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A Comprehensive Framework for High-Value Analytical Integration to Optimize Network Resource Allocation and Strategic Growth
Subject area: Science,Engineering and Technology · Area of research: Network Resource Optimization
Abstract
The increasing complexity of global networks demands innovative approaches to optimize resource allocation while simultaneously driving strategic growth. Traditional methods, often limited by fragmented data systems and reactive decision-making, are insufficient for sustaining competitiveness in dynamic environments. This proposes a comprehensive framework for high-value analytical integration designed to align advanced data-driven insights with network resource optimization and long-term organizational strategy. The framework integrates four core dimensions: data infrastructure, advanced analytics, resource optimization mechanisms, and strategic alignment. First, a robust data infrastructure ensures seamless integration of real-time and historical data across heterogeneous systems, enhancing interoperability and decision accuracy. Second, advanced analytical capabilities?encompassing predictive, prescriptive, and machine learning models?enable proactive scenario planning, risk management, and opportunity identification. Third, resource optimization mechanisms apply dynamic allocation algorithms and cost?benefit models to maximize efficiency while maintaining resilience against uncertainties. Finally, a strategic alignment layer connects operational insights with corporate objectives, embedding feedback loops that drive continuous improvement and sustainable performance. The proposed framework offers significant benefits, including improved operational efficiency, enhanced agility in managing disruptions, and strengthened pathways for sustainable, innovation-led growth. It also highlights critical challenges such as data silos, integration costs, and organizational resistance, while suggesting phased adoption, governance models, and leadership engagement as mitigation strategies. Looking forward, emerging technologies such as edge computing, blockchain, and generative AI are identified as key enablers that will further expand the framework?s applicability. By uniting analytical integration with resource allocation and strategy, the framework provides organizations with a structured and scalable pathway to achieve efficiency, resilience, and competitiveness in complex, resource-constrained environments.
References
[1] Ajonbadi, H.A., Lawal, A.A., Badmus, D.A. and Otokiti, B.O., 2014. Financial control and organisational performance of the Nigerian small and medium enterprises (SMEs): A catalyst for economic growth. American Journal of Business, Economics and Management, 2(2), pp.135-143.
[2] Akinbola, O.A. and Otokiti, B.O., 2012. Effects of lease options as a source of finance on profitability performance of small and medium enterprises (SMEs) in Lagos State, Nigeria. International Journal of Economic Development Research and Investment, 3(3), pp.70-76.
[3] Akinsulire, A.A., 2012. Sustaining competitive advantage in a small-sized animation & movie studio in a developing economy like Nigeria: A case study of Mighty Jot Studios (Unpublished master's thesis). The University of Manchester, Manchester, England.
[4] Alwan, Z., Greenwood, D. and Gledson, B., 2015. Rapid LEED evaluation performed with BIM based sustainability analysis on a virtual construction project. Construction Innovation, 15(2), pp.134-150.
[5] Amos, A.O., Adeniyi, A.O. and Oluwatosin, O.B., 2014. Market based capabilities and results: inference for telecommunication service businesses in Nigeria. European Scientific Journal, 10(7).
[6] Antonenko, P.D., 2015. The instrumental value of conceptual frameworks in educational technology research. Educational Technology Research and Development, 63(1), pp.53-71.
[7] Baghersad, M. and Zobel, C.W., 2015. Economic impact of production bottlenecks caused by disasters impacting interdependent industry sectors. International Journal of Production Economics, 168, pp.71-80.
[8] Battleson, D.A., West, B.C., Kim, J., Ramesh, B. and Robinson, P.S., 2016. Achieving dynamic capabilities with cloud computing: An empirical investigation. European Journal of Information Systems, 25(3), pp.209-230.
[9] Ben-Oz, C. and Greve, H.R., 2015. Short-and long-term performance feedback and absorptive capacity. Journal of management, 41(7), pp.1827-1853.
[10] Bereznoy, A., 2017. Corporate foresight in multinational business strategies. Форсайт, 11(1 (eng)), pp.9-22.
[11] Bettini, Y. and Head, B.W., 2016. Governance structures and strategies to support innovation and adaptability. Cooperative Research Centre for Water Sensitive Cities: Melbourne, Australia.
