Adoption of machine learning in streamlining maintenance strategies for effective operations in automotive industries

The traditional approach to vehicle maintenance in the automotive industry is often reactive, leading to increased downtime, higher costs, and decreased productivity. There is a need for a more proactive and data-driven approach to maintenance that can help identify potential issues before they esca...

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Main Authors: Aniekan Ikpe, Imoh Ekanem
Format: Article
Language:English
Published: REA Press 2024-09-01
Series:Big Data and Computing Visions
Subjects:
Online Access:https://www.bidacv.com/article_204360_02183c092f5e77eb5ecedc515ff8ab40.pdf
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author Aniekan Ikpe
Imoh Ekanem
author_facet Aniekan Ikpe
Imoh Ekanem
author_sort Aniekan Ikpe
collection DOAJ
description The traditional approach to vehicle maintenance in the automotive industry is often reactive, leading to increased downtime, higher costs, and decreased productivity. There is a need for a more proactive and data-driven approach to maintenance that can help identify potential issues before they escalate. Machine learning offers the potential to analyze vast amounts of data and predict maintenance needs accurately, leading to more efficient operations. To investigate the adoption of machine learning in streamlining vehicle maintenance strategies, a comprehensive literature review was conducted to understand the current state of the automotive industry and the potential benefits of machine learning in maintenance operations.  Case studies and examples of companies that have successfully implemented machine learning in their maintenance strategies were also analyzed to identify best practices. The study revealed that machine learning can help automotive companies optimize their maintenance schedules, prioritize critical maintenance tasks, and improve the overall reliability of their vehicles. Consequently, enabling these companies stay competitive in a rapidly changing market by supporting them to quickly adapt to new technologies and customer demands. This proactive approach to maintenance is observed as a viable tool that can prevent costly breakdowns and reduce downtime, ultimately leading to increased productivity and profitability. However, from the findings of this study, adoption of machine learning in vehicle maintenance strategies is still in its early stages within the automotive industry. While some companies have commence implementation, many are still hesitant to fully embrace this technology. Barriers to adoption include concerns about data security, lack of expertise in machine learning, and resistance to change within organizations. With the conventional trends in vehicle maintenance strategies, it is essential for automotive companies to stay ahead of the curve and leverage this technology to drive innovation and success in their operations.
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spelling doaj-art-313dfc842a2346d7b5160c48b64d122a2025-01-30T12:23:36ZengREA PressBig Data and Computing Visions2783-49562821-014X2024-09-014318020010.22105/bdcv.2024.476761.1187204360Adoption of machine learning in streamlining maintenance strategies for effective operations in automotive industriesAniekan Ikpe0Imoh Ekanem1Department of Mechanical Engineering, Akwa Ibom State Polytechnic, Ikot Osurua, Ikot Ekpene, Nigeria.Department of Mechanical Engineering, Akwa Ibom State Polytechnic, Ikot Osurua, Ikot Ekpene, Nigeria.The traditional approach to vehicle maintenance in the automotive industry is often reactive, leading to increased downtime, higher costs, and decreased productivity. There is a need for a more proactive and data-driven approach to maintenance that can help identify potential issues before they escalate. Machine learning offers the potential to analyze vast amounts of data and predict maintenance needs accurately, leading to more efficient operations. To investigate the adoption of machine learning in streamlining vehicle maintenance strategies, a comprehensive literature review was conducted to understand the current state of the automotive industry and the potential benefits of machine learning in maintenance operations.  Case studies and examples of companies that have successfully implemented machine learning in their maintenance strategies were also analyzed to identify best practices. The study revealed that machine learning can help automotive companies optimize their maintenance schedules, prioritize critical maintenance tasks, and improve the overall reliability of their vehicles. Consequently, enabling these companies stay competitive in a rapidly changing market by supporting them to quickly adapt to new technologies and customer demands. This proactive approach to maintenance is observed as a viable tool that can prevent costly breakdowns and reduce downtime, ultimately leading to increased productivity and profitability. However, from the findings of this study, adoption of machine learning in vehicle maintenance strategies is still in its early stages within the automotive industry. While some companies have commence implementation, many are still hesitant to fully embrace this technology. Barriers to adoption include concerns about data security, lack of expertise in machine learning, and resistance to change within organizations. With the conventional trends in vehicle maintenance strategies, it is essential for automotive companies to stay ahead of the curve and leverage this technology to drive innovation and success in their operations.https://www.bidacv.com/article_204360_02183c092f5e77eb5ecedc515ff8ab40.pdfmachine learningmaintenance strategiesvehicle technologyautomotive industries
spellingShingle Aniekan Ikpe
Imoh Ekanem
Adoption of machine learning in streamlining maintenance strategies for effective operations in automotive industries
Big Data and Computing Visions
machine learning
maintenance strategies
vehicle technology
automotive industries
title Adoption of machine learning in streamlining maintenance strategies for effective operations in automotive industries
title_full Adoption of machine learning in streamlining maintenance strategies for effective operations in automotive industries
title_fullStr Adoption of machine learning in streamlining maintenance strategies for effective operations in automotive industries
title_full_unstemmed Adoption of machine learning in streamlining maintenance strategies for effective operations in automotive industries
title_short Adoption of machine learning in streamlining maintenance strategies for effective operations in automotive industries
title_sort adoption of machine learning in streamlining maintenance strategies for effective operations in automotive industries
topic machine learning
maintenance strategies
vehicle technology
automotive industries
url https://www.bidacv.com/article_204360_02183c092f5e77eb5ecedc515ff8ab40.pdf
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AT imohekanem adoptionofmachinelearninginstreamliningmaintenancestrategiesforeffectiveoperationsinautomotiveindustries