Running head: DEMAND FORECASTING
Demand Forecasting
Afnan J AL Mutiri
Dr. Victoria Figiel
MGT-530 - 13738
Oct, 5, 2019
1
DEMAND FORECASTING
2
The task ahead involves conducting a forecast on the demand levels for Highline financial
services for the next four quarters. The company offers various financial services A, B, and C form
the core products that Highline has. The three are the primary sources of income for the company.
One of the Managing Partners, Freddie Mack, has sort for business forecasting services concerning
the three products.
The forecast will assist Mr. Mack in making critical decisions and plans for the future.
There have been periods of uncertainty in the financial markets; thus, Mr. Mack is uncertain of the
future (Mishra, 2018). Based on the forecast given, the company will take several actions to help
the company withstand the storm of anxiety about the future. This forecast shall, therefore, form a
basis for Mr. Mack to make decisions, and shall be subject to thorough scrutiny by the company's
top management (Bower, 2018). The forecast shall also influence other factors such as business
strategies, rebranding of products, increasing shift of labor, technology adoption, and even a costprofit ratio. Highline does not have stock inventory since it offers real-time services; thus, the
demand estimates should be as accurate as possible.
Forecasting the Three Products
The data given about these products is not enough to make seasonal relations, but then it is
possible to make reasonably excellent and intuitive estimates for the next four quarters (Bower,
2018). Therefore, the demand data from the last two years were tabulated in an excel sheet and a
graphical representation of the trend drawn. Using the graphical representation, it is easy to
intuitively and naively make out a particular trend followed by the demand quantities (Rivera-
DEMAND FORECASTING
3
Castro et al., 2019). The best approach to use while estimating the demand for products A, B and
C are moving average technique forecasting.
Product
Quarters
A
B
C
1
60
95
93
2
45
85
90
3
100
92
110
4
75
65
90
5
72
85
102
6
51
75
75
7
112
85
110
8
85
60
100
9
82.6667
70
95
10
93.2222
68.3333
101.667
11
86.963
62.7778
98.8889
12
87.6173
67.037
98.5185
The figures above represent the forecasted demands for financial products A, B, and C.
The data presented above indicates that with the maximum likelihood, the following demands will
be witnessed for the next four quarters. The output suggests that for the products A, B, and C, the
company will most likely experience the following demands for financial products A, B, and C
during the next four quarters, respectively:
DEMAND FORECASTING
4
Product A
Quarter
Forecasted moving average demand
9
82.67
10
93.22
11
86.96
12
87.62
(Rounded –off to the nearest two decimal places)
Product B
Quarter
Forecasted moving average demand
9
70.00
10
68.33
11
62.78
12
67.03
(Rounded –off to the nearest two decimal places)
Product C
Quarter
Forecasted moving average demand
9
95.00
10
101.66
DEMAND FORECASTING
5
11
98.89
12
98.52
(Rounded –off to the nearest two decimal places)
Why I Chose Moving Average Method
Moving average is advantageous since it can be used to measure the trend of any series of
data, whether linear or non-linear(Rivera-Castro et al., 2019). The method gives room for
smoothing that chances of varying datasets are significantly decreased. The data provided was not
enough to form a seasonal relationship of the data, thus to make an intuitive forecast, the moving
average is the choice to make. Using moving average at periods of three quarters, data was easily
examined; thus, a trend, flow, and cycle followed by the data were established.
Additionally, the moving average is also easy in both the application and interpretation of
results. Therefore, every person, including Mr. Mack, can understand the outcomes of the forecast
without a problem. Moving average also makes use of the immediate last data sets (Quarters 6 to
8). Therefore, there is a high probability that the forecasted values will be almost close to actual
values(Rivera-Castro et al., 2019). The values established from the forecast are just assumptions,
and therefore the actual values may either exceed or go less. The forecast values will, however,
explain the trend followed by future values.
Disadvantages of Using Moving Average
Moving average is majorly used for a data set with many data values to enhance accuracy.
However, the data values given above were not enough to make a more accurate forecast(RiveraCastro et al., 2019). This, however, does not negate the intuition involved in the forecast above.
Another weakness is the assumption that all the data values have equal effort in affecting and
DEMAND FORECASTING
6
altering the future values. Ideally, it is the immediate last values that affect the forecasted value
the most.
Considerations while Using Moving Average
Precision is of utmost attention while using a moving average. The averaging formula
should
be
accurately
tabulated
and
then
subjected
to
the
same
conditions
throughout(Bicer&Lücker, 2018). Forecast values from moving average techniques are always
precise in representing the nature of the actual value. Moving average is also favorable in demand
forecasting since it is easy to use and interpret results. The simplicity also makes it convenient to
use for any data series, whether linear or not. Lastly, moving average allows for smooth, thus
possible, to eliminate data values that have some variations(Rivera-Castro et al., 2019). The line
graphs of products A, B, and C is consistent hence possible to locate a particular trend, as shown
below:
Product A
120.00
100.00
80.00
60.00
A
40.00
20.00
0.00
Q1Y Q2Y Q3Y Q4Y Q1Y Q2Y Q3Y Q4Y Q1Y Q2Y Q3Y Q3Y
1
1
1
1
2
2
2
2
3
3
3
4
A 60.0 45.0 100. 75.0 72.0 51.0 112. 85.0 82.6 93.2 86.9 87.6
DEMAND FORECASTING
7
Product B
100.00
80.00
60.00
40.00
B
20.00
0.00
Q1Y1 Q2Y1 Q3Y1 Q4Y1 Q1Y2 Q2Y2 Q3Y2 Q4Y2 Q1Y3 Q2Y3 Q3Y3 Q3Y4
B 95.00 85.00 92.00 65.00 85.00 75.00 85.00 60.00 70.00 68.33 62.78 67.04
Product C
120.00
100.00
80.00
60.00
C
40.00
20.00
0.00
Q1Y1 Q2Y1 Q3Y1 Q4Y1 Q1Y2 Q2Y2 Q3Y2 Q4Y2 Q1Y3 Q2Y3 Q3Y3 Q3Y4
C 93.00 90.00 110.0 90.00 102.0 75.00 110.0 100.0 95.00 101.6 98.89 98.52
Overall Results and Recommendations
The values from the moving average formula show the eventual outcome of demand for
the next four quarters. The values no doubt primarily stand within the expected needs. The graph
DEMAND FORECASTING
8
below represents the expected trend for the demands of products A, B, and C.
