Machine Learning Algorithms for Supply Chain Optimisation

Sridhar Jampani¹, Srikanthudu Avancha², Amit Mangal³, Dr S P Singh⁴, Shalu Jain⁵ & Raghav Agarwal⁶

¹Acharya Nagarjuna University, Guntur, Andhra Pradesh, India, , jampani.sridhar@gmail.com

²Bharathidasan University Palkalaiperur Tiruchirappalli – 620 024.srikaanth.avancha@gmail.com

 ³University Of Phoenix, Ts Riverpoint Pkwy, Phoenix, Az 85040, United States, atmangal108@gmail.com

 ⁴Ex-Dean, Gurukul Kangri University, Haridwar, Uttarakhand

 ⁵Maharaja Agrasen Himalayan Garhwal University, Pauri Garhwal, Uttarakhand mrsbhawnagoel@gmail.com

 ⁶Assistant System Engineer, TCS, Bengaluru, raghavagarwal4998@gmail.com

Abstract

The adoption of machine learning (ML) algorithms is transforming supply chain management by enabling businesses to enhance efficiency, accuracy, and decision-making. This paper explores the application of advanced ML techniques in optimizing various facets of the supply chain, including demand forecasting, inventory management, route planning, and supplier evaluation. Predictive models such as neural networks, time-series algorithms, and ensemble methods help organizations accurately forecast demand, reducing stockouts and overstock situations. Reinforcement learning models further contribute by optimizing dynamic pricing and inventory replenishment strategies.

ML-driven route optimization algorithms ensure efficient transportation by minimizing delivery times and fuel costs, improving both cost-efficiency and environmental sustainability. Additionally, unsupervised learning techniques aid in segmenting suppliers based on performance, risk, and reliability, promoting better supplier management. Real-time data analytics and anomaly detection algorithms are also instrumental in identifying disruptions, enabling faster responses to supply chain risks and bottlenecks.

This research emphasizes the integration of ML with IoT and cloud-based platforms, facilitating real-time visibility and enhanced data exchange across supply chain networks. The challenges associated with implementing ML, such as data quality, privacy concerns, and the need for skilled professionals, are also discussed. By leveraging machine learning, companies can achieve greater flexibility, improved customer satisfaction, and sustainable growth. The study concludes with insights into the future scope of ML applications, suggesting that continuous advancements in ML algorithms will unlock new opportunities for end-to-end supply chain optimization.

Keywords

Machine learning, supply chain optimization, demand forecasting, inventory management, route planning, predictive models, reinforcement learning, real-time data analytics, anomaly detection, supplier evaluation, IoT integration, dynamic pricing, cloud platforms, operational efficiency, risk management, sustainable growth.

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