105) What are the realities
of forecasting that companies face?
Answer: First, forecasts are seldom perfect. Second, most forecasting techniques assume
that there is some underlying stability in the system. Finally, both product family and aggregated
forecasts are more accurate than individual product forecasts.
Diff: 2
Topic: Seven steps in the forecasting system
Objective: LO4-2
106) What are the
differences between quantitative and qualitative forecasting methods?
Answer: Quantitative methods use mathematical models
to analyze historical data. Qualitative
methods incorporate such factors as the decision maker's intuition, emotions,
personal experiences, and value systems in determining the forecast.
Diff: 2
Topic: Forecasting approaches
Objective: LO4-2
107) Identify four
quantitative forecasting methods.
Answer: The list includes naive, moving averages,
exponential smoothing, trend projection, and linear regression.
Diff: 2
Topic: Forecasting approaches
Objective: LO4-2
108) What is a time-series forecasting model?
Answer: A time-series
forecasting model is any mathematical model that uses historical values of the
quantity of interest to predict future values of that quantity.
Diff: 1
Topic: Forecasting approaches
Objective: LO4-2
109) What is the difference
between an associative model and a time-series
model?
Answer: A time-series model
uses only historical values of the quantity of interest to predict future
values of that quantity. The associative model, on the other hand,
attempts to identify underlying factors
that control the variation of the quantity of interest, predict future values
of these factors, and use these predictions in a model to predict future values
of the specific quantity of interest.
Diff: 2
Topic: Forecasting approaches
Objective: LO4-2
110) Name and discuss three
qualitative forecasting methods.
Answer: Qualitative forecasting methods
include: jury of executive opinion, where high-level managers arrive at a group estimate of demand;
sales force composite, where salespersons' estimates are aggregated; Delphi
method, where respondents provide inputs to a group of decision makers; the
group of decision makers, often experts, then make the actual forecast;
consumer market survey, where consumers are queried about their future purchase
plans.
Diff: 2
Topic: Forecasting approaches
Objective: LO4-2
111) Identify four
components of a time series. Which one of these is rarely forecast? Why is this
so?
Answer: Trend, seasonality, cycles, and random
variation. Since random variations follow no discernible pattern, they cannot be
predicted, and thus are not forecast.
Diff: 2
Topic: Time-series forecasting
Objective: LO4-3
112) Compare seasonal
effects and cyclical effects.
Answer: A cycle is longer (typically several years)
than a season (typically days, weeks, months, or quarters). A cycle has variable duration, while a season
has fixed duration and regular repetition.
Diff: 2
Topic: Time-series forecasting
Objective: LO4-5
113) Distinguish between a
moving average model and an exponential smoothing model.
Answer: Exponential smoothing is a weighted moving
average model wherein previous values are weighted in a specific manner--in particular, all previous values are weighted
with a set of weights that decline exponentially.
Diff: 2
Topic: Time-series forecasting
Objective: LO4-3
114) Describe three popular
measures of forecast accuracy.
Answer: Measures of forecast accuracy include:
(a) MAD (mean absolute deviation) is a sum of the absolute values of
individual errors divided by the number of periods of data. (b) MSE (mean
squared error) is the average of the squared differences between the forecast
and observed values. (c) MAPE
(mean absolute percent error) is independent of the magnitude of the variable
being forecast.
Diff: 2
Topic: Forecasting approaches: Measuring forecast
error
Objective: LO4-4
115) Give an example–other
than a restaurant or other food-service
firm–of an organization that experiences an hourly seasonal pattern. (That is,
each hour of the day has a pattern that tends to repeat day after day.) Explain.
Answer: Answer will vary. However, two non-food examples would be banks and movie theaters.
Diff: 2
Topic: Time-series forecasting
AACSB: Reflective Thinking
Objective: LO4-5
116) Explain the role of
regression models (time series and otherwise) in forecasting. That is, how is
trend projection able to forecast? How is regression used for causal
forecasting?
Answer: For trend projection, the independent
variable is time. The trend projection equation has a slope that is the change
in demand per period. To forecast the demand for period t, perform the
calculation a + bt. For
causal forecasting, the independent variables are predictors of the forecast
value or dependent variable. The slope
of the regression equation is the change in the Y variable per unit change in
the X variable.
Diff: 3
Topic: Time-series forecasting
Objective: LO4-6
117) Identify three
advantages of the moving average forecasting model. Identify three
disadvantages of the moving average forecasting model.
