A Review of Data-Driven Decision-Making in Supply Chain Resilience: A Study of Logistics Firms in Nasarawa State
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Background of the Study
In the face of global disruptions, supply chain resilience has become a critical focus for businesses aiming to mitigate risks and maintain operational continuity. Data-driven decision-making (DDDM) plays a pivotal role in strengthening supply chain resilience, enabling firms to respond more effectively to disruptions, forecast potential risks, and optimize supply chain processes (Bajpai et al., 2024). Data-driven strategies leverage technologies such as big data analytics, real-time tracking systems, and machine learning to enhance the responsiveness, efficiency, and adaptability of supply chains.
Logistics firms in Nasarawa State are increasingly adopting data-driven methods to improve their supply chain resilience. These methods offer improved visibility across the supply chain, helping logistics firms better manage inventory, predict disruptions, and streamline operations. However, the adoption and effective use of data analytics in supply chain resilience remain underexplored, particularly within the context of logistics firms in Nasarawa State. This study reviews the role of data-driven decision-making in enhancing the resilience of supply chains in this region, offering insights into its current application and potential for improvement.
Statement of the Problem
Logistics firms in Nasarawa State face significant challenges in managing their supply chains, especially in the context of unexpected disruptions such as natural disasters, political instability, or supply shortages (Akpan & Idowu, 2023). While data-driven decision-making has proven to be an effective tool for enhancing supply chain resilience, many logistics firms in the state have been slow to implement such strategies. This lack of adoption hinders their ability to manage risks effectively and respond to disruptions promptly. The study aims to review the impact of data-driven decision-making on the resilience of logistics firms in Nasarawa State.
Objectives of the Study
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To review the role of data-driven decision-making in enhancing supply chain resilience for logistics firms in Nasarawa State.
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To identify the key challenges logistics firms face in adopting data-driven decision-making for supply chain resilience.
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To assess the effectiveness of data-driven strategies in mitigating supply chain risks in logistics firms in Nasarawa State.
Research Questions
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How is data-driven decision-making currently being used to enhance supply chain resilience in logistics firms in Nasarawa State?
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What are the challenges faced by logistics firms in adopting data-driven decision-making for supply chain resilience?
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How effective are data-driven strategies in improving supply chain resilience and mitigating risks in logistics firms in Nasarawa State?
Research Hypotheses
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Data-driven decision-making has no significant impact on the resilience of supply chains in logistics firms in Nasarawa State.
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There is no significant relationship between the adoption of data-driven strategies and risk mitigation in supply chains.
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The challenges in adopting data-driven decision-making do not significantly hinder supply chain resilience in logistics firms in Nasarawa State.
Scope and Limitations of the Study
This review focuses on logistics firms in Nasarawa State and examines the role of data-driven decision-making in enhancing supply chain resilience. Limitations include the availability of reliable data from firms and the variability in the implementation of data-driven strategies across firms.
Definitions of Terms
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Data-Driven Decision-Making: The process of making decisions based on data analysis and insights rather than intuition or traditional decision-making methods.
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Supply Chain Resilience: The ability of a supply chain to adapt to and recover from disruptions while maintaining operational performance.
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Logistics Firms: Companies that provide services related to the transportation, storage, and distribution of goods.
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