Page 71 - eBook_Proceedings of the International Conference on Digital Manufacturing V1

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

It also supports sustainable practices by optimising resource
use, reducing environmental impact, and promoting ethical
algorithm deployment, with a focus on fairness, transparency, and
accountability

Supply chain resilience is strengthened through diversified
sourcing, buffer inventories, and flexible distribution strategies.
Visibility, supported by mapping tools, helps firms identify
vulnerabilities and prepare for disruptions. Advanced SCM
techniques, such as Economic Order Quantity (EOQ), Just-in-
Time (JIT), Safety Stock, FIFO (First-In-First-Out), and Vendor-
Managed Inventory (VMI) streamline operations, control costs,
and enhance inventory turnover. As global markets continue to
evolve, the need for agile and efficient supply chains will only
intensify. ML offers a powerful toolkit for addressing the
challenges of modern SCM, particularly in the realm of demand
forecasting. By embracing intelligent systems and data-driven
approaches, organisations can unlock new levels of performance
and resilience in their supply chain operations. The transformation
of SCM through ML is not just a technological evolution, but a
strategic imperative for organisations seeking to thrive in the era
of Industry 4.0.

RESEARCH METHODOLOGY

In this research study, the following methodology, shown in
Figure 12 is employed to ensure rigorous, replicable, and
impactful results.

55