The Role of Artificial Intelligence in Optimizing Supply Chains in Northern Nigeria: A Case Study of BUA Group Sugar Refinery, Kwara State
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1.1 Background of the Study
The optimization of supply chains has become an essential goal for manufacturers globally, as it directly affects costs, operational efficiency, and competitiveness. In Northern Nigeria, businesses face significant supply chain challenges, including logistical inefficiencies, inventory management issues, and unpredictable demand (Adamu et al., 2024). The use of Artificial Intelligence (AI) to optimize supply chains has been widely studied in various industries, showing that AI tools can improve demand forecasting, inventory management, route optimization, and supplier selection.
BUA Group Sugar Refinery, located in Kwara State, represents one of the major sugar producers in Nigeria. The company has faced challenges with managing the complexities of its supply chain, including fluctuating raw material availability, transportation delays, and the efficient coordination of multiple suppliers and distributors. AI has the potential to address these challenges by streamlining operations, predicting demand, and improving decision-making processes (Olayemi & Fashola, 2025).
This study seeks to evaluate the role of AI in optimizing supply chain management at BUA Group Sugar Refinery, assessing the potential benefits, challenges, and opportunities that AI technologies can bring to the company’s supply chain operations.
1.2 Statement of the Problem
The BUA Group Sugar Refinery faces significant supply chain challenges, such as inconsistent raw material availability, delays in transportation, and inefficient inventory management. These issues result in increased production costs, stockouts, and reduced overall operational efficiency. While the company has attempted to address these issues through traditional supply chain management methods, these approaches have proven inadequate in addressing the increasing complexity and unpredictability of modern supply chains.
AI offers the possibility of optimizing various supply chain functions, including demand forecasting, inventory management, and route optimization. However, the adoption of AI for supply chain management at BUA Group Sugar Refinery has not been thoroughly explored. This study aims to investigate the role of AI in addressing the supply chain inefficiencies faced by the company and its potential to optimize operations.
1.3 Objectives of the Study
1. To assess the potential impact of AI technologies on optimizing the supply chain management at BUA Group Sugar Refinery.
2. To evaluate how AI can improve demand forecasting, inventory management, and transportation logistics within the supply chain at BUA Group Sugar Refinery.
3. To identify the challenges and barriers to the implementation of AI in supply chain management at BUA Group Sugar Refinery.
1.4 Research Questions
1. How can AI technologies optimize supply chain functions such as demand forecasting, inventory management, and transportation logistics at BUA Group Sugar Refinery?
2. What are the benefits of adopting AI in optimizing the supply chain at BUA Group Sugar Refinery?
3. What challenges does BUA Group Sugar Refinery face in adopting AI for supply chain optimization, and how can these challenges be addressed?
1.5 Research Hypothesis
1. The use of AI will significantly optimize supply chain functions at BUA Group Sugar Refinery, improving efficiency and reducing costs.
2. AI-based demand forecasting will reduce stockouts and improve production scheduling at BUA Group Sugar Refinery.
3. The successful implementation of AI in supply chain management will face challenges such as resistance to change, data integration issues, and lack of technical expertise.
1.6 Significance of the Study
This study is significant because it explores how AI technologies can optimize supply chain management in the Nigerian manufacturing sector. By focusing on BUA Group Sugar Refinery, the study provides actionable insights into the potential for AI to improve operational efficiency, reduce costs, and enhance product availability in Nigeria’s sugar industry. The findings will contribute to the body of knowledge on AI in supply chain optimization and offer valuable recommendations for other companies in the region seeking to integrate AI into their operations.
1.7 Scope and Limitations of the Study
The study will focus on the application of AI technologies to optimize the supply chain management at BUA Group Sugar Refinery in Kwara State. The research will cover AI's impact on demand forecasting, inventory management, and transportation logistics. However, it will not address other operational aspects such as marketing or customer relationship management. The study’s limitations include potential challenges in accessing proprietary data, the time frame of the study, and the difficulties in generalizing the findings to all manufacturing plants in Nigeria.
1.8 Operational Definition of Terms
1. Artificial Intelligence (AI): The use of advanced algorithms and machine learning techniques to automate decision-making and improve processes.
2. Supply Chain Optimization: The process of improving the efficiency and effectiveness of supply chain operations, including forecasting, inventory management, and logistics.
3. Demand Forecasting: The use of AI to predict future product demand based on historical data and trends.
4. Inventory Management: The process of managing and controlling stock levels of raw materials, components, and finished goods to ensure an efficient supply chain.
5. Route Optimization: The use of AI to determine the most efficient routes for transportation to reduce delivery times and costs.
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