Marketing & Sales Forecasting Exercise $2$

Exercise Objective:

You and your assigned team will learn how to create and compare various types of forecasting

models using the MAPE. Each team will use Microsoft Excel and ForecastX to develop your

models and identify which of the $3$ forecasting methods best fit your data.

Task: Create, Evaluate, and Compare $(1)$ simple $/$ SES, $(2)$ Holt$$s and $(3)$

Winters$$ models $/$ Holt$-$Winters$$ smoothing models

This exercise is designed to:

$1)$ Get you acclimated with creating forecasts using forecasting methods

$2)$ Provide an opportunity to compare forecasting models using the MAPE

Grading Rubric & Expectations: Total $=60$ pts

$5$pts $-$ Timeliness: Word and Excel documents uploaded to Bb before $11$:$59$pm EST on due date

$5$pts $-$ References: Minimum $1$ reference using APA citations with full reference included

$*$pts $-$ Formatting: $12$pt$.$ font, single spaced, and $1$ line header $($top right corner$)$ for your names.

Title the word document $$MKT $555$ Exercise # $-$ Team # $-$ Academic term$.$docx and title your

excel file $$MKT $555$ Exercise # Data $-$ Team # $-$ Academic term$.$xlsx

$*$pts $$ Original Content: Safe Assign existing and source content usage under $30\%$; content that

exceeds the $30\%$ mark will be investigated for plagiarism. If reviewed content is found to be

stolen all team members may receive a $0$ on the exercise.

Forecasting Process Steps listed below with unique Exercise dynamics listed:

$10$pts $-$ Steps $1-4$

Step $1$: Specify Objectives:

a$.$ Identify a clear statement of objectives for the forecast

i$.$ Exercise $2$: $1$ sentence to describe forecast statement for sales predictions

b$.$ Determine audience of the forecast

i$.$ Exercise $2$: audience is your boss who is semi$-$knowledgeable about

forecasting. $1$ paragraph addressing elements the need to know and your

approach$)$

Step $2$: Determine what to forecast:

c$.$ Determine what type of data to forecast

i$.$ Note: some exercises will be limited and the $$what$$ will be identified in the

data