A Review of AI Applications in Predictive Maintenance for Manufacturing Firms: A Case Study in Niger State
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Background of the Study
The manufacturing industry is vital to the Nigerian economy, contributing significantly to industrial output and employment. However, the sector faces significant challenges, particularly in equipment maintenance. Traditional maintenance practices, such as scheduled or reactive maintenance, have proven inefficient and costly. Predictive maintenance, supported by artificial intelligence (AI), has emerged as an innovative solution. By using AI to analyze real-time data from equipment sensors, manufacturers can predict when a machine is likely to fail, allowing for preemptive repairs (Ademola & Balogun, 2024).
In Niger State, many manufacturing firms are beginning to explore AI-driven predictive maintenance to enhance operational efficiency and reduce downtime. The adoption of AI technologies in predictive maintenance allows manufacturers to avoid costly unplanned shutdowns and extend the lifespan of their equipment. However, the full implementation of AI in this context is often hindered by issues such as the high cost of AI technology, a lack of technical expertise, and difficulties in integrating AI systems with existing machinery (Oladipo & Salau, 2023). This review investigates the application of AI in predictive maintenance within Niger State’s manufacturing sector, examining its effectiveness, challenges, and opportunities.
Statement of the Problem
While AI applications in predictive maintenance have shown promising results globally, their adoption in manufacturing firms in Niger State is still at an early stage. These firms face several challenges, including limited access to AI expertise, the high cost of implementation, and integration difficulties with existing systems. This review aims to evaluate the effectiveness of AI in predictive maintenance for manufacturing firms in Niger State and identify the barriers to its widespread adoption.
Objectives of the Study
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To evaluate the impact of AI applications on predictive maintenance in manufacturing firms in Niger State.
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To examine the challenges faced by manufacturing firms in Niger State in adopting AI for predictive maintenance.
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To identify strategies for enhancing the adoption of AI-powered predictive maintenance in Niger State’s manufacturing sector.
Research Questions
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How effective are AI applications in predictive maintenance in manufacturing firms in Niger State?
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What challenges do manufacturing firms in Niger State face in adopting AI for predictive maintenance?
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What strategies can be employed to improve the adoption of AI-driven predictive maintenance in Niger State’s manufacturing sector?
Research Hypotheses
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AI applications in predictive maintenance have no significant impact on reducing downtime in manufacturing firms in Niger State.
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Challenges such as high implementation costs and technical expertise do not significantly hinder the adoption of AI-powered predictive maintenance in Niger State’s manufacturing firms.
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Strategies aimed at enhancing AI adoption will not significantly improve predictive maintenance outcomes in Niger State’s manufacturing sector.
Scope and Limitations of the Study
This study is limited to manufacturing firms in Niger State that are either currently using or are in the process of adopting AI-based predictive maintenance technologies. Limitations include access to proprietary data, and there may be variation in the level of AI integration across firms in the region.
Definitions of Terms
Predictive Maintenance: Maintenance strategies that use AI and real-time data analysis to predict when equipment failures will occur, allowing for preemptive repairs.
Artificial Intelligence (AI): The simulation of human intelligence in machines programmed to perform tasks such as learning, reasoning, and problem-solving.
Manufacturing Firms: Companies that engage in the production of goods using labor, machines, and raw materials.
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