Expert answer:Calculate the forecasts for periods 7 through 11 u
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homework_4.docx
hw4_problem_3.xlsx
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SCHM 2301
Homework 4
Print out a copy and hand it in at the beginning of class.
Staple all of the sheets together – unstapled sheets is a five point deduction.
Name
__________________________
Section
__________________________
100 points.
1. (20 points) Given the series of demand data below
Period:
1
2
3
4
5
6
7
8
9
10
Demand:
40
33
56
43
23
45
38
40
29
40
a. Calculate the forecasts for periods 7 through 11 using moving average models with n=2, n=4, and n=6.
b. Calculate the Bias and MAD for each set of forecasts. Which moving average model is best?
2. (20 points) Using α= 0.5 and the data below, compute exponential smoothing forecasts for periods 2
through 8.
Period:
Forecast:
Actual demand:
1
10
12
2
3
4
5
6
7
15
11
13
11
11
10
3. (20 points) Download the spreadsheet “HW4 Problem 3.” The “Data” sheet has demand in millions
of units for the past four years for your company in column C. In column D I have constructed a
variable to capture the trend in the data, columns E through O have indicator variables for each month
(January is the excluded month), and column P indicates when a new plant was opened. Use this data
to answer the following questions.
a. Does there appear to be seasonality in demand? Provide evidence to support your claim.
b. Is the trend positive or negative? Give an estimate of trend, including time period (e.g., x units
per time period).
c. Use multiple linear regression to predict demand by month for 2017. Include your predictions in
a table below.
d. Did demand increase when you opened a plant? By how much on average? Was it significant?
Provide evidence to support your claim.
e. Why might demand increase when you open a new plant?
4. (20 points) Using the same spreadsheet, create a chart with historical demand and your forecasted
demand as different data series. The chart should look similar to the one below (but it is okay if your
actual forecasted numbers are not exactly the same as the numbers displayed in the example chart).
Turn in your spreadsheet via Blackboard. Grading will be as follows: almost exactly the same – 20
points; major elements – 15 points; some attempt – 10 points; no Excel spreadsheet turned in – 0
points. Note that you must use Excel for this exercise.
Historical Demand and Forecast for Next Year
1,400
Demand
Forecast
1,200
Millions of Units
1,000
800
600
400
200
–
10
20
30
Month
40
50
60
5. (20 points) Read “Demand Forecasting” by BCG. Answer these questions.
Q1: Why do companies have trouble meeting service expectations despite high inventory levels?
Q2: How do sales managers and supply chain managers have competing interests when it comes to
forecasting?
Q3: List four benefits of better forecasting.
Q4: What variables did BCG consider in their example of forecasting for a consumer products company
in North America?
Q5: How did forecasting accuracy change? What were some other benefits of improved forecasting?
Year
2013
2013
2013
2013
2013
2013
2013
2013
2013
2013
2013
2013
2014
2014
2014
2014
2014
2014
2014
2014
2014
2014
2014
2014
2015
2015
2015
2015
2015
2015
2015
2015
2015
2015
2015
2015
2016
2016
2016
2016
2016
2016
2016
2016
2016
2016
Month
1
2
3
4
5
6
7
8
9
10
11
12
1
2
3
4
5
6
7
8
9
10
11
12
1
2
3
4
5
6
7
8
9
10
11
12
1
2
3
4
5
6
7
8
9
10
Demand
296
295
337
432
507
488
518
485
312
290
268
270
338
301
371
432
561
576
570
562
448
412
339
373
457
479
580
640
751
793
777
709
649
601
496
557
655
647
798
865
971
1,119
1,130
1,086
963
819
Trend
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
34
35
36
37
38
39
40
41
42
43
44
45
46
February
0
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
March
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
April
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
2016
2016
11
12
673
681
47
48
0
0
0
0
0
0
May
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
June
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
July
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
August
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
September
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
0
October
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
November
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
December
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
New Plant
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
0
1
1
1
…
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