An Evaluation of Real-Time Predictive Maintenance in Nigerian Manufacturing Firms: A Study in Yobe State
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- NGN 5000
Background of the Study
Predictive maintenance is a proactive approach that uses data analytics, sensors, and machine learning algorithms to predict equipment failures and schedule maintenance before breakdowns occur. In real-time applications, this technology enables continuous monitoring of machinery to prevent downtime, reduce maintenance costs, and enhance operational efficiency (Okonkwo & Yusuf, 2023).
Manufacturing firms in Yobe State operate in a challenging environment where maintaining consistent production is essential for profitability. Real-time predictive maintenance offers a solution to equipment failures, which are a common cause of unplanned downtime and production delays. However, factors such as inadequate technological infrastructure, high implementation costs, and limited technical expertise hinder the adoption of predictive maintenance technologies in the region (Ahmed & Salisu, 2024).
This study evaluates the role of real-time predictive maintenance in improving operational efficiency and reducing costs in manufacturing firms in Yobe State, providing insights into its adoption, benefits, and challenges.
Statement of the Problem
Unplanned equipment failures and maintenance issues are significant challenges for manufacturing firms in Yobe State, leading to production losses and increased operational costs. While real-time predictive maintenance offers a data-driven solution to these problems, its adoption is limited due to high costs, lack of skilled personnel, and poor technological infrastructure (Bello & Musa, 2023).
Existing research has primarily focused on predictive maintenance in developed economies, with limited attention to its application and effectiveness in Nigerian manufacturing firms, particularly in Yobe State. This study seeks to fill this gap by examining the adoption and impact of real-time predictive maintenance in the local context.
Objectives of the Study
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To assess the adoption of real-time predictive maintenance in manufacturing firms in Yobe State.
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To evaluate the impact of real-time predictive maintenance on operational efficiency and cost reduction.
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To propose strategies for enhancing the adoption of predictive maintenance technologies in manufacturing firms.
Research Questions
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How widely is real-time predictive maintenance adopted by manufacturing firms in Yobe State?
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What impact does real-time predictive maintenance have on operational efficiency and cost reduction?
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What strategies can improve the adoption of predictive maintenance technologies in manufacturing firms?
Research Hypotheses
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Real-time predictive maintenance has no significant effect on operational efficiency in manufacturing firms.
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The adoption of predictive maintenance does not significantly reduce maintenance costs.
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Strategies for enhancing predictive maintenance adoption have no significant impact on operational outcomes.
Scope and Limitations of the Study
This study focuses on manufacturing firms in Yobe State, evaluating the adoption and impact of real-time predictive maintenance technologies. Limitations include access to operational data, variability in tool adoption, and potential biases in responses from firm representatives.
Definitions of Terms
Predictive Maintenance: A maintenance strategy that uses data and analytics to predict equipment failures before they occur.
Real-Time Monitoring: The continuous tracking and analysis of equipment performance data in real time.
Manufacturing Firms: Companies involved in the production of goods using labor, machines, tools, and raw materials.
