Page 67 - eBook_Proceedings of the International Conference on Digital Manufacturing V1

Leveraging Machine Learning to Enhance Supply Chain Agility and Strategic
Operational Excellence

Recent studies have underscored the transformative impact of
ML on SCM. For instance, Douaioui et al. (2024) conducted a
comprehensive review of ML and deep learning models for
demand forecasting, highlighting their effectiveness in capturing
nonlinear demand patterns and adapting to dynamic market
conditions. Similarly, Deore et al. (2024) examined the role of ML
in supply chain optimisation, emphasising its potential to enhance
decision-making processes and operational efficiency. Despite
these advancements, the practical implementation of ML in SCM
poses several challenges, including data quality issues, algorithm
selection, and integration with existing systems. This research
aims to address these challenges by developing and evaluating
ML-based models for demand forecasting, utilising historical and
real-world datasets. By comparing the performance of algorithms
such as Extreme Gradient Boosting (XGBoost) and Random
Forest, this study seeks to identify optimal approaches for
enhancing supply chain agility and achieving strategic operational
excellence.

STRATEGIC EVOLUTION OF SUPPLY CHAIN
MANAGEMENT

For instance, the advent of Industry 4.0 has ushered in an era of
unprecedented volatility and competition in the global market.
Supply chains face escalating pressure to become more agile and
efficient, driven by fluctuating demand patterns, operational
disruptions, and logistical uncertainties (Ivanov, 2019). This
examines the transformative potential of machine learning (ML)
in enhancing supply chain agility, with a specific focus on
optimising demand forecasting—a critical element for achieving
strategic and operational excellence. Supply Chain Management
(SCM) encompasses the planning and management of all
activities involved in sourcing, procurement, and logistics
management. The goal of SCM is to make these processes as
efficient as possible to guarantee that customers obtain the right
items, at the right time, and at the best conceivable cost.

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