Cornerstone Institute | The Role of Artificial Intelligence in Inventory Management: Making Smarter, Faster Decisions

Introduction

AI is changing the game in inventory management, making everything from tracking stock to predicting demand a whole lot smarter. Gone are the days of rigid, rule-based systems—now, machine learning (ML) and predictive analytics help businesses optimise their inventory, cut costs, and avoid stockouts. With supply chains becoming more complex and global, the need for AI-driven solutions has never been greater. This article dives into how AI is making inventory management more efficient, looking at real-world applications, key benefits, and what the future might hold.

How AI is Optimizing Inventory

One of the biggest headaches in inventory management is keeping the right amount of stock—too much, and you waste money; too little, and you lose sales. AI helps businesses find the right balance by using real-time data to make predictions rather than relying solely on historical trends (Dhaliwal et al., 2023).

Machine learning models, like artificial neural networks (ANN) and reinforcement learning, analyse buying patterns, seasonal demand shifts, and even external factors like economic trends and supplier performance. This means AI doesn’t just react—it anticipates. Some studies have shown AI can reduce stockouts by 30% and improve demand forecasting by 15% (Pasupuleti et al., 2024). These improvements translate into higher customer satisfaction, improved supply chain resilience, and increased profitability for businesses.

Smarter Inventory Management with AI

AI isn’t just about making predictions—it’s also about taking action. Integrated with platforms like Odoo, AI-driven systems can track stock levels in real-time, automate reordering, and even predict when warehouse equipment might fail (Naik & Raj, 2023).

IoT sensors play a huge role here, sending live updates on stock levels and warehouse conditions. This means companies can be proactive instead of reactive, leading to fewer surprises and smoother operations. AI-driven predictive maintenance has already cut warehouse downtime by 20% (Obeidat & Puiul, 2024). Moreover, businesses that incorporate AI-powered automation into their inventory management systems have seen significant reductions in labour costs and human error.

Solving the Backorder Problem with AI

No business wants to tell a customer, “Sorry, we’re out of stock.” AI-powered predictive analytics help companies avoid backorders by forecasting demand spikes and flagging products at risk of running out (Maitra & Kundu, 2023).

Using techniques like balanced bagging classifiers, fuzzy logic, and generative adversarial networks (GANs), AI models make better predictions and reduce false alarms. By also factoring in financial impacts, AI helps businesses make smarter, cost-effective replenishment decisions. This ensures that inventory levels are always aligned with real demand, preventing unnecessary stockpiling while minimizing shortages.

Making Warehouses Smarter with AI

AI is taking warehouse management to the next level by optimizing order picking, improving routing, and making resource allocation more efficient. Algorithms like genetic algorithms (GA) and ant colony optimization (ACO) help speed up operations by minimizing travel time and optimizing order fulfilment (Obeidat & Puiul, 2024).

Deep reinforcement learning is also being used to refine route planning and labour distribution, resulting in a 25% reduction in travel time and a 15% drop in operational costs. AI is not just improving efficiency—it’s making warehouse operations leaner and more cost-effective. Additionally, the integration of robotics and AI-driven automation has led to more streamlined warehouse workflows, allowing businesses to handle higher order volumes with fewer resources.

Enhancing Decision-Making with AI

Beyond logistics and inventory tracking, AI is helping businesses make smarter strategic decisions. By analysing vast datasets, AI models can identify trends and suggest optimal inventory policies. Businesses can adjust pricing strategies, forecast demand across different regions, and even predict supply chain disruptions before they happen.

AI’s ability to process massive amounts of data in real-time means that decision-makers no longer have to rely solely on intuition or outdated reports. Instead, they can make data-driven decisions that align with market trends and consumer preferences, ultimately boosting efficiency and profitability.

The Future of AI in Inventory Management

AI is already making inventory management more sustainable by cutting waste, improving energy efficiency and optimizing resources. Looking ahead, research should focus on hybrid AI models that mix machine learning with optimization techniques for even better decision-making. Another exciting possibility is integrating AI with blockchain for more transparent and traceable supply chains.

Real-time data processing and adaptive AI models will also become more common, allowing businesses to respond dynamically to market changes. AI’s role in inventory management is only going to expand, making supply chains smarter and more resilient. Companies that embrace AI-driven inventory management will have a competitive edge, improving efficiency while reducing costs and environmental impact.

Conclusion

AI is transforming inventory management by boosting forecasting accuracy, streamlining warehouse operations, and reducing backorder risks. By integrating AI with ERP and WMS, businesses can make faster, smarter decisions in real-time. As AI technology continues to evolve, companies that embrace these innovations will stay ahead in the competitive world of supply chain management. AI-driven solutions are no longer just an option; they are becoming a necessity for businesses looking to scale efficiently and remain adaptable in an ever-changing global market.

For new and mature students interested in studying supply chain management, Operations Management is offered as a qualification by the Cornerstone Institute, Business Studies. For more information contact our Deputy Dean of Business Studies, Sharon Brand, at  sharonb@cornerstone.ac.za.

By: Sharon Brand, Department of Business Studies, Cornerstone Institute, February 2025

References

Dhaliwal, N., Tomar, P.K., Joshi, A. and Reddy, G.S., 2023. A detailed analysis of the use of AI in inventory management for technically better management. International Conference on Advanced Computing and Innovative Technologies in Engineering (ICACITE). IEEE.

Naik, G.R. and Raj, C.V., 2023. AI-Based Inventory Management System Using Odoo. International Journal of Scientific Research in Engineering and Management (IJSREM), 7(8).

Obeidat, R. and Puiul, M.M., 2024. The influence of artificial intelligence on warehouse management systems. Proceedings in Manufacturing Systems, 19(1), pp.43-50.

Maitra, S. and Kundu, S., 2023. Backorder prediction in inventory management: Classification techniques and cost considerations. International Conference on Decision Science and Supply Chain Management.

Pasupuleti, V., Thuraka, B., Kodete, C.S. and Malisetty, S., 2024. Enhancing supply chain agility and sustainability through machine learning: Optimization techniques for logistics and inventory management. Logistics, 8(3), p.73.