Answer: Three advantages of the model are that it
uses simple calculations, it smoothes out sudden fluctuations, and it is easy
for users to understand. The disadvantages are that the averages always stay
within past ranges, that they require extensive record keeping of past data,
and that they do not pick up on trends very well.
Diff: 2
Topic: Time-series forecasting
Objective: LO4-3
118) What does it mean to
"decompose" a time series?
Answer: To decompose a time series means to
break past data down into components of trends, seasonality, cycles, and random
blips, and to project them forward.
Diff: 1
Topic: Time-series forecasting
Objective: LO4-3
119) Distinguish a dependent
variable from an independent variable.
Answer: The independent variable is related to some
behavior in the dependent variable; the dependent variable shows the effect of
changes in the independent variable.
Diff: 2
Topic: Associative forecasting methods: Regression
and correlation
Objective: LO4-6
120) Explain, in your own
words, the meaning of the coefficient of determination.
Answer: The coefficient of determination measures the
amount (percent) of total variation in the data that is explained by the model.
Diff: 2
Topic: Associative forecasting methods: Regression
and correlation
Objective: LO4-6
121) What is a tracking
signal? Explain the connection between adaptive smoothing and tracking signals.
Answer: A tracking signal is a measure of how well
the forecast actually predicts. The larger the absolute tracking signal, the
worse the forecast is performing. Adaptive smoothing sets limits to the
tracking signal, and makes changes to its forecasting models when the tracking
signal goes beyond those limits.
Diff: 2
Topic: Monitoring and controlling forecasts
Objective: LO4-7
122) What is focus
forecasting?
Answer: It is a forecasting method that tries a
variety of computer models, and selects the one that is best for a particular
application.
Diff: 1
Topic: Monitoring and controlling forecasts
Objective: LO4-7
123) What is the key difference
between weighted moving average and simple moving average approaches to
forecasting?
Answer: Simple moving averages are useful where there
is no identifiable trend in the historical data, i.e. demand has been fairly
steady over time. If there was an
identifiable trend, weighted moving averages would provide a more accurate
forecast since higher weights would be put on the most recent data.
Diff: 2
Topic: Time series forecasting; Moving averages
Objective: LO4-3
124) Weekly sales of ten-grain bread at the local organic food market are in
the table below. Based on this data, forecast week 9 using a five-week moving average.
Week Sales
1 415
2 389
3 420
4 382
5 410
6 432
7 405
8 421
Answer: (382+410+432+405+421)/5 =
410.0
Diff: 1
Topic: Time-series forecasting
AACSB: Analytic Skills
Objective: LO4-3
125) Given the following
data, calculate the three-year moving
averages for years 4 through 10.
|
Year
|
Demand
|
|
1
|
74
|
|
2
|
90
|
|
3
|
59
|
|
4
|
91
|
|
5
|
140
|
|
6
|
98
|
|
7
|
110
|
|
8
|
123
|
|
9
|
99
|
Answer:
|
Year
|
Demand
|
3-Year Moving
Ave.
|
|
1
|
74
|
|
|
2
|
90
|
|
|
3
|
59
|
|
|
4
|
91
|
74.33
|
|
5
|
140
|
80.00
|
|
6
|
98
|
96.67
|
|
7
|
110
|
109.67
|
|
8
|
123
|
116.00
|
|
9
|
99
|
110.33
|
|
|
|
110.67
|
Diff: 2
Topic: Time-series forecasting
AACSB: Analytic Skills
Objective: LO4-3
126) What is the forecast
for May based on a weighted moving average applied to the following past demand
data and using the weights: 4, 3, 2 (largest weight is for most recent data)?
|
Nov.
|
Dec.
|
Jan.
|
Feb.
|
Mar.
|
April
|
|
37
|
36
|
40
|
42
|
47
|
43
|
Answer: 2x42 +
3x47 + 4x43 = 84+141+172 =
397; 397/9 = 44.1
Diff: 1
Topic: Time-series forecasting
AACSB: Analytic Skills
Objective: LO4-3
127) Weekly sales of copy
paper at Cubicle Suppliers are in the table below. Compute a three-period moving average and a four-period moving average for weeks 5, 6, and 7. Compute
MAD for each forecast. Which model is more accurate? Forecast week 8 with the more accurate
method.
