Recommendations to Big D Inc. Board on Forecasting Monthly Sales

This report provides recommendations to the Big D Incorporated Board of Directors regarding the forecasting of monthly sales. It identifies key variables impacting sales volume, including price, demand, duration of service, and availability of substitutes. The report suggests using multiple linear regression modeling for forecasting, expressed as: Monthly sale= B1 (price) + B2 (demand) + B3 (duration) + B4 (Substitute). The justification for this method lies in the availability of data and the multiple factors influencing sales. The report references statistical forecasting and regression models to support its recommendations.

Running Head: RECOMMENDATIONS 1
Recommendations to the Big D Incorporated Board of Directors
Institution Name:
Student Name:

Contents
Recommended Variables.................................................................................................................3
Forecast of Monthly Sales...............................................................................................................4
Recommendations............................................................................................................................4
Justifications....................................................................................................................................5
References........................................................................................................................................6

Recommended Variables
A variable is a quantity or quality or factor that has an in effect on a given factor. In our
scenario, variables are the factors that have effect or influence on the volume of sales of the
outdoor sporting goods. There are a number of such factors that have an effect on the volume of
monthly sales that we identified in the previous studies (Szcygiel & Ubysz, 2009). Some of the
variables that we identified include the price of an outdoor sporting goods, the volume of sale of
an outdoor sporting goods, the duration of service of an outdoor sporting goods, the demand of
the outdoor sporting good and availability of a substitute of the outdoor sporting goods (Lyapina
& Lyapin, 2011).
Sales volume of outdoor sporting goods is measured in terms of the number of outdoor
sporting goods that have been sold. Sales volume refers to the total number of outdoor sporting
goods that are sold say in a particular period or duration of time (Lyapina & Lyapin, 2011).
Similarly, sales volume of outdoor sporting goods is measured in terms of the money received
from the sales during a particular period of time (Szcygiel & Ubysz, 2009).
The price of outdoor sporting goods is measured in terms of the amount of money it cost
(Sara, 2012). The duration of service of outdoor sporting goods is measured in terms of the
duration of time that the goods take in their lifetime (Kornikov, Pepelyshev, & Zhigljavsky,
2014).

Forecast of Monthly Sales
Forecasting a monthly sale is the method of predicting the expected volume of sale. The
prediction of the expected volume of sales is done through a number of methods. Some of the
commonly known and widely used methods of predicting an expected outcome based on
historical data include the methods of moving averages, generalized linear models and the
methods of multiple regression models (Kornikov, Pepelyshev, & Zhigljavsky, 2014).
Moving averages method is a way of predicting the future volume of sales based on the
segmentation of the historical data into groups such as quarterly data. The generalized linear
model, on the other hand, is a method of fitting a set of independent variables to predict the
dependent variables. Multiple linear regression model, on the other hand, is a method of
forecasting a dependent variable using a set of independent variables by determining a linear
relationship between the dependent and independent variables (Kornikov, Pepelyshev, &
Zhigljavsky, 2014).
Recommendations
The best method of predicting monthly sales of the outdoor sporting goods is by the use
of a multiple linear regression modeling. The forecast by use of regression modeling method is
determined by;
Monthly sale= B1 (price) + B2 (demand) + B3 (duration) + B4 (Substitute).
B1, B2, B3, and B4 are constants representing the regression coefficient between the
volume of sales and the individual variables. A regression coefficient is a measure of the level of
association between one variable and another (Kornikov, Pepelyshev, & Zhigljavsky, 2014).

Justifications
The use of multiple regression modeling is appropriate for forecasting the volume of
monthly sales in our scenario because the variables affecting the volume of sales are more than
one. Moreover, the data of the variables for determining the monthly sales are available and we
can use them for prediction (Kornikov, Pepelyshev, & Zhigljavsky, 2014).
References
Kornikov, V., Pepelyshev, A., & Zhigljavsky, A. (2014). Statistical Forecasting of Earth
Temperature Records. 7.
Lyapina, K., & Lyapin, D. (2011). Ukrainian Tax code norms influence forecast on the
development of the small enterprise. 03.

Sara, J. T. (2012). Linear Layer and Generalized Regression Computational Intelligence Models
for Predicting Shelf Life of Processed Cheese. Russian Journal of Agricultural and
Socio-Economic, 5.
Szcygiel, R., & Ubysz, B. (2009). Regression models permitting forecasting the flammable
material humidity depending on meteorological parameters. 37.