TRL4861
Assignment 2
Due September 2025
, TRL4861
Assignment 2
Due September 2025
Forecasting Demand for Building Materials in Rural KwaZulu-Natal: Application of
Qualitative and Quantitative Methods
Question 1: What are the fundamental differences between demand forecasting
methods and quantitative forecasting methods?
Answer:
Demand forecasting methods—often classified as qualitative approaches—are rooted in
subjective judgments rather than statistical computation. They rely on expert opinions,
market insights, stakeholder surveys, and analogy-based reasoning. Such methods are
indispensable when historical data is scarce, unreliable, or irrelevant, which is often the
case in emerging markets, new product launches, or contexts shaped by irregular
government interventions. For example, the demand for building materials in rural
KwaZulu-Natal (KZN) is strongly influenced by unpredictable infrastructure projects,
disaster recovery programs, and socio-economic volatility. These conditions diminish
the reliability of purely data-driven models, making qualitative techniques particularly
valuable.
By contrast, quantitative forecasting methods draw on historical numerical data and
statistical models to detect patterns and project future demand. Techniques such as
time series analysis, regression models, exponential smoothing, and ARIMA
assume that past behavior offers a reliable proxy for the future. Quantitative methods
are objective, replicable, and efficient in stable contexts with robust datasets—where
cyclical trends, seasonal fluctuations, or long-term growth patterns can be statistically
Assignment 2
Due September 2025
, TRL4861
Assignment 2
Due September 2025
Forecasting Demand for Building Materials in Rural KwaZulu-Natal: Application of
Qualitative and Quantitative Methods
Question 1: What are the fundamental differences between demand forecasting
methods and quantitative forecasting methods?
Answer:
Demand forecasting methods—often classified as qualitative approaches—are rooted in
subjective judgments rather than statistical computation. They rely on expert opinions,
market insights, stakeholder surveys, and analogy-based reasoning. Such methods are
indispensable when historical data is scarce, unreliable, or irrelevant, which is often the
case in emerging markets, new product launches, or contexts shaped by irregular
government interventions. For example, the demand for building materials in rural
KwaZulu-Natal (KZN) is strongly influenced by unpredictable infrastructure projects,
disaster recovery programs, and socio-economic volatility. These conditions diminish
the reliability of purely data-driven models, making qualitative techniques particularly
valuable.
By contrast, quantitative forecasting methods draw on historical numerical data and
statistical models to detect patterns and project future demand. Techniques such as
time series analysis, regression models, exponential smoothing, and ARIMA
assume that past behavior offers a reliable proxy for the future. Quantitative methods
are objective, replicable, and efficient in stable contexts with robust datasets—where
cyclical trends, seasonal fluctuations, or long-term growth patterns can be statistically
