An evaluation of the role of predictive analytics in supply chain management: A study of Dangote Cement in Borno State
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Background of the Study
Predictive analytics in supply chain management refers to the use of historical data, statistical algorithms, and machine learning techniques to forecast future trends, demands, and potential disruptions. This technology allows businesses to make data-driven decisions regarding inventory management, procurement, and logistics. By predicting future market conditions, companies can reduce uncertainties, optimize operations, and enhance their competitive advantage.
Dangote Cement, one of the largest cement manufacturers in Nigeria, operates a complex supply chain that spans across various regions, including Borno State. The company’s ability to predict demand, manage inventory levels, and anticipate potential disruptions in the supply chain is critical to its continued growth and success. Given the volatility of the Nigerian economy, which is often affected by factors such as energy shortages, political instability, and supply chain disruptions, predictive analytics has become an essential tool in enhancing Dangote Cement’s supply chain resilience.
This study will evaluate the role of predictive analytics in improving supply chain management at Dangote Cement in Borno State, focusing on its ability to forecast demand and optimize operations.
Statement of the Problem
Dangote Cement operates in a highly dynamic environment, where fluctuations in demand, political instability, and supply chain disruptions can impact production and distribution efficiency. While predictive analytics offers the potential to improve forecasting accuracy and operational planning, its adoption and effectiveness within Dangote Cement’s supply chain have not been adequately explored. This study seeks to evaluate the role of predictive analytics in enhancing supply chain management and its impact on operational efficiency at Dangote Cement in Borno State.
Objectives of the Study
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To evaluate the role of predictive analytics in improving supply chain management at Dangote Cement in Borno State.
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To examine how predictive analytics contributes to demand forecasting and inventory management at Dangote Cement.
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To assess the impact of predictive analytics on operational efficiency and cost reduction in the supply chain of Dangote Cement.
Research Questions
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How does predictive analytics improve supply chain management at Dangote Cement in Borno State?
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What is the role of predictive analytics in demand forecasting and inventory management at Dangote Cement?
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How does the use of predictive analytics impact operational efficiency and cost reduction at Dangote Cement?
Research Hypotheses
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Predictive analytics significantly improves supply chain management at Dangote Cement in Borno State.
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Predictive analytics enhances demand forecasting and inventory management at Dangote Cement.
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The use of predictive analytics leads to increased operational efficiency and cost reduction at Dangote Cement.
Scope and Limitations of the Study
This study will focus on evaluating the role of predictive analytics in supply chain management at Dangote Cement in Borno State. Data will be gathered through interviews, surveys, and analysis of supply chain performance metrics. Limitations of the study may include challenges in accessing proprietary data and quantifying the direct impact of predictive analytics on operational performance.
Definitions of Terms
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Predictive Analytics: The use of historical data, statistical algorithms, and machine learning to forecast future trends and behaviors.
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Supply Chain Management: The management of the flow of goods, services, and information from the point of origin to the final consumer.
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Demand Forecasting: The process of estimating future customer demand for products and services based on historical data and market trends.
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