Version 1
: Received: 8 December 2023 / Approved: 14 December 2023 / Online: 14 December 2023 (04:43:00 CET)
How to cite:
Balasubramanian, S.N.; B, V.S.R.; Chebiyyam, C.S.; J, K. Innovative Framework for Effective Service Parts Management in the Automotive Industry. Preprints2023, 2023121051. https://doi.org/10.20944/preprints202312.1051.v1
Balasubramanian, S.N.; B, V.S.R.; Chebiyyam, C.S.; J, K. Innovative Framework for Effective Service Parts Management in the Automotive Industry. Preprints 2023, 2023121051. https://doi.org/10.20944/preprints202312.1051.v1
Balasubramanian, S.N.; B, V.S.R.; Chebiyyam, C.S.; J, K. Innovative Framework for Effective Service Parts Management in the Automotive Industry. Preprints2023, 2023121051. https://doi.org/10.20944/preprints202312.1051.v1
APA Style
Balasubramanian, S.N., B, V.S.R., Chebiyyam, C.S., & J, K. (2023). Innovative Framework for Effective Service Parts Management in the Automotive Industry. Preprints. https://doi.org/10.20944/preprints202312.1051.v1
Chicago/Turabian Style
Balasubramanian, S.N., Chandrasekhara Sastry Chebiyyam and Krishnaiah J. 2023 "Innovative Framework for Effective Service Parts Management in the Automotive Industry" Preprints. https://doi.org/10.20944/preprints202312.1051.v1
Abstract
The Thailand automotive industry grapples with complex challenges in service parts management, demanding a strategic approach for post-sales operations. This research unveils a tailored methodology, leveraging historical sales data, refined part classifications, and strategic decision rules. A high-level overview of the automotive supply chain emphasizes interconnected stakeholders. The systematic methodology delves into part categorization, decision rules, and data analysis. Decision rule outcomes, exceptions, and contingency solutions showcase efficacy. A cost impact assessment highlights substantial savings. Visualization tools offer nuanced perspectives. Optimized service parts classification, robust stocking decision rules, and cost-effective strategies emerge. Support Vector Regression excels in forecasting, with recommendations for dynamic stocking. Implications extend to the broader industry, offering efficiency and service quality blueprints. Acknowledging limitations and suggesting future research, this study contributes a valuable framework for Thai automotive service parts management.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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