Optimizing Decision-making in Supply Chain Management using Hybrid Machine Learning Algorithms and Predictive Analytics
Open accessAbstract
This research examines the use of a hybrid approach combining K-Means clustering, Genetic Algorithms (GA), and regression models in SCM to enhance decisionmaking and optimization. Although based on historical data, limiting generalization to other contexts, the hybrid model achieved 90% accuracy, with significantly lower MAE and MSE compared to individual methods. The findings support integrated approaches for improving predictor accuracy and resource allocation. Recommendations include incorporating real-time data, developing analytics and ML capabilities, and maintaining simple, regularly updated decision models. Future studies should test these models across sectors and regions and explore emerging technologies like IoT and blockchain. This study provides empirical support for the practical value of ML in SCM and highlights the importance of interdisciplinary research.
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References 31
- Authors
- R, S. B., & Sundararajan, S.
- Affiliations
- Skyline University Nigeria (NG)
- Venue
- SJCC Management Research Review
- Publisher
- St Josephs College of Commerce
- Published
- 2025-12-30
- Pages
- 70-92
- ISSN
- 2249-4359 (ISSN-L)
- OpenAlex
- W7125492262
- Cited by
- 0
- References
- 31
- FWCI
- 0.00
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- SJCC Management Research Review (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
- Social Sciences › Business, Management and Accounting › Management Information Systems › Big Data and Business Intelligence
- Social Sciences › Business, Management and Accounting › Management Information Systems › Supply Chain and Inventory Management
- Keywords
- supply chain management, decision-making, hybrid machine learning, K-means clustering, genetic algorithm, regression models, predictive analytics, resource allocation, supply chain optimization
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