Page 66 - eBook_Proceedings of the International Conference on Digital Manufacturing V1

Proceedings of the International Conference on Digital Manufacturing –
Volume 1

adaptability, and facilitate data-driven decision-making. This
paper highlights the transformative potential of intelligent systems
in revolutionising demand forecasting, optimising operational
efficiency, reducing costs, minimising delays, and ultimately,
fostering a more resilient and agile supply chain infrastructure.

Keywords: Supply Chain Management, Machine Learning,
Demand Forecasting

INTRODUCTION

In an era marked by heightened globalisation and market
volatility, Supply Chain Management (SCM) has emerged as a
pivotal determinant of organisational competitiveness and
resilience. SCM encompasses the arrangement of processes,
assets, stakeholders, and information flows involved in the
production and delivery of goods and services. Efficient SCM is
instrumental in minimising operational costs, enhancing customer
satisfaction, and ensuring timely product availability. The advent
of Industry 4.0 has catalysed the integration of advanced
technologies such as Machine Learning (ML), the Internet of
Things (IoT), and big data analytics into SCM. These technologies
facilitate real-time monitoring, predictive analytics, and
autonomous decision-making, thereby transforming traditional
supply chains into intelligent, adaptive networks. Among these
technologies, ML has gained significant attention for its capacity
to process vast and complex datasets, uncover hidden patterns, and
generate accurate forecasts. In the context of SCM, ML algorithms
have been applied to various domains, including demand
forecasting, inventory optimisation, procurement, and logistics.
Notably, ML-driven demand forecasting has demonstrated
superior accuracy compared to traditional statistical methods,
enabling firms to align production schedules with market demand,
reduce inventory holding costs, and mitigate the risks of stock outs
or overstocking.

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