TRL4861 Assignment 2
(COMPLETE ANSWERS) 2025
- DUE September 2025
FOR FURTHER ASSISTANCE PLEASE
CONTACT
, Demand and Quantitative Forecasting for a
Road Freight Business in Rural KwaZulu-
Natal
1. IntroductioN
Demand forecasting is a crucial step in the establishment of any business, particularly for
industries that rely heavily on logistics and transport such as road freight. In this case, a business
specialising in the transportation of building materials to rural areas in KwaZulu-Natal (KZN)
must understand the likely demand patterns for materials such as cement, bricks, timber, roofing
sheets, and sand. Rural development, infrastructure projects, and housing initiatives are
constantly changing, and accurate forecasting allows the freight business to allocate trucks,
drivers, and resources optimally while minimising costs.
Two broad categories of forecasting techniques exist: demand forecasting methods, which are
often qualitative and focus on understanding customer needs, market trends, and environmental
factors, and quantitative forecasting methods, which rely on numerical data, statistical
analysis, and mathematical models to predict future demand. This essay will:
1. Explain the fundamental differences between demand forecasting methods and
quantitative forecasting methods.
2. Analyse the relationship between these two approaches.
3. Apply five demand forecasting methods and five quantitative forecasting methods to the
road freight business scenario.
4. Identify the most suitable methods and justify why they are most appropriate in this
context.
2. Differences Between Demand Forecasting and
Quantitative Forecasting Methods
Although both aim to predict future demand, demand forecasting methods and quantitative
forecasting methods differ fundamentally in their approach, data sources, and assumptions.
1. Nature of Data
o Demand forecasting often uses qualitative data, such as expert opinion, customer
surveys, or market intelligence.
o Quantitative forecasting is based on historical numerical data, using
mathematical and statistical models.
(COMPLETE ANSWERS) 2025
- DUE September 2025
FOR FURTHER ASSISTANCE PLEASE
CONTACT
, Demand and Quantitative Forecasting for a
Road Freight Business in Rural KwaZulu-
Natal
1. IntroductioN
Demand forecasting is a crucial step in the establishment of any business, particularly for
industries that rely heavily on logistics and transport such as road freight. In this case, a business
specialising in the transportation of building materials to rural areas in KwaZulu-Natal (KZN)
must understand the likely demand patterns for materials such as cement, bricks, timber, roofing
sheets, and sand. Rural development, infrastructure projects, and housing initiatives are
constantly changing, and accurate forecasting allows the freight business to allocate trucks,
drivers, and resources optimally while minimising costs.
Two broad categories of forecasting techniques exist: demand forecasting methods, which are
often qualitative and focus on understanding customer needs, market trends, and environmental
factors, and quantitative forecasting methods, which rely on numerical data, statistical
analysis, and mathematical models to predict future demand. This essay will:
1. Explain the fundamental differences between demand forecasting methods and
quantitative forecasting methods.
2. Analyse the relationship between these two approaches.
3. Apply five demand forecasting methods and five quantitative forecasting methods to the
road freight business scenario.
4. Identify the most suitable methods and justify why they are most appropriate in this
context.
2. Differences Between Demand Forecasting and
Quantitative Forecasting Methods
Although both aim to predict future demand, demand forecasting methods and quantitative
forecasting methods differ fundamentally in their approach, data sources, and assumptions.
1. Nature of Data
o Demand forecasting often uses qualitative data, such as expert opinion, customer
surveys, or market intelligence.
o Quantitative forecasting is based on historical numerical data, using
mathematical and statistical models.
