OMGT4743
- HW1
Reading Assignment
I will be assigning two unique problems to the class. A
reading
assignment and a Excel based problem. Each person will read
the
article and then prepare a one to two
paragraph
synopsis including purpose and main points.
The article entitled "7 Deadly Sins of Sales Forecasting" can be
found
here: 7DeadlySinsWhitepaper.pdf
Post your synopsis via
the Canvas
Discussions area. This part of the assignment is separate
from the Excel based problem that is discussed next.
Forecasting Philosophies - Excel Based Problem
Clem has been given the following historical data for demand from
2007
to 2019 (refer to Table 1.1). In the past, the company has used a
naïve
forecasting model. Her boss wants her to forecast demand for
2020
using a 3-year moving average. However, she believes a 5-year
moving
average may be more accurate. Help Clem develop a naïve, a
3-year
moving average, and a 5-year moving average for demand and decide
which
forecasting model is more accurate (Hint: employing an error
measurement such as MAD would be advisable).
Once you have finished your analysis you are required to write up a
one-page executive summary of the work. I will speak to
writing
executive summaries more (see link below) but the document should be
1-page,
single spaced, at least 10 pt font, and contain an intro statement,
a
problem statement/purpose, an analysis section, and a
conclusion/recommendation section. You need to include
specific
numbers in your analysis and your recommendations (e.g., the
forecast
for 2020 will be XXX or the models are performing well since MAD are
X,
Y, Z). More info on writing Executive Summaries can be found
by
clicking this link Writing
Guidelines.
Post your synopsis via
the
Canvas Assignments area remembering to name your file(e) following
the
naming convention set down in the syllabus.
Table 1.1
| Year |
Demand |
Year |
Demand |
| 2007 |
30,000 |
2014 |
28,000 |
| 2008 |
28,000 |
2015 |
29,850 |
| 2009 |
32,000 |
2016 |
23,400 |
| 2010 |
23,000 |
2017 |
29,750 |
| 2011 |
34,000 |
2018 |
27,500 |
| 2012 |
24,000 |
2019 |
32,000 |
| 2013 |
31,400 |
2020 |
??? |