TRL4861
Assignment 2
Due September 2025
, TRL4861
Assignment 2
Due September 2025
Advanced Transport Management
Forecasting Demand for a Road Freight Business in Rural KwaZulu-Natal
Question 1: What are the fundamental differences between demand forecasting
methods and quantitative forecasting methods?
Demand forecasting methods—often described as qualitative approaches—rely
primarily on human judgment, expert opinion, market surveys, and intuitive
assessments. They are most valuable when reliable historical data is absent, limited, or
distorted, such as in new markets or under volatile conditions. For instance, in rural
KwaZulu-Natal (KZN), demand for building materials is strongly shaped by
unpredictable factors like infrastructure funding shifts, climate events, or government-led
housing projects. These approaches avoid mathematical modelling and instead harness
techniques such as surveys, consensus-building exercises, or historical analogies to
approximate future demand.
In contrast, quantitative forecasting methods are grounded in statistical analysis and
historical numerical data. They rely on the assumption that past patterns, such as trends
or cycles, will extend into the future. Common techniques include time series analysis,
regression models, exponential smoothing, and ARIMA. Quantitative methods generate
objective, replicable results, but they require sufficient, high-quality data and work best
in relatively stable environments where measurable patterns can be observed and
projected.
Assignment 2
Due September 2025
, TRL4861
Assignment 2
Due September 2025
Advanced Transport Management
Forecasting Demand for a Road Freight Business in Rural KwaZulu-Natal
Question 1: What are the fundamental differences between demand forecasting
methods and quantitative forecasting methods?
Demand forecasting methods—often described as qualitative approaches—rely
primarily on human judgment, expert opinion, market surveys, and intuitive
assessments. They are most valuable when reliable historical data is absent, limited, or
distorted, such as in new markets or under volatile conditions. For instance, in rural
KwaZulu-Natal (KZN), demand for building materials is strongly shaped by
unpredictable factors like infrastructure funding shifts, climate events, or government-led
housing projects. These approaches avoid mathematical modelling and instead harness
techniques such as surveys, consensus-building exercises, or historical analogies to
approximate future demand.
In contrast, quantitative forecasting methods are grounded in statistical analysis and
historical numerical data. They rely on the assumption that past patterns, such as trends
or cycles, will extend into the future. Common techniques include time series analysis,
regression models, exponential smoothing, and ARIMA. Quantitative methods generate
objective, replicable results, but they require sufficient, high-quality data and work best
in relatively stable environments where measurable patterns can be observed and
projected.
