Author(s)

Ahmed Mustafa, Nourhan Saleh, Rania Adel^*

Affiliation(s)

School of Business, Imperial College London, Exhibition Rd, South Kensington, London SW7 2AZ, UK. 

Corresponding Author

Rania Adel

ABSTRACT

This paper explores the transformative potential of advanced deep learning frameworks in enhancing supply chain efficiency. Supply chain management is essential for modern businesses, encompassing the planning, execution, and control of activities involved in delivering products from raw materials to end consumers. Traditional supply chain practices often rely on linear processes and historical data, leading to inefficiencies such as delays, stockouts, and excess inventory. These challenges necessitate the adoption of innovative technologies to improve operational performance. Deep learning, a subset of artificial intelligence, has emerged as a powerful tool for analyzing large datasets and identifying complex patterns, making it particularly valuable for applications in demand forecasting, inventory management, and logistics optimization.

It reviews existing literature on the application of deep learning in supply chains, highlighting its capabilities in improving demand forecasting accuracy, optimizing inventory levels, and enhancing decision-making processes. The study identifies key deep learning techniques, including recurrent neural networks and convolutional neural networks, which are effective in managing time-series data and image-related tasks, respectively. Furthermore, the paper discusses the challenges and gaps in current research, such as the need for integrated frameworks that harmonize deep learning with traditional supply chain processes and the ethical implications of AI-driven decision-making.

The findings suggest that organizations can significantly benefit from the integration of deep learning into their supply chain operations, achieving greater agility and responsiveness to market changes. However, successful implementation requires a commitment to continuous learning and adaptation, as well as investment in infrastructure and data quality. The future of supply chain efficiency in the age of deep learning is promising, with opportunities for further research and development to fully harness the capabilities of these advanced technologies. By fostering a culture of innovation and collaboration, organizations can position themselves to thrive in an increasingly complex and competitive landscape.

KEYWORDS

Deep learning; Supply chain efficiency; Artificial Intelligence

CITE THIS PAPER

Ahmed Mustafa, Nourhan Saleh, Rania Adel. Improving supply chain efficiency through advanced deep learning framework. Social Science and Management. 2024, 1(3): 19-27. DOI: https://doi.org/10.61784/ssm3031.

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