An Evaluation of the Role of AI in Supply Chain Optimization: A Case Study of FrieslandCampina WAMCO in Adamawa State
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Background of the Study
Artificial Intelligence (AI) is becoming increasingly influential in enhancing supply chain management, providing organizations with the tools needed to optimize their operations and improve decision-making. FrieslandCampina WAMCO, a prominent dairy company in Nigeria, has been at the forefront of adopting innovative technologies to optimize its supply chain. In Adamawa State, where FrieslandCampina operates several key logistics and distribution centers, the application of AI holds significant promise in streamlining processes such as demand forecasting, inventory management, transportation, and supplier selection. AI technologies such as machine learning, predictive analytics, and automation can provide insights that help improve operational efficiency, reduce costs, and enhance the overall supply chain performance (Umar & Bello, 2024).
AI in supply chain management can lead to enhanced demand forecasting, ensuring that FrieslandCampina can better align production and inventory levels with consumer demand. Additionally, AI-based predictive models can optimize logistics routes, reducing transportation costs and improving delivery times. These advancements can have a profound impact on FrieslandCampina’s ability to meet market demands efficiently while maintaining quality standards. Despite the promising potential, the adoption of AI in the Nigerian supply chain context faces several challenges, including data quality, infrastructure limitations, and the need for skilled personnel (Ibrahim & Akinwunmi, 2023).
This study aims to evaluate the role of AI in supply chain optimization at FrieslandCampina WAMCO in Adamawa State, examining how the company leverages AI technologies to enhance efficiency and streamline operations.
Statement of the Problem
While AI has shown potential in optimizing supply chains across various industries, there is limited empirical research on its implementation in Nigeria, especially within the context of FrieslandCampina WAMCO in Adamawa State. The problem lies in understanding the extent to which AI can be integrated into FrieslandCampina’s supply chain operations and evaluating its effectiveness in improving key metrics such as cost reduction, inventory accuracy, and overall operational efficiency.
Objectives of the Study
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To evaluate the role of AI in supply chain optimization at FrieslandCampina WAMCO in Adamawa State.
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To assess the impact of AI technologies on logistics, demand forecasting, and inventory management at FrieslandCampina WAMCO.
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To identify the challenges and opportunities associated with AI adoption in FrieslandCampina WAMCO’s supply chain operations.
Research Questions
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How does AI contribute to supply chain optimization at FrieslandCampina WAMCO in Adamawa State?
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What is the impact of AI technologies on logistics, demand forecasting, and inventory management at FrieslandCampina WAMCO?
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What challenges does FrieslandCampina WAMCO face in adopting AI technologies in its supply chain operations?
Research Hypotheses
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AI significantly optimizes supply chain operations at FrieslandCampina WAMCO in Adamawa State.
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AI technologies positively impact logistics, demand forecasting, and inventory management at FrieslandCampina WAMCO.
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Challenges in AI adoption negatively affect FrieslandCampina WAMCO’s supply chain optimization in Adamawa State.
Scope and Limitations of the Study
This study will focus on evaluating the role of AI in supply chain optimization at FrieslandCampina WAMCO’s operations in Adamawa State. Limitations may include the difficulty in accessing proprietary data on AI systems and performance, as well as potential biases in the data used for analysis.
Definitions of Terms
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Artificial Intelligence (AI): The simulation of human intelligence processes by machines, particularly in the context of decision-making and automation (Ibrahim & Akinwunmi, 2023).
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Supply Chain Optimization: The process of improving the efficiency of a supply chain by reducing costs, improving delivery times, and enhancing inventory management (Umar & Bello, 2024).
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Predictive Analytics: A branch of AI that uses data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data (Ibrahim & Akinwunmi, 2023).
