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FDA621S - FORECASTING AND DATA ANALYSIS - 2ND OPP - JAN 2023 |
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nAm I BI A u n IVE RS ITV
OF SCIEn CE Ano TECHn OLOGY
FACULTYOF COMMERCE,HUMAN SCIENCEAND EDUCATION
DEPARTMENT OF MARKETING, LOGISTICSAND SPORTS MANAGEMENT
QUALIFICATION: BACHELOR OF LOGISTICS AND SUPPLY CHAIN MANAGEMENT
QUALIFICATION CODE: 07BLSC
COURSE CODE: FDA621S
SESSION: JANUARY 2023
DURATION: 3 HOURS
LEVEL: 6
COURSE NAME: FORECASTINGAND DATA ANALYSIS
PAPER: THEORY
MARKS: 100
SECOND OPPORTUNITY EXAMINATION QUESTION PAPER
EXAMINER(S)
Ms. Emilia Salomo
Mr. Tangi Nepolo
(FT & DI)
(PT)
MODERATOR: Ms Gloria Tshoopara
INSTRUCTIONS
1. Answer ALL 4 questions in all sections
2. Read each question carefully
3. Write as legible and precise as possible
4. Indicate your class lecturer's name on your answer sheet
THIS EXAMINATION QUESTION PAPER CONSISTS OF 5 PAGES (Including this front page)
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QUESTION 1:
[20 MARKS]
Match the statements below with the best-described technique. Please do not rewrite the information-just
the statement number followed by the matching technique.
e.g.1. MAPE
NB. Eachstatement only describes ONE technique. Writing two more will cost you marks.
Statements
1. A type of forecast used for new product planning, capital expenditures,
facility location or expansion and R&D.
2. When an independent party ask individual experts questions relating to an
underlying forecasting problem to seek a consensus forecast by providing
feedback to the various experts in a manner that prevents the identification of
unique positions
3. A forecasting technique that uses advertising initiatives to determine
demand
4. A forecasting method that does not rely on rigorous mathematical
computations.
5. A sequence of data points that are measured typically at successive times
at regular time intervals is known as:
6. Using the latest observation in a sequence of data to forecast the next
period is
7. A forecast based on the previous forecast plus a percentage of the forecast
error
8. Data exhibit a steady growth or decline over time.
9. Data exhibit upward and downward swings over a very long-time frame.
10. Eliminate the problem of positive errors cancelling negative errors
Technique
Cycles
Delphi method
Executive Opinion
Exponential
forecast
MAD
smoothing
MAPE
Na"iveforecasting
Qualitative data methods
Simple Linear regressions
Strategic forecast
Time series
Trend
Weighted moving average
QUESTION 2
[10 MARKS]
The below graph represents data analysis conducted to determine any correlation between the
selling price of the house and the house sales in respective geographic locations.
Graph one shows the correlation between the house sales for houses in Klein Windhoek and the
selling price. While graph 2 shows the result for the correlation between the house sales for
dwellings located in Katutura and the selling price
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Graph 1
Graph 2
2.1 What forecasting method was used in the above scenario?
2.2 Interpret the results of each graph
[1 mark]
[4 marks]
A client comes to you and would like to know the price of their house in the same area, which has
four bedrooms, three bathrooms, a guest toilet, and no swimming pool. The size of the erf is 629
m2 • Below is the excel output.
i:ite_rcept
-·-e-·dro-·o- m
toilets
-··.
ERf si~~s-qua_i:emeter
Swimmingpool
Coefficients andardEm t Stat
302785.4823 1456554: 0.207878
3-9··3· 42·6·-.4-8---56 3-75572.8 1.047537
-96722.20708 - 365611-- -0.26455
1288.433712 -1983. 311 0.649638
362822.513 285400.6 1.271275
P-va/ue
0.843526
0.342825
0.801915
0.5-44586--
0.259559
Lower95% Upper 95%ower 95. O'Ppper95.0%
-3441406. 616 4046978 -3441407 4046978
-5-72·-014·-.1-8-17 135-8-867 -572014· 1358867
-103- 6-555.22 843-110.8 -1036555 843110.8
-3809.830648 6386.698 -3809.83 6386.698
-370822.9651 1096468 -370823 1096468
2.3 Write down the formula for the above. [1 mark]
2.4 How much will the house cost?
[4 marks]
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QUESTION 3
[40 MARKS]
Volkswagen's famous Beetle sales have grown steadily at Zimmerman's garage during the past five
years (see table below). The sales manager had predicted in 2014 that 2015 sales would be 410
VWs.
NB: Please round your answers to two decimal places.
Year
2015
2016
2017
2018
2019
2020
Sales
450
495
518
563
584
?
2.1 Forecast above data using;
a) Exponential Smoothing with a=0.30.
b) 3 months moving average
[8 marks]
[6 marks]
2.2 Compute and interpret below for both exponential smoothing and 3-month moving average:
a) MAD
b) MSE
c) MAPE
d) Tracking Signal
e) Which forecasting method will you recommend and why?
[6 marks]
[6 marks]
[6 marks]
[6 marks]
[2 marks]
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QUESTION 4
[30 MARKS]
Mr Shilongo has been running a small retail outlet in the northern town of Tsumeb, selling Fast
Moving Consumers Goods (FMCGs), his business has experienced rapid growth over the years, and
inventory management has been a growing concern. He has since decided to offer students
internships as demand planners; you are one of the lucky students. You have suggested demand
forecasting as a solution to managing the inventory. However, Mr Shilongo has no clue where to
start but is keen on the idea.
(a) Explain to Mr Shilongo the importance of Demand forecasting to his business. [6 marks]
(b) The practical examples help Mr Shilongo draft a detailed systematic forecasting approach
explaining the various steps involved in forecasting.
[20 marks]
(c) What forecasting method/sis/are appropriate for Mr Shilongo's s business? Justify your answers
[4 marks]
GRAND TOTAL: 100 MARKS
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