The Impact of Machine Learning Algorithms on Sales Forecasting: A Case Study of Supermarkets in Benue State
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- NGN 5000
Background of the Study
Accurate sales forecasting is essential for effective inventory management, financial planning, and overall business success. Machine learning (ML) algorithms, which leverage large datasets to predict future sales patterns, have revolutionized traditional forecasting methods. These algorithms enhance accuracy, reduce forecasting errors, and enable businesses to respond proactively to market trends.
In Benue State, supermarkets face unique challenges in sales forecasting due to fluctuating market demands, limited access to advanced technologies, and operational inefficiencies. Research by Uche and Okafor (2024) underscores the growing relevance of ML in addressing these challenges by providing data-driven insights and improving decision-making. This study evaluates the impact of ML algorithms on sales forecasting in supermarkets in Benue State, offering insights into their effectiveness and implementation barriers.
Statement of the Problem
Supermarkets in Benue State often rely on traditional forecasting methods, which are prone to inaccuracies and fail to account for dynamic market changes. The adoption of machine learning algorithms, despite their proven effectiveness, remains limited due to factors such as high implementation costs, lack of expertise, and data quality issues.
Research by Ogbu and Adigun (2025) highlights that while ML-based sales forecasting significantly improves accuracy and efficiency, its adoption in developing regions like Benue State is constrained by several challenges. This study aims to assess the impact of ML algorithms on sales forecasting and identify strategies to enhance their adoption in supermarkets.
Objectives of the Study
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To evaluate the effectiveness of machine learning algorithms in sales forecasting for supermarkets in Benue State.
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To identify the challenges faced by supermarkets in adopting machine learning for sales forecasting.
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To propose strategies for improving the adoption and effectiveness of machine learning algorithms in sales forecasting.
Research Questions
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How effective are machine learning algorithms in forecasting sales for supermarkets in Benue State?
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What challenges hinder the adoption of machine learning algorithms for sales forecasting?
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What strategies can enhance the adoption and effectiveness of machine learning algorithms in sales forecasting?
Research Hypotheses
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Machine learning algorithms do not significantly improve sales forecasting accuracy in supermarkets in Benue State.
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Challenges significantly hinder the adoption of machine learning algorithms for sales forecasting.
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Proposed strategies do not significantly enhance the adoption of machine learning algorithms.
Scope and Limitations of the Study
The study focuses on supermarkets in Benue State, assessing the impact of machine learning algorithms on sales forecasting. Limitations include potential resistance to sharing proprietary data and the rapidly evolving nature of ML technologies.
Definitions of Terms
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Machine Learning Algorithms: Computational models that use data to predict outcomes and optimize processes.
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Sales Forecasting: The process of estimating future sales based on historical data and market trends.
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Supermarkets: Large retail establishments that sell a wide variety of goods, including food and household items.
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