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TRL4861 ASSIGNMENT 2 2025

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TRL4861 Assignment 2 2025; 100% TRUSTED workings with detailed Answers for A+ Grade.










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Uploaded on
September 14, 2025
Number of pages
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Written in
2025/2026
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TRL4861
ASSIGNMENT 2 2025

UNIQUE NO.
DUE DATE: SEPTEMBER 2025

, TRL4861 Assignment 2: Forecasting Demand for a Road Freight Business in
KwaZulu-Natal

Introduction

The establishment of a road freight business that specialises in transporting building
materials to rural areas of KwaZulu-Natal (KZN) requires a clear understanding of future
demand trends. Demand for building materials in rural regions is shaped by
infrastructure development projects, housing initiatives, seasonal construction cycles,
and macroeconomic variables such as interest rates and inflation. For effective
planning, both demand forecasting methods and quantitative forecasting techniques are
necessary to project likely consumption patterns for 2025 and 2026. This essay
distinguishes between the two approaches, analyses their relationship, and proposes
the most suitable methods for a freight business operating in the rural KZN market.




1. Differences between Demand Forecasting and Quantitative Forecasting

Although often used interchangeably, demand forecasting methods and quantitative
forecasting methods are conceptually distinct. Demand forecasting refers to the broader
process of predicting customer demand for goods or services using qualitative insights,
market trends, and judgemental assessments (Heizer et al., 2020). It is generally
subjective, drawing on expert opinions, market surveys, and scenario planning. For
instance, assessing government housing policy in KZN or consulting with contractors
about upcoming projects falls under demand forecasting.

Quantitative forecasting, on the other hand, is grounded in numerical analysis and
statistical modelling. It relies on historical data to predict future demand using
mathematical techniques such as regression analysis, time series models, and moving
averages (Makridakis et al., 2019). Quantitative forecasting provides more objective
results but depends heavily on the availability and accuracy of past data. In the context

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