Deep Learning Applications in Inventory Management

Laxmi Pandit Vishwakarma () and Rajesh Kumar Singh
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Laxmi Pandit Vishwakarma: Management Development Institute Gurgaon
Rajesh Kumar Singh: Management Development Institute Gurgaon

Chapter Chapter 11 in The Palgrave Handbook of Supply Chain and Disruptive Technologies, 2025, pp 287-308 from Springer

Abstract: Abstract The emergence of artificial intelligence, deep learning, machine learning, and artificial neural networks is rapidly changing supply chain activities. These technologies are responsible for shifting the traditional inventory management system into an intelligent system. Inventory management is the most crucial part of any supply chain management. With the rise in complexities like frequently changing demands, inventory management uncertainty increases and distributes the entire supply chain. Challenges are mitigated by adopting Industry 4.0 technologies, such as deep learning and inventory management. Deep learning is the developing sub-field of artificial intelligence (also known as the sub-field of machine learning). The application of deep learning has shown great potential in inventory management. The deep learning models are responsible for developing an automated inventory management system, making the supply chain more efficient. This chapter discusses twelve deep learning applications for managing inventory for an effective supply chain. These applications are automating inventory inspections, reducing inventory costs, improving decision performances, reducing the bullwhip effect, minimizing the risk of ineffective inventory management, extracting inventory features, optimizing inventory management, reducing inaccurate forecasting, developing inventory policies for the dynamic environment, predicting and managing stock levels, providing real-time inventory information, and providing vision for autonomous cars, robots, and drones.

Keywords: Inventory management; Deep learning; Artificial intelligence; Supply chain management; Bibliometric analysis; Literature review (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/978-3-031-90210-9_11

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