Manuscript Number : SHISRRJ221224

Standardizing Cost Reduction Models Across SAP-Based Financial Planning Systems in Multinational Operations

Authors(5) :-Onyinye Jacqueline Ezeilo, Sandra Orobosa Ikponmwoba, Onyeka Kelvin Chima, Benjamin Monday Ojonugwa, Michael Olumuyiwa Adesuyi

This paper addresses the critical need for standardizing cost reduction models within SAP-based financial planning systems in multinational enterprises. Variations in cost management approaches across global subsidiaries often result in inefficiencies, data inconsistencies, and compliance risks, undermining financial control and operational agility. By analyzing SAP's financial modules and the challenges of multinational contexts, this study proposes a unified framework grounded in principles of cost accounting, process standardization, and cross-border financial harmonization. The designed model incorporates clearly defined cost drivers, KPIs, benchmarking templates, and automation rules configured within SAP, supporting consistent and transparent cost optimization. A phased implementation strategy that balances global standardization with local adaptability is outlined, emphasizing stakeholder engagement, change management, and governance. Furthermore, risk mitigation and performance monitoring mechanisms are integrated to ensure system resilience and sustained financial improvements. The paper concludes with implications for enhanced scalability, predictability, and cross-functional collaboration in multinational financial planning and highlights future research avenues, including AI-driven cost optimization and hybrid ERP integrations.

Authors and Affiliations

Onyinye Jacqueline Ezeilo
Independent researcher, Abuja, Nigeria
Sandra Orobosa Ikponmwoba
Independent Researcher, Abuja, Nigeria
Onyeka Kelvin Chima
Africa Capital Alliance, Ikoyi, Lagos. Nigeria
Benjamin Monday Ojonugwa
Independent researcher, Lagos, Nigeria
Michael Olumuyiwa Adesuyi
First Bank of Nigeria Ltd, Kano, Nigeria

SAP Financial Planning, Cost Reduction Standardization, Multinational Enterprises, Financial Governance, Process Automation, Cross-Border Financial Harmonization

