The Role of Artificial Intelligence-Driven Inventory Management Solutions for Retailers: A Case Study of Kano Markets, Kano State
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1.1 Background of the Study
Inventory management is a crucial aspect of the retail business that ensures the right products are available at the right time without overstocking or understocking. Traditionally, inventory management has been handled manually or using basic software solutions, but with the rise of Artificial Intelligence (AI), retailers can now implement more sophisticated systems that automate inventory tracking, demand forecasting, and restocking processes (Chinonso & Okereke, 2024). AI-powered inventory management solutions utilize machine learning algorithms, predictive analytics, and real-time data collection to optimize stock levels, reduce waste, and improve the overall efficiency of the retail supply chain (Adeyemo et al., 2025).
Kano Markets, one of the largest retail hubs in Northern Nigeria, faces numerous challenges in managing inventory due to the high volume of transactions, seasonal demand fluctuations, and limited access to advanced technology. Implementing AI-driven inventory management solutions in such a high-traffic market could greatly enhance the operational efficiency of retailers, reducing errors and ensuring that stock levels meet consumer demand. This study will explore the role of AI-powered inventory management systems at Kano Markets and assess how these solutions can improve the overall performance of retailers in the region.
1.2 Statement of the Problem
Kano Markets is home to a variety of retailers facing challenges related to inventory management, including overstocking, stockouts, and waste. These challenges are exacerbated by the limited technological infrastructure and manual processes in place. Although AI-driven inventory management systems have proven successful in other parts of the world, the adoption of these technologies in Kano Markets remains limited. The problem lies in the gap between the potential of AI and the actual implementation and effectiveness of AI-powered systems in optimizing inventory management for local retailers.
1.3 Objectives of the Study
1. To assess the effectiveness of AI-driven inventory management solutions at Kano Markets.
2. To evaluate the impact of AI-powered inventory systems on stock levels, waste reduction, and customer satisfaction.
3. To identify the challenges and opportunities for AI adoption in inventory management within the retail sector of Kano Markets.
1.4 Research Questions
1. How effective are AI-driven inventory management solutions in optimizing stock levels and reducing waste at Kano Markets?
2. What impact do AI-powered inventory systems have on the overall operational efficiency and customer satisfaction in Kano Markets?
3. What challenges and barriers exist for the adoption of AI-driven inventory management systems in Kano Markets?
1.5 Research Hypothesis
1. AI-driven inventory management systems significantly reduce stockouts and overstocking at Kano Markets.
2. The implementation of AI-powered inventory solutions leads to improved customer satisfaction and operational efficiency.
3. Retailers in Kano Markets face challenges such as lack of technical expertise, limited access to data, and high initial costs when adopting AI-driven inventory management systems.
1.6 Significance of the Study
This study is significant because it will demonstrate the impact of AI-driven inventory management on retailers in Kano Markets, which are vital to the economic structure of the region. By examining the effectiveness of AI in improving inventory efficiency, reducing waste, and enhancing customer satisfaction, this study can provide valuable insights for policymakers, business owners, and other stakeholders in the Nigerian retail sector. Furthermore, the study will contribute to the growing literature on the use of AI in inventory management, especially in developing economies like Nigeria.
1.7 Scope and Limitations of the Study
The study focuses on AI-driven inventory management solutions for retailers operating within Kano Markets. It does not extend to other areas of retail or other technological solutions beyond AI. Limitations include the availability of accurate data on stock levels and sales, potential biases in retailer self-reporting, and the varying levels of technology adoption across different retailers in Kano Markets.
1.8 Operational Definition of Terms
1. AI-Driven Inventory Management: The use of machine learning algorithms and predictive analytics to automate inventory tracking and optimize stock levels.
2. Stockouts: Occurs when inventory levels are insufficient to meet customer demand, leading to missed sales opportunities.
3. Overstocking: The situation where retailers hold more inventory than needed, resulting in increased storage costs and potential waste.
4. Operational Efficiency: The ability of a business to deliver goods and services in the most cost-effective manner without sacrificing quality.
5. Customer Satisfaction: The level of contentment customers experience when they are able to find the products they want, with timely delivery and appropriate pricing.
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