Strategic Insights and Innovations in Prefabricated Component Obsolescence Mitigation: A Focus on SVM-Based Models
Keywords:
Prefabricated component obsolescence, Mitigation model, Machine learning regression, Support Vector Machine regressionAbstract
The research addresses prefabricated component obsolescence challenges, aiming to develop a robust mitigation model using machine learning regression, particularly Support Vector Machine (SVM) regression. The comparative study shows SVM's superiority in predicting obsolescence over other models, yet highlights interpretability and scalability improvements. Introducing SVM-based prefabricated component Obsolescence Mitigation, a specialized model, the research emphasizes domain-specific features for accurate predictions. It encourages further refinement and exploration across industries. Positioned as a valuable tool, the SVM-based model offers precise information for decision-making, potentially reducing costs and fortifying supply chains. The three-stage approach includes data collection, SVM model development, and mitigation strategy development, providing a comprehensive solution for obsolescence management. SVM's accuracy shows an increase with higher regularization factor, ranging from 0.782 at C = 0.01 to 0.907 at C = 0.1. SVM-based Prefabricated Component Obsolescence Mitigation consistently demonstrates higher accuracy, reaching 0.976 for both C values (0.01 and 0.1). The research underscores the critical role of sophisticated models in addressing prefabricated component obsolescence challenges.
Downloads
References
P. F. Rocha, N. O. Ferreira, F. Pimenta, and N. B. Pereira, “Impacts of Prefabrication in the Building Construction Industry,” Encyclopedia, vol. 3, no. 1, pp. 28–45, 2022, doi: 10.3390/encyclopedia3010003.
M. Ahmid and O. Kazar, “A Comprehensive Review of the Internet of Things Security,” J. Appl. Secur. Res., vol. 18, no. 3, pp. 289–305, 2023, doi: 10.1080/19361610.2021.1962677.
E. Attouri, Z. Lafhaj, L. Ducoulombier, and B. Linéatte, “The current use of industrialized construction techniques in France: Benefits, limits and future expectations,” Clean. Eng. Technol., vol. 7, 2022, doi: 10.1016/j.clet.2022.100436.
B. Yao, “A systematic literature review on supply chain management,” Sch. Manag., vol. 8, no. 33, p. 44, 2014.
R. Sun, X. Geng, L. Zhao, Y. Wang, and R. Guo, “Research on the Risk-Inducing Factors of Prefabricated,” pp. 1–27, 2023.
L. Sierra-Fontalvo, A. Gonzalez-Quiroga, and J. A. Mesa, “A deep dive into addressing obsolescence in product design: A review,” Heliyon, vol. 9, no. 11, p. e21856, 2023, doi: 10.1016/j.heliyon.2023.e21856.
T. Sudan, R. Taggar, P. K. Jena, and D. Sharma, “Supply chain disruption mitigation strategies to advance future research agenda: A systematic literature review,” J. Clean. Prod., vol. 425, no. November, p. 138643, 2023, doi: 10.1016/j.jclepro.2023.138643.
A. Ates and N. Acur, “Making obsolescence obsolete: Execution of digital transformation in a high-tech manufacturing SME,” J. Bus. Res., vol. 152, no. August, pp. 336–348, 2022, doi: 10.1016/j.jbusres.2022.07.052.
P. Singh and P. Sandborn, “OBSOLESCENCE DRIVEN DESIGN REFRESH PLANNING FOR SUSTAINMENT- DOMINATED SYSTEMS Pameet Singh Peter Sandborn CALCE Electronic Products and Systems Center Department of Mechanical Engineering,” Eng. Econ., vol. 51, no. 2, pp. 115–139, 2006.
J. Ma and G. E. O. Kremer, A systematic literature review of modular product design (MPD) from the perspective of sustainability, vol. 86, no. 5–8. 2016. doi: 10.1007/s00170-015-8290-9.
Y. K. Dwivedi et al., “Setting the future of digital and social media marketing research: Perspectives and research propositions,” Int. J. Inf. Manage., vol. 59, no. May 2020, p. 102168, 2021, doi: 10.1016/j.ijinfomgt.2020.102168.
A. Ates and N. Acur, “Making obsolescence obsolete: Execution of digital transformation in a high-tech manufacturing SME,” J. Bus. Res., vol. 152, no. September 2021, pp. 336–348, 2022, doi: 10.1016/j.jbusres.2022.07.052.
A. Gunasekaran, N. Subramanian, and S. Rahman, “Supply chain resilience: Role of complexities and strategies,” Int. J. Prod. Res., vol. 53, no. 22, pp. 6809–6819, 2015, doi: 10.1080/00207543.2015.1093667.
M. Z. Hauschild, S. Kara, and I. Røpke, “Absolute sustainability: Challenges to life cycle engineering,” CIRP Ann., vol. 69, no. 2, pp. 533–553, 2020, doi: 10.1016/j.cirp.2020.05.004.
I. Sifat, “Artificial Intelligence (AI) and Future Retail Investment,” SSRN Electron. J., no. December, 2023, doi: 10.2139/ssrn.4539625.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
