An Assessment of the Effectiveness of Prescriptive Analytics in Supply Chain Management: A Study of Logistics Firms in Kaduna State
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Background of the Study
Prescriptive analytics is the most advanced form of analytics, providing recommendations for decision-making based on data analysis and predictive insights. In supply chain management, prescriptive analytics can optimize operations, enhance decision-making, and improve overall efficiency by analyzing large volumes of data and suggesting actionable strategies (Okonkwo & Bello, 2023).
Logistics firms in Kaduna State play a crucial role in ensuring the smooth movement of goods across Nigeria. However, challenges such as route optimization, inventory management, and delivery delays often hinder their performance. By leveraging prescriptive analytics, these firms can make data-driven decisions to mitigate risks, reduce costs, and improve service delivery (Ibrahim & Ahmed, 2024).
This study evaluates the effectiveness of prescriptive analytics in supply chain management among logistics firms in Kaduna State, highlighting its impact on operational efficiency and decision-making.
Statement of the Problem
Supply chain inefficiencies are a significant challenge for logistics firms, leading to increased costs, delayed deliveries, and customer dissatisfaction. While prescriptive analytics offers a powerful tool for addressing these challenges, its adoption among logistics firms in Kaduna State is limited due to high costs, lack of technical expertise, and insufficient awareness (Bello & Yusuf, 2023).
Despite the growing recognition of prescriptive analytics in global supply chain management, there is limited research on its application and effectiveness in the Nigerian context. This study seeks to fill this gap by examining how logistics firms in Kaduna State use prescriptive analytics to improve their supply chain operations.
Objectives of the Study
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To assess the adoption of prescriptive analytics in supply chain management by logistics firms in Kaduna State.
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To evaluate the impact of prescriptive analytics on operational efficiency and decision-making in supply chain management.
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To propose strategies for enhancing the adoption of prescriptive analytics in logistics firms.
Research Questions
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How widely is prescriptive analytics adopted in supply chain management by logistics firms in Kaduna State?
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What impact does prescriptive analytics have on operational efficiency and decision-making in supply chain management?
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What strategies can enhance the adoption of prescriptive analytics in logistics firms?
Research Hypotheses
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Prescriptive analytics has no significant effect on operational efficiency in supply chain management.
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The adoption of prescriptive analytics does not significantly improve decision-making in logistics firms.
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Strategies for enhancing the adoption of prescriptive analytics have no significant impact on supply chain outcomes.
Scope and Limitations of the Study
The study focuses on logistics firms in Kaduna State, assessing the adoption and impact of prescriptive analytics in supply chain management. Limitations include variability in tool usage, access to proprietary data, and potential biases in survey responses.
Definitions of Terms
Prescriptive Analytics: A form of analytics that uses data to recommend actionable strategies and decisions.
Supply Chain Management: The management of the flow of goods and services from production to consumption.
Logistics Firms: Companies that specialize in the transportation, storage, and distribution of goods.
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