  1. "Advances in global services and retail management: Volume 2," Advances in global services and retail management: Volume 2, Sep. 2021, doi: 10.5038/9781955833035.
  2. E. O. Alonge, N. L. Eyo-Udo, B. C. Ubanadu, A. I. Daraojimba, E. D. Balogun, and K. O. Ogunsola, "Digital transformation in retail banking to enhance customer experience and profitability," Iconic Research and Engineering Journals, 2021.
  3. M. O. Nwaozomudoh, E. Odio, E. Kokogho, T. A. Olorunfemi, I. E. Adeniji, and A. Sobowale, "Developing a Conceptual Framework for Enhancing Interbank Currency Operation Accuracy in Nigeria’s Banking Sector," vol. 2, pp. 481–494, doi: 10.54660/.IJMRGE.2021.2.1.481-494.
  4. F. U. Ojika, W. O. Owobu, O. A. Abieba, O. J. Esan, B. C. Ubamadu, and A. I. Daraojimba, "Integrated framework for enhancing sales enablement through advanced CRM and analytics solutions," 2022.
  5. R. Kepczynski, R. Jandhyala, G. Sankaran, and A. Dimofte, "What Makes Integrated Business Planning," Management for Professionals, vol. Part F626, pp. 31–72, 2018, doi: 10.1007/978-3-319-75665-3_3/FIGURES/28.
  6. E. O. Alonge, N. L. Eyo-Udo, B. Chibunna, and A. I. D. Ubanadu, "Digital Transformation in Retail Banking to Enhance Customer Experience and Profitability," ICONIC RESEARCH AND ENGINEERING JOURNALS, vol. 4, no. 09, pp. 169–188, 2021.
  7. "Ajiga: AI-powered HR analytics: Transforming workforce... - Google Scholar." Accessed: May 11, 2025. Online]. Available: https://scholar.google.com/scholar?cluster=8169453348357691513&hl=en&oi=scholarr
  8. A. Ifesinachi Daraojimba, F. Uche Ojika, W. Oseremen Owobu, O. Anthony Abieba, O. Janet Esan, and B. Chibunna Ubamadu, "A Conceptual Framework for AI-Driven Digital Transformation: Leveraging NLP and Machine Learning for Enhanced Data Flow in Retail Operations," 2021. Online]. Available: https://www.researchgate.net/publication/390928712
  9. "Bristol-Alagbariya: Integrative HR approaches in... - Google Scholar." Accessed: May 11, 2025. Online]. Available: https://scholar.google.com/scholar?cluster=575703752053816023&hl=en&oi=scholarr
  10. B. I. Adekunle, E. C. Chukwuma-Eke, E. D. Balogun, and K. O. Ogunsola, "Machine learning for automation: Developing data-driven solutions for process optimization and accuracy improvement," Mach Learn, vol. 2, no. 1, 2021.
  11. C. Rudin, "Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead," Nat Mach Intell, vol. 1, no. 5, pp. 206–215, May 2019, doi: 10.1038/S42256-019-0048-X;SUBJMETA=117,4002,4014,4045,531,639,705;KWRD=COMPUTER+SCIENCE,CRIMINOLOGY,SCIENCE.
  12. "Agho: Sustainable pore pressure prediction and its... - Google Scholar." Accessed: May 11, 2025. Online]. Available: https://scholar.google.com/scholar?cluster=4983019839357889401&hl=en&oi=scholarr
  13. S. S. Roy, R. Chopra, K. C. Lee, C. Spampinato, and B. Mohammadi-Ivatlood, "Random forest, gradient boosted machines and deep neural network for stock price forecasting: A comparative analysis on South Korean companies," International Journal of Ad Hoc and Ubiquitous Computing, vol. 33, no. 1, pp. 62–71, 2020, doi: 10.1504/IJAHUC.2020.104715;PAGE:STRING:ARTICLE/CHAPTER.
  14. S. Chowdhury, A. Covic, R. Y. Acharya, S. Dupee, F. Ganji, and D. Forte, "Physical security in the post-quantum era: A survey on side-channel analysis, random number generators, and physically unclonable functions," J Cryptogr Eng, vol. 12, no. 3, pp. 267–303, Sep. 2022, doi: 10.1007/s13389-021-00255-w.
  15. E. O. Alonge, N. L. Eyo-Udo, B. C. Ubanadu, A. I. Daraojimba, and E. D. Balogun, "Enhancing data security with machine learning: A study on fraud detection algorithms," Journal of Data Security and Fraud Prevention, vol. 7, no. 2, pp. 105–118, 2021.
  16. Chianumba, I. E. C., M. N., F. A. Y., A. Y. Osamika, and D, "Exploring the role of AI and machine learning in improving healthcare diagnostics and personalized medicine," C., Ikhalea, N., Mustapha, A. Y., Forkuo, A. Y., & Osamika, D. (2023). Exploring the role of AI and machine learning in improving healthcare diagnostics and personalized medicine. Journal of Frontiers in Multidisciplinary Research, vol. 2023), 2023.
