MGT 3332 The University of Texas at Dallas Forecasting Case Analysis

MGT3332 Forecasting Dataset
Spring 2022
Month
Year
Period
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
January
February
2019
2019
2019
2019
2019
2019
2019
2020
2020
2020
2020
2020
2020
2020
2020
2020
2020
2020
2020
2021
2021
2021
2021
2021
2021
2021
2021
2021
2021
2021
2021
2022
2022
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
March
April
May
2022
2022
2022
34
35
36
Price
6.11
5.75
5.71
5.73
5.61
5.62
5.61
6.34
6.42
6.21
6.31
5.91
5.73
5.75
5.77
5.82
5.71
5.81
5.72
5.85
5.92
5.75
5.71
5.55
5.61
5.65
5.72
5.75
5.83
5.37
5.42
5.71
5.92
AIP
5.8
6.01
6.32
5.71
5.85
5.81
5.75
5.85
5.65
6.02
6.13
6.03
6.11
6.22
6.13
6.15
6.22
6.29
6.11
5.76
5.78
5.64
5.93
5.66
6.12
6.24
5.65
5.78
5.85
6.25
6.31
6.43
6.51
Diff
Adv
-0.31
0.26
0.61
-0.02
0.24
0.19
0.14
-0.49
-0.77
-0.19
-0.18
0.12
0.38
0.47
0.36
0.33
0.51
0.48
0.39
-0.09
-0.14
-0.11
0.22
0.11
0.51
0.59
-0.07
0.03
0.02
0.88
0.89
0.72
0.59
5.77
6.21
7.41
7.95
7.63
7.08
7.35
7.08
6.32
5.99
7.08
6.97
7.63
7.52
7.41
7.41
7.73
7.63
7.41
7.08
7.30
8.17
7.95
8.17
8.82
9.04
9.48
9.59
9.91
10.35
10.57
10.79
11.22
Demand
31.68
33.66
36.52
35.42
35.21
34.11
33.44
30.58
29.26
28.63
30.36
32.56
33.66
35.86
38.72
38.28
37.62
36.96
36.32
35.25
33.44
33.21
34.98
35.64
38.49
40.48
44.01
42.02
40.26
39.66
38.55
37.84
37.41
Forecasting Case Analysis
ENTERPRISE INDUSTRIES
Enterprise Industries produces Fresh, a brand of liquid detergent. In order to more effectively manage its inventory,
the company would like to better predict demand for Fresh. To develop a prediction model, the company has gathered
data concerning demand for Fresh over the last 33 sales periods. Each sales period is defined as one month. The
variables are as follows:
a) Make time series scatter plots of all five variables (five separate graphs). Insert trend line, equation, and R-squared.
Observe graphs and provide interpretation of results.
b) Construct scatter plots of Demand vs. Diff, Demand vs. Adv, Demand vs. AIP, and Demand vs. Price. Insert fitted
line, equation, and R-squared. Observe graphs and provide interpretation. Note that Demand is always on the Y axis.
c) Obtain the correlation matrix for all six variables and list the variables that have strong correlation with Demand.
High correlation is r > 0.50. Explain your findings in plain language.
d) Use 3-month and 6-month moving averages to predict the demand for October 2021. Find MAD for both forecasts
and identify the preferred one based on each calculation. Is the moving average suitable method for forecasting for
this data set? Explain your reasoning.
e) Use Exponential smoothing forecasts with alpha of 0.1, 0.2, …, 0.9 to predict March 2022 demand. Identify the value
of alpha that results in the lowest MAD.
f) Find the monthly seasonal indices for the demand values using Simple Average (SA) method. Find the
de-seasonalized demand values by dividing monthly demand by corresponding seasonal indices.
g) Use regression to perform trend analysis on the de-seasonalized demand values. Is trend analysis suitable for this
data? Find MAD and explain the Excel Regression output (trend equation, r, r-squared, goodness of model).
h) Find the seasonally adjusted trend forecasts for March through May 2022.
i)
Perform simple linear regression analysis with ADV as the independent variable. Write the complete equation, find
MAD and explain the Excel Regression output. Make sure to use the de-seasonalized demand data for this model and
all future models.
j)
Repeat part (i) with Diff as the independent variable.
k) Construct multiple linear regression model with Period, AIP, Diff, and Adv as independent variables. Formulate the
equation, find MAD, and explain the output. Rank variables based on their degree of contribution to the model.
Observe significant F, R-squared, and p-values and explain.
l)
Perform multiple linear regression analysis with Period, Diff, and Adv as independent variables. Formulate the
equation and find MAD. Which variable is the most significant predictor of demand? Rank the independent variables
based on their degree of contribution to the model. Observe significant F, R-squared, and p-values and explain.
m) Use the model in part (l) and make forecasts for the following months. Make sure to seasonalize final forecasts.
n) Provide analytical conclusion for the case based on above analysis.

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MGT 3332 The University of Texas at Dallas Forecasting Case Analysis

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