Week Sales (cases)
1 17
2 21
3 27
4 31
5 19
6 17
7 21
Answer:
|
Week
|
Sales (cases)
|
3MA
|
|error|
|
|
4MA
|
|error|
|
|
1
|
17
|
|
|
|
|
|
|
2
|
21
|
|
|
|
|
|
|
3
|
27
|
|
|
|
|
|
|
4
|
31
|
21.7
|
9.3
|
|
|
|
|
5
|
19
|
26.3
|
7.3
|
|
24.0
|
5.0
|
|
6
|
17
|
25.7
|
8.7
|
|
24.5
|
7.5
|
|
7
|
21
|
22.3
|
1.3
|
|
23.5
|
2.5
|
|
8
|
|
19.0
|
|
|
22.0
|
|
The four-week moving average is more accurate. The forecast
with the 4-moving average is
22.0.
Diff: 2
Topic: Time-series forecasting
AACSB: Analytic Skills
Objective: LO4-4
128) The last four weekly
values of sales were 80, 100, 105, and 90 units. The last four forecasts (for
the same four weeks) were 60, 80, 95, and 75 units. Calculate MAD, MSE, and
MAPE for these four weeks.
|
Sales
|
Forecast
|
Error
|
Error squared
|
Pct. error
|
|
80
|
60
|
20
|
400
|
.25
|
|
100
|
80
|
20
|
400
|
.20
|
|
105
|
95
|
10
|
100
|
.095
|
|
90
|
75
|
15
|
225
|
.167
|
Answer: MAD =
65/4 = 16.25; MSE = 1125/4 =
281.25; MAPE = 0.712/4 = .178 or 17.8%
Diff: 2
Topic: Time series forecasting: Measuring forecast
error
AACSB: Analytic Skills
Objective: LO4-4
129) A management analyst is
using exponential smoothing to predict merchandise returns at an upscale branch
of a department store chain. Given an actual number of returns of 154 items in
the most recent period completed, a forecast of 172 items for that period, and
a smoothing constant of 0.3, what is the forecast for the next period? How
would the forecast be changed if the smoothing constant were 0.6? Explain the
difference in terms of alpha and responsiveness.
Answer: 166.6; 161.2
The larger the smoothing constant in an exponentially smoothed forecast,
the more responsive the forecast.
Diff: 1
Topic: Time-series forecasting
AACSB: Analytic Skills
Objective: LO4-3
130) The following trend
projection is used to predict quarterly demand: Y = 250 -
2.5t, where t = 1 in the
first quarter. Seasonal (quarterly) indices are Quarter 1 = 1.5; Quarter 2 =
0.8; Quarter 3 = 1.1; and
Quarter 4 = 0.6. What is the
seasonally adjusted forecast for the next four quarters?
Answer: Quarter Projection Adjusted
1 247.5 371.25
2 245 196
3 242.5 266.75
4 240 144
Diff: 2
Topic: Time-series forecasting
AACSB: Analytic Skills
Objective: LO4-5
131) Jim's department at a
local department store has tracked the sales of a product over the last ten
weeks. Forecast demand using exponential
smoothing with an alpha of 0.4, and an initial forecast of 28.0 for period
1. Calculate the MAD. What do you recommend?
|
Period
|
Demand
|
|
1
|
24
|
|
2
|
23
|
|
3
|
26
|
|
4
|
36
|
|
5
|
26
|
|
6
|
30
|
|
7
|
32
|
|
8
|
26
|
|
9
|
25
|
|
10
|
28
|
|
Answer:
Period
|
Demand
|
Forecast
|
Error
|
|
Absolute
|
|
1
|
24
|
28.00
|
|
|
|
|
2
|
23
|
26.40
|
-3.40
|
|
3.40
|
|
3
|
26
|
25.04
|
0.96
|
|
0.96
|
|
4
|
36
|
25.42
|
10.58
|
|
10.58
|
|
5
|
26
|
29.65
|
-3.65
|
|
3.65
|
|
6
|
30
|
28.19
|
1.81
|
|
1.81
|
|
7
|
32
|
28.92
|
3.08
|
|
3.08
|
|
8
|
26
|
30.15
|
-4.15
|
|
4.15
|
|
9
|
25
|
28.49
|
-3.49
|
|
3.49
|
|
10
|
28
|
27.09
|
0.91
|
|
0.91
|
|
|
|
Total
|
2.64
|
|
32.03
|
|
|
|
Average
|
0.29
|
|
3.56
|
|
|
|
|
Bias
|
|
MAD
|
The tracking signal RSFE/MAD
= 2.64/3.56 = .742 is low;
therefore, keep using the forecasting method.
Diff: 2
Topic: Time-series forecasting, and monitoring and
controlling forecasts
AACSB: Analytic Skills
Objective: LO4-4
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