120.00
Graph showing forecasted demands
100.00
80.00
60.00
Product A
40.00
Product B
20.00
0.00
Product C
First Quarter
Second Quarter
Third Quarter
Fourth Quarter
Product A
82.67
93.22
86.96
87.62
Product B
70.00
68.33
62.78
67.04
Product C
95.00
101.67
98.89
98.52
The use of the same methods of forecasting for all three products ensures consistency of
the forecasts. The process of interpreting the likely results of the forecast should be as simplified
and straight forward (Rivera-Castro et al., 2019). Employing different approaches for the methods
may lead to inconsistent outcomes. It is also practical to use the same forecasting technique to deal
with the same products. Catering to different forecasting methods for each product can be costly
for the company in terms of time, energy, and resources. It is thus advisable to use a single
forecasting technique for the three products.
Benefits of Formalized Approach in Forecasting
Accuracy and precision should be significantly considered when dealing with business
forecasting. It is impossible to achieve accuracy using pure guessing or naïve prediction, thus the
need for using formalized statistical approaches to make accurate forecasts(Bower, 2018).
Companies rely on various methods while making both quantitative and qualitative forecasts on
multiple factors, such as demand, price, and revenues (Bicer&Lücker, 2018). Thus, relying solely
on intuition and guesses without using formalized techniques would result in erroneous judgments.
DEMAND FORECASTING
9
Formalized forecasting methods are also simple to use and interpret results(Bicer&Lücker,
2018). The only requirement is for the correct formula to be applied. For instance, using MS Excel
requires the input of the formula only once, and then the formula can be used for as long as needed
(Bower, 2018). It is also possible to store the data for future references and referrals. A formalized
approach also puts into consideration all possible outcomes before arriving at a final result. Lastly,
it is economical to use formalized procedures since it saves on time, energy, and money used in
forecasting.
References
DEMAND FORECASTING
10
Bicer, I., &Lücker, F. (2018).Inventory Dispersion in a Sequential Inventory System with Demand
Forecast Evolution. Available at SSRN 3275499.
Bower, P. (2018).S&OP, Demand Control, and Quick Response Forecasting. Journal of Business
Forecasting, 37(2).
Mishra, S. (2018).Financial management and forecasting using business intelligence and big data
analytics tools. International Journal of Financial Engineering, 5(02), 1850011.
Rivera-Castro, R., Nazarov, I., Xiang, Y., Pletneev, A., Maksimov, I., &Burnaev, E. (2019, July).
Demand forecasting techniques for build-to-order lean manufacturing supply chains.
In International Symposium on Neural Networks (pp. 213-222).Springer, Cham. Retrieved
from https://link.springer.com/chapter/10.1007/978-3-030-22796-8_23
CASE
HIGHLINE FINANCIAL SERVICES, LTD.
or promotion, and competition doesn't change, predict demand
for the services the company offers for the next four quarters.
Note that there are not enough data to develop seasonal rela-
tives. Nonetheless, you should be able to make reasonably good,
approximate intuitive estimates of demand. What general obser-
vations can you make regarding demand? Should Freddie have
any concerns? Explain.
B
Highline Financial Services provides three categories of service to
its clients. Managing partner Freddie Mack is getting ready to pre-
pare financial and personnel hiring (or layoff) plans for the coming
year. He is a bit perplexed by the following printout he obtained
which seems to show oscillating demand for the three categories
of services over the past eight quarters:
Service
Year Quarter А
с
1 60 95 93
2
90
3 100 92 110
75 65 90
Examine the demand that this company has experienced
for the three categories of service it offers over the preceding
two years. Assuming nothing changes in terms of advertising
1
Year
A
C
45
85
2
Quarter
1
2
72
51
Service
B
85
75
85
50
102
75
110
3
112
85
4
100
SELECTED
BIBLIOGRAPHY AND
FURTHER READINGS
Acar, Yavuc, and Everette S. Gardner, Jr. "Forecasting Hanke, John, and Dean Wichern. Business Forecast-
Method Selection in a Global Supply Chain." Inter ing. 9th ed. Upper Saddle River, NJ: Pearson, 2009.
national Journal of Forecasting 28, no. 4 (October- Hopp, Wallace J., and Mark 1. Spearman. Factory
December 2012), 842-48.
Physics, 3rd ed. New York: McGraw-Hill, 2008.
Bonomo, Charles. “Forecasting from the Center of the Wilson, J. Holton, Barry Keating, and John Galt Solu-
Supply Chain." Journal of Business Forecasting tions. Business Forecasting with ForecasiX, 6th ed.
Methods and Systems 22, no. 1 (Spring 2003), p. 3. New York: McGraw-Hill, 2009.
Byrne, Robert F. "Forecasting Performance for North
American Consumer Products." Journal of Business
Forecasting 31, no. 3 (Fall 2012), p. 12.
Purchase answer to see full
attachment