Background of the Study
Artificial intelligence (AI) has revolutionized warehouse management, enhancing efficiency through automation, predictive analytics, and real-time decision-making. AI applications in warehouse automation include robotic systems, inventory tracking, and demand forecasting. Companies like Dangote Cement have begun integrating AI-driven systems to streamline operations and reduce costs (Ahmed & Bello, 2023).
In Borno State, Dangote Cement’s warehouse operations provide a context to explore how AI-driven automation impacts productivity and operational efficiency. This study aims to understand the role of AI in addressing warehouse challenges and driving innovation (Musa & Adewale, 2024).
Statement of the Problem
Manual and semi-automated warehouse systems often lead to inefficiencies, errors, and higher operational costs. Dangote Cement faces challenges in scaling its warehouse operations to meet growing demands in Borno State. AI presents a solution, but its adoption requires an understanding of its effectiveness and implementation barriers. This study addresses these gaps by analyzing AI's role in warehouse automation (Okonkwo & Sadiq, 2023).
Objectives of the Study
Research Questions
Research Hypotheses
Scope and Limitations of the Study
The study focuses on Dangote Cement’s warehouse operations in Borno State, analyzing the implementation and impact of AI-driven systems. Limitations include access to proprietary data and technological infrastructure variations.
Definitions of Terms
Inventory Accuracy: The degree to which inventory records match physical stock levels.
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