Page 69 - eBook_Proceedings of the International Conference on Digital Manufacturing V1

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

ML models can capture intricate patterns in data and adapt to
changing market conditions, providing a significant advantage
over traditional statistical methods. Techniques such as regression
models, time series analysis, and deep learning models can be
applied to achieve accurate and robust predictions. Recent
research has shown that machine learning models outperform
traditional statistical methods in forecasting accuracy, especially
when dealing with complex and volatile demand patterns
(Syntetos et al., 2016).

Core Applications of Machine Learning in Supply Chain
Management

Several studies have demonstrated the successful application of
ML in various aspects of SCM:

(a) Demand Forecasting
ML algorithms such as Regression Models, Time Series
Analysis, and Deep Learning Models can be applied to get
accurate and robust predictions. Machine learning can
significantly improve forecast accuracy, reduce inventory
costs, and enhance customer satisfaction (Syntetos et al.,
2016).
(b) Predictive Maintenance
ML algorithms can analyse sensor data from equipment
to predict potential failures, enabling proactive
maintenance and minimising downtime (Lee et al., 2018).
Predictive maintenance reduces maintenance costs,
improves equipment reliability, and enhances overall
operational efficiency.
(c) Logistics Optimisation
ML can optimise transportation routes, warehouse
operations, and delivery schedules, reducing costs and
improving efficiency (Boysen et al., 2019). Logistics
optimisation includes optimising transportation routes,
warehouse operations, and delivery schedules to reduce
costs and improve service levels.

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