Optimizing Branch Relocation with Predictive and Regression Models
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Abstract
The optimization of branch relocation is a critical challenge for businesses and organizations striving to enhance operational efficiency and customer accessibility. This study explores the integration of predictive analytics and regression models as powerful tools to guide decision-making in branch relocation processes. By leveraging historical data, geographic information, and customer behavior patterns, predictive models enable the identification of optimal branch locations to maximize service reach and profitability. Regression models further refine this process by analyzing quantitative relationships between location-specific factors and branch performance metrics. This combined approach offers a data-driven framework for evaluating potential relocation scenarios, minimizing disruption, and achieving strategic alignment with business objectives. The findings highlight the efficacy of predictive and regression methodologies in facilitating cost-effective, customer-centric branch relocation strategies. This paper also discusses practical implications, challenges, and recommendations for the implementation of these advanced analytical models in real-world scenarios.
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References
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• Jaiswal, S., Shaheen, N., Mangal, A., Singh, D. S. P., Jain, S., & Agarwal, R. (2024). Transforming Performance Management Systems for Future-Proof Workforce Development in the U.S. Journal of Quantum Science and Technology (JQST), 1(3), Apr(287–304). Retrieved from https://jqst.org/index.php/j/article/view/121.
• Bhardwaj, Abhijeet, Nagender Yadav, Jay Bhatt, Om Goel, Prof. (Dr.) Punit Goel, and Prof. (Dr.) Arpit Jain. 2024. Leveraging SAP BW4HANA for Scalable Data Warehousing in Large Enterprises. Integrated Journal for Research in Arts and Humanities 4(6): 143-163. https://doi.org/10.55544/ijrah.4.6.13.
• Abhijeet Bhardwaj, Pradeep Jeyachandran, Nagender Yadav, Prof. (Dr) MSR Prasad, Shalu Jain, Prof. (Dr) Punit Goel. (2024). Best Practices in Data Reconciliation between SAP HANA and BI Reporting Tools. International Journal of Research Radicals in Multidisciplinary Fields, ISSN: 2960-043X, 3(2), 348–366. Retrieved from https://www.researchradicals.com/index.php/rr/article/view/133.
• Abhijeet Bhardwaj, Nagender Yadav, Jay Bhatt, Om Goel, Prof.(Dr.) Arpit Jain, Prof. (Dr) Sangeet Vashishtha. (2024). Optimizing SAP Analytics Cloud (SAC) for Real-time Financial Planning and Analysis. International Journal of Multidisciplinary Innovation and Research Methodology, ISSN: 2960-2068, 3(3), 397–419. Retrieved from https://ijmirm.com/index.php/ijmirm/article/view/144.
• Bhardwaj, Abhijeet, Jay Bhatt, Nagender Yadav, Priya Pandey, S. P. Singh, and Punit Goel. 2024. Implementing Integrated Data Management for Multi-system SAP Environments. International Journal of Research in Modern Engineering and Emerging Technology (IJRMEET) 12(11):1–10. https://www.ijrmeet.org.
• Bhardwaj, A., Jeyachandran, P., Yadav, N., Singh, N., Goel, O., & Chhapola, A. (2024). Advanced Techniques in Power BI for Enhanced SAP S/4HANA Reporting. Journal of Quantum Science and Technology (JQST), 1(4), Nov(324–344). Retrieved from https://jqst.org/index.php/j/article/view/126.
• Bhardwaj, A., Yadav, N., Bhatt, J., Goel, O., Goel, P., & Jain, A. (2024). Enhancing Business Process Efficiency through SAP BW4HANA in Order-to-Cash Cycles. Stallion Journal for Multidisciplinary Associated Research Studies, 3(6), 1–20. https://doi.org/10.55544/sjmars.3.6.1.
• Das, A., Gannamneni, N. K., Jena, R., Agarwal, R., Vashishtha, P. (Dr) S., & Jain, S. (2024). “Implementing Low-Latency Machine Learning Pipelines Using Directed Acyclic Graphs.” Journal of Quantum Science and Technology (JQST), 1(2):56–95. Retrieved from https://jqst.org/index.php/j/article/view/8.
• Mane, Hrishikesh Rajesh, Shyamakrishna Siddharth Chamarthy, Vanitha Sivasankaran Balasubramaniam, T. Aswini Devi, Sandeep Kumar, and Sangeet. "Low-Code Platform Development: Reducing Man-Hours in Startup Environments." International Journal of Research in Modern Engineering and Emerging Technology 12(5):107. Retrieved from www.ijrmeet.org.
• Mane, H. R., Kumar, A., Dandu, M. M. K., Goel, P. (Dr.) P., Jain, P. A., & Shrivastav, E. A. "Micro Frontend Architecture With Webpack Module Federation: Enhancing Modularity Focusing On Results And Their Implications." Journal of Quantum Science and Technology (JQST) 1(4), Nov(25–57). Retrieved from https://jqst.org.
• Kar, Arnab, Ashish Kumar, Archit Joshi, Om Goel, Dr. Lalit Kumar, and Prof. (Dr.) Arpit Jain. 2024. Distributed Machine Learning Systems: Architectures for Scalable and Efficient Computation. International Journal of Worldwide Engineering Research 2(11): 139-157.
• Mali, A. B., Khan, I., Dandu, M. M. K., Goel, P. (Dr) P., Jain, P. A., & Shrivastav, E. A. (2024). Designing Real-Time Job Search Platforms with Redis Pub/Sub and Machine Learning Integration. Journal of Quantum Science and Technology (JQST), 1(3), Aug(184–206). Retrieved from https://jqst.org/index.php/j/article/view/115.