  17. O. Saidani Neffati et al., "Migrating from traditional grid to smart grid in smart cities promoted in developing country," Sustainable Energy Technologies and Assessments, vol. 45, p. 101125, Jun. 2021, doi: 10.1016/J.SETA.2021.101125.
  18. "Bristol-Alagbariya: Integrative HR approaches in... - Google Scholar." Accessed: May 11, 2025. Online]. Available: https://scholar.google.com/scholar?cluster=13278782840988498877&hl=en&oi=scholarr
  19. "Okolie: Implementing robotic process automation (RPA)... - Google Scholar." Accessed: May 11, 2025. Online]. Available: https://scholar.google.com/scholar?cluster=9666179811413792884&hl=en&oi=scholarr
  20. A. Hübner, J. Hense, and C. Dethlefs, "The revival of retail stores via omnichannel operations: A literature review and research framework," Eur J Oper Res, vol. 302, no. 3, pp. 799–818, Nov. 2022, doi: 10.1016/J.EJOR.2021.12.021.
  21. Osamika, A. D., K.-A. B. S., M. M. C., A. Y. Ikhalea, and N, "Machine learning models for early detection of cardiovascular diseases: A systematic review," S., Kelvin-Agwu, M. C., Mustapha, A. Y., & Ikhalea, N. (2021). Machine learning models for early detection of cardiovascular diseases: A systematic review. IRE Journals, vol. 2021), 2021, Online]. Available: https://doi.org/IRE.1702780
  22. F. U. Ojika, W. O. Owobu, O. A. Abieba, O. J. Esan, B. C. Ubamadu, and A. I. Daraojimba, "The Impact of Machine Learning on Image Processing: A Conceptual Model for Real-Time Retail Data Analysis and Model Optimization," 2022.
  23. F. U. Ojika, W. O. Owobu, O. A. Abieba, O. J. Esan, B. C. Ubamadu, and A. I. Daraojimba, "A Conceptual Framework for AI-Driven Digital Transformation: Leveraging NLP and Machine Learning for Enhanced Data Flow in Retail Operations," 2021.
  24. Ajiga and D. I, "Strategic framework for leveraging artificial intelligence to improve financial reporting accuracy and restore public trust," I. (2021). Strategic framework for leveraging artificial intelligence to improve financial reporting accuracy and restore public trust. International Journal of Multidisciplinary Research and Growth Evaluation, vol. 2021), 2021.
  25. "Ajiga: AI-powered HR analytics: Transforming workforce... - Google Scholar." Accessed: May 11, 2025. Online]. Available: https://scholar.google.com/scholar?cluster=8169453348357691513&hl=en&oi=scholarr
  26. "Adewale: Leveraging blockchain for enhanced risk... - Google Scholar." Accessed: May 11, 2025. Online]. Available: https://scholar.google.com/scholar?cluster=4054054058610420343&hl=en&oi=scholarr
  27. "Ige: Developing multimodal AI systems for comprehensive... - Google Scholar." Accessed: May 11, 2025. Online]. Available: https://scholar.google.com/scholar?cluster=16007807647139226972&hl=en&oi=scholarr
  28. J. Ali, R. Khan, N. Ahmad, and I. Maqsood, "Random Forests and Decision Trees," 2012, Accessed: May 11, 2025. Online]. Available: www.IJCSI.org
  29. M. Awad and R. Khanna, "Support Vector Regression," Efficient Learning Machines, pp. 67–80, 2015, doi: 10.1007/978-1-4302-5990-9_4.
  30. A. Natekin, A. K.-F. in neurorobotics, and undefined 2013, "Gradient boosting machines, a tutorial," frontiersin.org, vol. 7, no. DEC, 2013, doi: 10.3389/FNBOT.2013.00021/FULL.
  31. K. T. Yang, "Artificial Neural Networks (ANNs): A new paradigm for thermal science and engineering," J Heat Transfer, vol. 130, no. 9, Sep. 2008, doi: 10.1115/1.2944238/467746.
  32. "Bristol-Alagbariya: Integrative HR approaches in... - Google Scholar." Accessed: May 11, 2025. Online]. Available: https://scholar.google.com/scholar?cluster=575703752053816023&hl=en&oi=scholarr
  33. "Adeniji: Customized financial solutions: Conceptualizing... - Google Scholar." Accessed: May 11, 2025. Online]. Available: https://scholar.google.com/scholar?cluster=5533215540651632137&hl=en&oi=scholarr
