An Examination of the Role of Big Data Analytics in Demand Forecasting: A Study of UAC Foods in Adamawa State
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- NGN 5000
Background of the Study
Big data analytics refers to the use of advanced data processing techniques and algorithms to extract valuable insights from large datasets. In the context of demand forecasting, big data analytics enables companies to analyze vast amounts of historical data, customer behavior, and external factors to predict future demand more accurately. UAC Foods, a leading Nigerian food processing company, has increasingly incorporated big data analytics into its demand forecasting processes, particularly in Adamawa State, where the company faces fluctuating consumer demand patterns and regional variations in purchasing behavior (Ismail & Bello, 2023).
Demand forecasting plays a crucial role in optimizing inventory management, reducing stockouts and overstocking, and ensuring that production schedules align with market demand. By leveraging big data analytics, UAC Foods can refine its demand forecasting models, increase the accuracy of predictions, and improve its overall supply chain performance. The ability to predict demand trends allows the company to adjust its production capacity and inventory levels accordingly, thus minimizing wastage and enhancing operational efficiency (Akinlolu & Olusola, 2024). Given the volatility in consumer preferences and the seasonal nature of some products, big data analytics offers UAC Foods a strategic advantage in adapting to changing market conditions.
This study will examine the role of big data analytics in enhancing demand forecasting accuracy and its impact on supply chain efficiency at UAC Foods in Adamawa State.
Statement of the Problem
Despite the potential of big data analytics in demand forecasting, UAC Foods in Adamawa State faces challenges related to data collection, integration, and the interpretation of large datasets. There is limited research on how big data analytics is being applied in the demand forecasting process at UAC Foods, particularly in addressing the specific challenges faced by companies in volatile and unpredictable markets like Adamawa State. Understanding the effectiveness of big data in improving demand forecasting accuracy is crucial for optimizing inventory and production processes.
Objectives of the Study
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To assess the role of big data analytics in demand forecasting at UAC Foods in Adamawa State.
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To examine the impact of big data analytics on the accuracy of demand forecasting and overall supply chain efficiency.
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To identify the challenges and opportunities associated with the adoption of big data analytics in demand forecasting.
Research Questions
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How does big data analytics contribute to demand forecasting at UAC Foods in Adamawa State?
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What impact does big data analytics have on the accuracy of demand forecasting and supply chain performance?
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What are the challenges and opportunities associated with the use of big data analytics in demand forecasting?
Research Hypotheses
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Big data analytics significantly improves the accuracy of demand forecasting at UAC Foods in Adamawa State.
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Big data analytics enhances supply chain performance by optimizing production and inventory management at UAC Foods.
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Challenges in data collection and integration reduce the effectiveness of big data analytics in demand forecasting at UAC Foods.
Scope and Limitations of the Study
This study will focus on the role of big data analytics in demand forecasting at UAC Foods in Adamawa State. Limitations may include difficulties in accessing detailed data on forecasting models, inventory management practices, and the integration of big data into the company’s operations.
Definitions of Terms
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Big Data Analytics: The process of examining large and complex datasets to uncover hidden patterns, correlations, and insights that can aid in decision-making (Ismail & Bello, 2023).
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Demand Forecasting: The process of predicting future customer demand for products and services to ensure efficient production and inventory management (Akinlolu & Olusola, 2024).
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Supply Chain Efficiency: The ability to manage supply chain activities effectively to minimize costs, maximize productivity, and meet customer demand (Ismail & Bello, 2023).
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