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 naive 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 naive, 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 |
??? |