TRL4861 EXAM ANSWERS JAN-FEB 2023 EXAM TRL4861 EXAM ANSWERS
DISCLAIMER
THE DOCUMENT CONTAINS EXAMINATION QUESTIONS
AND ANSWERS WITH THE APPLICATION OF THE CASE
STUDY FOR TRL4861 2023 JAN/FEB EXAMINATION. THIS
DOCUMENT IS IDEAL FOR EXAMINATION PREPARATION.
,TRL4861 EXAM ANSWERS JAN-FEB 2023 EXAM TRL4861 EXAM ANSWERS
Contents
DISCLAIMER ........................................................................................................ 1
QUESTION 1 ............................................................................................................ 3
QUESTION 2 ............................................................................................................ 7
QUESTION 3 .......................................................................................................... 13
QUESTION 4 .......................................................................................................... 18
Bibliography .......................................................................................................... 23
, TRL4861 EXAM ANSWERS JAN-FEB 2023 EXAM TRL4861 EXAM ANSWERS
QUESTION 1
Assume you are an operations manager for Luxi Airline. Use five types of demand
forecasting and five quantitative data collection methods to forecast the demand for
the airline’s services for the next two years. In your discussion, make sure you discuss
the primary distinctions as 6 well as the relationships between the two methods, and
you motivate why those methods are the most suitable for an airline.
Introduction
South Africa’s Department of Transport (DoT) envisions a safe, reliable, efficient, and
fully integrated transport system that serves the demands of both freight and
passenger customers, ensuring increased service levels. When assessing public
transport in South Africa’s urban regions, it’s necessary to analyse the existing state
of the industry, the obstacles faced, and the progress made towards this aim. The
investigation will primarily focus on public transit networks inside cities such as
Johannesburg, Cape Town, Durban, and Pretoria.0717513144
Types of Demand Forecasting Methods
Demand forecasting techniques can be classified into two primary categories:
qualitative and quantitative (Duzbaievna-Sharapiyeva, Antoni, & Yessenzhigitova,
2023). Quantitative methods are typically the most appropriate for an airline, as they
depend on historical data and statistical models, which are essential for forecasting
future demand based on previous trends and patterns.
a) Time Series Analysis
Time series analysis involves using historical data points collected at consistent
intervals over time to identify patterns such as seasonality, trends, and cycles. The
method extrapolates these patterns into the future to predict demand (Kmiecik, 2022).
Using time series analysis, Luxi Airline can predict demand based on historical flight
data, including the number of passengers, bookings, and cancellations during various
months or seasons. This is particularly useful for identifying peak travel times such as
holidays, summer vacations, or business seasons. Airlines typically experience
seasonal fluctuations in demand, making time series analysis crucial for forecasting
during high and low-demand periods (e.g., holiday travel versus off-peak seasons).
DISCLAIMER
THE DOCUMENT CONTAINS EXAMINATION QUESTIONS
AND ANSWERS WITH THE APPLICATION OF THE CASE
STUDY FOR TRL4861 2023 JAN/FEB EXAMINATION. THIS
DOCUMENT IS IDEAL FOR EXAMINATION PREPARATION.
,TRL4861 EXAM ANSWERS JAN-FEB 2023 EXAM TRL4861 EXAM ANSWERS
Contents
DISCLAIMER ........................................................................................................ 1
QUESTION 1 ............................................................................................................ 3
QUESTION 2 ............................................................................................................ 7
QUESTION 3 .......................................................................................................... 13
QUESTION 4 .......................................................................................................... 18
Bibliography .......................................................................................................... 23
, TRL4861 EXAM ANSWERS JAN-FEB 2023 EXAM TRL4861 EXAM ANSWERS
QUESTION 1
Assume you are an operations manager for Luxi Airline. Use five types of demand
forecasting and five quantitative data collection methods to forecast the demand for
the airline’s services for the next two years. In your discussion, make sure you discuss
the primary distinctions as 6 well as the relationships between the two methods, and
you motivate why those methods are the most suitable for an airline.
Introduction
South Africa’s Department of Transport (DoT) envisions a safe, reliable, efficient, and
fully integrated transport system that serves the demands of both freight and
passenger customers, ensuring increased service levels. When assessing public
transport in South Africa’s urban regions, it’s necessary to analyse the existing state
of the industry, the obstacles faced, and the progress made towards this aim. The
investigation will primarily focus on public transit networks inside cities such as
Johannesburg, Cape Town, Durban, and Pretoria.0717513144
Types of Demand Forecasting Methods
Demand forecasting techniques can be classified into two primary categories:
qualitative and quantitative (Duzbaievna-Sharapiyeva, Antoni, & Yessenzhigitova,
2023). Quantitative methods are typically the most appropriate for an airline, as they
depend on historical data and statistical models, which are essential for forecasting
future demand based on previous trends and patterns.
a) Time Series Analysis
Time series analysis involves using historical data points collected at consistent
intervals over time to identify patterns such as seasonality, trends, and cycles. The
method extrapolates these patterns into the future to predict demand (Kmiecik, 2022).
Using time series analysis, Luxi Airline can predict demand based on historical flight
data, including the number of passengers, bookings, and cancellations during various
months or seasons. This is particularly useful for identifying peak travel times such as
holidays, summer vacations, or business seasons. Airlines typically experience
seasonal fluctuations in demand, making time series analysis crucial for forecasting
during high and low-demand periods (e.g., holiday travel versus off-peak seasons).