  34. C. Aguilar-Palacios, S. Munoz-Romero, and J. L. Rojo-Alvarez, "Cold-Start Promotional Sales Forecasting through Gradient Boosted-Based Contrastive Explanations," IEEE Access, vol. 8, pp. 137574–137586, 2020, doi: 10.1109/ACCESS.2020.3012032.
  35. Esan, U. O. J., O. O. T., O. O., G. O. Etukudoh, and E. A, "Procurement 4.0: Revolutionizing supplier relationships through blockchain, AI, and automation: A comprehensive framework," J., Uzozie, O. T., Onaghinor, O., Osho, G. O., & Etukudoh, E. A. (2022). Procurement 4.0: Revolutionizing supplier relationships through blockchain, AI, and automation: A comprehensive framework. Journal of Frontiers in Multidisciplinary Research, vol. 2022), 2022.
  36. C. Tudor, R. Sova, A. Gegov, and R. Jafari, "Benchmarking GHG Emissions Forecasting Models for Global Climate Policy," Electronics 2021, Vol. 10, Page 3149, vol. 10, no. 24, p. 3149, Dec. 2021, doi: 10.3390/ELECTRONICS10243149.
  37. B. I. Adekunle, E. C. Chukwuma-Eke, E. D. Balogun, and K. O. Ogunsola, "Machine learning for automation: Developing data-driven solutions for process optimization and accuracy improvement," Mach Learn, vol. 2, no. 1, p. 18, 2021.
  38. N. J. Isibor, C. Paul-Mikki Ewim, A. I. Ibeh, E. M. Adaga, N. J. Sam-Bulya, and G. O. Achumie, "A Generalizable Social Media Utilization Framework for Entrepreneurs: Enhancing Digital Branding, Customer Engagement, and Growth," International Journal of Multidisciplinary Research and Growth Evaluation, vol. 2, no. 1, pp. 751–758, 2021, doi: 10.54660/.IJMRGE.2021.2.1.751-758.
  39. N. J. Isibor, C. P. M. Ewim, A. I. Ibeh, E. M. Adaga, N. J. Sam-Bulya, and G. O. Achumie, "A generalizable social media utilization framework for entrepreneurs: Enhancing digital branding, customer engagement, and growth," International Journal of Multidisciplinary Research and Growth Evaluation, 2021.
  40. G. Freeman and N. M. Radziwill, "Voice of the Customer (VoC): A Review of Techniques to Reveal and Prioritize Requirements for Quality," vol. 2018, no. 3, pp. 1–29, 2018.
  41. M. R. Machado, S. Karray, and I. T. De Sousa, "LightGBM: An effective decision tree gradient boosting method to predict customer loyalty in the finance industry," 14th International Conference on Computer Science and Education, ICCSE 2019, pp. 1111–1116, Aug. 2019, doi: 10.1109/ICCSE.2019.8845529.
  42. L. Zhao, "Event Prediction in the Big Data Era: A Systematic Survey," ACM Comput Surv, vol. 54, no. 5, Jun. 2021, doi: 10.1145/3450287/SUPPL_FILE/3450287-CORRIGENDUM.PDF.
  43. E. C. Chukwuma-Eke, O. Y. Ogunsola, and N. J. Isibor, "A conceptual approach to cost forecasting and financial planning in complex oil and gas projects," International Journal of Multidisciplinary Research and Growth Evaluation, 2022.
  44. J. Hou, C. Wang, and S. Luo, "How to improve the competiveness of distributed energy resources in China with blockchain technology," Technol Forecast Soc Change, vol. 151, p. 119744, Feb. 2020, doi: 10.1016/J.TECHFORE.2019.119744.
  45. E. D. Balogun, K. O. Ogunsola, and A. S. Ogunmokun, "Developing an advanced predictive model for financial planning and analysis using machine learning," ICONIC RESEARCH AND ENGINEERING JOURNALS, vol. 5, no. 11, p. 320, 2022.

Publication Details

Published in : Volume 5 | Issue 2 | March-April 2022
Date of Publication : 2022-03-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 175-190
Manuscript Number : SHISRRJ221224
Publisher : Shauryam Research Institute

ISSN : 2581-6306

Cite This Article :

Onyinye Jacqueline Ezeilo, Sandra Orobosa Ikponmwoba, Onyeka Kelvin Chima, Benjamin Monday Ojonugwa, Michael Olumuyiwa Adesuyi, "Standardizing Cost Reduction Models Across SAP-Based Financial Planning Systems in Multinational Operations", Shodhshauryam, International Scientific Refereed Research Journal (SHISRRJ), ISSN : 2581-6306, Volume 5, Issue 2, pp.175-190, March-April.2022
URL : https://shisrrj.com/SHISRRJ221224

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