The Influence of Big Data Analytics on Logistics Cost Optimization: A Case Study of BUA Cement in Kebbi State
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Background of the Study
Big data analytics plays a pivotal role in logistics, offering insights into cost drivers, operational inefficiencies, and demand patterns. By leveraging data from IoT devices, transportation management systems, and customer interactions, companies can optimize logistics costs and improve service delivery. BUA Cement has increasingly utilized big data analytics to enhance its supply chain efficiency in Kebbi State (Ezekiel & Bello, 2024).
This study examines how big data analytics influences cost optimization in logistics operations, with a focus on BUA Cement’s practices and strategies in Kebbi State (Musa & Ibrahim, 2023).
Statement of the Problem
High logistics costs remain a major challenge for companies like BUA Cement, affecting profitability and competitive positioning. Traditional cost management approaches have proven inadequate in addressing modern logistics complexities. Big data analytics offers a solution but requires proper evaluation of its effectiveness in cost optimization. This study addresses this gap (Olawale & Sadiq, 2024).
Objectives of the Study
- To analyze the impact of big data analytics on identifying cost drivers in logistics operations.
- To evaluate the role of big data analytics in optimizing transportation and warehousing costs.
- To recommend strategies for enhancing logistics cost optimization using big data analytics.
Research Questions
- How does big data analytics help in identifying logistics cost drivers at BUA Cement?
- What role does big data analytics play in optimizing transportation and warehousing costs?
- How can BUA Cement enhance logistics cost optimization using big data analytics?
Research Hypotheses
- H0: Big data analytics does not significantly identify logistics cost drivers at BUA Cement.
- H0: Big data analytics does not significantly optimize transportation and warehousing costs.
- H0: Enhancing logistics cost optimization is not significantly influenced by big data analytics.
Scope and Limitations of the Study
The study focuses on BUA Cement’s logistics operations in Kebbi State, analyzing the role of big data analytics in cost optimization. Limitations include data access constraints and the rapidly evolving nature of analytics technologies.
Definitions of Terms
- Big Data Analytics: The process of examining large datasets to uncover patterns, trends, and insights for decision-making.
- Logistics Cost Optimization: Strategies aimed at minimizing costs associated with transportation, warehousing, and distribution.
- Cost Drivers: Factors that cause changes in the cost of logistics operations.
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