order to better see what type of data and trends to look for. Linear Regression attempts to model a relationship between two variables; the dependent variable (y) and an explanatory variable (x). Linear Regression allows for a visual look into the linear trend of a forecast. It formulates the best-fitting straight line for the plotted data. Linear Regression allows for a visual view in determining whether the trend is ascending (positive) or descending (negative). Figure 1.1 (Service A), Figure 1.2 (Service B), and Figure 1.3 (Service C) are shown below.
Figure 1.1