Session Syllabus
EMBA 512 – Assessing Business Opportunities
Session Session Session
Date
#
Time
Sat.
October
8
PM
20, 2012
Primary Instructor Name
Phil Fry
Session Title
Instructor(s)
Demand (Sales) Forecasting
Phil Fry
Phone
208.426.4276
Email
pfry@boisestate.edu
Learning Objective:
Demand forecasting is a critical input to all planning activities. In those cases where
historical demand data is available, one approach to forecasting is to project the historical
time series into the future. This module will introduce the student to the role and purpose
of forecasting time series data. The module will provide an overview of the
characteristics of forecasts, the components of time series data, basic models for time
series forecasting, and measuring and monitoring forecast accuracy. Students will use
historical data to develop and evaluate forecasts.
Advanced Preparation:
Reading(s): None!
Other:
Review Module Materials: PowerPoint slides and data sets will be available on EMBA
InfoSite approximately two weeks prior to our session.
Day of Session:
Assignment(s):
Assign.
#
8.0
Description
Forecasting
Problem, Part I
Date
Due
Time
Due
10-20-2012
In Class
Submittal Individual Grading
Method
/ Group Instructor
hard copy
Individual
Fry
Points
Assign.
Weight
50
n/a
Assignment Details:
Identify a forecasting issue in your organization. Determine whether it is best described
as a short-term, medium-term, or long-term forecasting issue.
Collect historical time series data for the forecasting issue you identify. Recall that time
series data is measured at successive points in time (e.g., yearly, quarterly, monthly,
weekly, daily, hourly, etc). Examples of time series data include monthly sales in dollars,
calls arriving at a help center each hour, and monthly expenditures for electricity. You
should collect no fewer than 24 observations. To keep the data set manageable and to
facilitate your analysis, do not collect more than 60 observations.
Use Excel to graph this data with respect to time. What patterns, if any, can you identify
in the data you graphed? Bring your data to class where you will learn how to fit a
forecasting model that can be used to predict future values of your time series.
Post Session:
Assignment(s):
Assign.
#
8.1
Description
Forecasting
Problem, Part II
Date
Due
Time
Due
10-29-2012
5:00 (PM)
Submittal Individual Grading
Method
/ Group Instructor
email
Individual
Fry
Points
Assign.
Weight
50
n/a
Assignment Details:
Divide the data you collected for Part I of the assignment into two groups. Let the first
group be, approximately, the first 80% of your data. Use the data in the first group to
develop a forecasting model for demand (sales). Use the model you developed to
forecast (predict) demand for the observations in the second group (the observations in
the second group are referred to as a holdout sample).
If the forecasting model you developed above does not accurately predict the demand
values in the holdout sample, how would you measure your forecast error? How would
measurements of forecast error be useful to you as a decision maker?