[12] Bhavnani, S.P., Parakh, K., Atreja, A., Druz, R., Graham, G.N., Hayek, S.S., Krumholz, H.M., Maddox, T.M., Majmudar, M.D., Rumsfeld, J.S. and Shah, B.R., 2017. 2017 Roadmap for innovation—ACC health policy statement on healthcare transformation in the era of digital health, big data, and precision health: a report of the American College of Cardiology Task Force on Health Policy Statements and Systems of Care. Journal of the American College of Cardiology, 70(21), pp.2696-2718.
[13] Biswas, S. and Sen, J., 2017. A proposed architecture for big data driven supply chain analytics. arXiv preprint arXiv:1705.04958.
[14] Bosley, L.C.C., 2017. The “New” Normal: Instability Risk Assessment in an Uncertainty-Based Strategic Environment. International Studies Review, 19(2), pp.206-227.
[15] Carayannis, E.G., Grigoroudis, E., Del Giudice, M., Della Peruta, M.R. and Sindakis, S., 2017. An exploration of contemporary organizational artifacts and routines in a sustainable excellence context. Journal of Knowledge Management, 21(1), pp.35-56.
[16] Cenamor, J., Sjödin, D.R. and Parida, V., 2017. Adopting a platform approach in servitization: Leveraging the value of digitalization. International Journal of Production Economics, 192, pp.54-65.
[17] Cordes, J.J., 2017. Using cost-benefit analysis and social return on investment to evaluate the impact of social enterprise: Promises, implementation, and limitations. Evaluation and program planning, 64, pp.98-104.
[18] Delicato, F., Pires, P.F. and Batista, T., 2017. Resource management for Internet of Things. Springer International Publishing:
[19] Eltantawy, R.A., 2016. The role of supply management resilience in attaining ambidexterity: a dynamic capabilities approach. Journal of Business & Industrial Marketing, 31(1), pp.123-134.
[20] Fang, B. and Zhang, P., 2016. Big data in finance. In Big data concepts, theories, and applications (pp. 391-412). Cham: Springer International Publishing.
[21] Frisk, J.E. and Bannister, F., 2017. Improving the use of analytics and big data by changing the decision-making culture: A design approach. Management Decision, 55(10), pp.2074-2088.
[22] Garay, J., Cartagena, R., Esensoy, A.V., Handa, K., Kane, E., Kaw, N. and Sadat, S., 2015. Strategic analytics: Towards fully embedding evidence in healthcare decision-making. Healthc Q, 17, pp.23-7.
[23] Gudivada, V.N., 2017. Data analytics: fundamentals. In Data analytics for intelligent transportation systems (pp. 31-67). Elsevier.
[24] Head, B.W., 2016. Toward more “evidence‐informed” policy making?. Public administration review, 76(3), pp.472-484.
[25] Heil, S. and Enkel, E., 2015. Exercising opportunities for cross-industry innovation: How to support absorptive capacity in distant knowledge processing. International Journal of Innovation Management, 19(05), p.1550048.
[26] Houe, T. and Murphy, E., 2017. A study of logistics networks: the value of a qualitative approach. European Management Review, 14(1), pp.3-18.
[27] Houston, C., Gooberman-Hill, S., Mathie, R., Kennedy, A., Li, Y. and Baiz, P., 2017. Case study for the return on investment of internet of things using agent-based modelling and data science. Systems, 5(1), p.4.
[28] Kache, F. and Seuring, S., 2017. Challenges and opportunities of digital information at the intersection of Big Data Analytics and supply chain management. International journal of operations & production management, 37(1), pp.10-36.
[29] Kittlaus, H.B. and Fricker, S.A., 2017. Software product management. Berlin: SpringerVerlag GmbH Germany, 298.
[30] Komninos, N., 2016. Smart environments and smart growth: Connecting innovation strategies and digital growth strategies. International Journal of Knowledge-Based Development, 7(3), pp.240-263.
[31] Kortmann, S., 2015. The mediating role of strategic orientations on the relationship between ambidexterity‐oriented decisions and innovative ambidexterity. Journal of Product Innovation Management, 32(5), pp.666-684.
[32] Lawal, A.A., Ajonbadi, H.A. and Otokiti, B.O., 2014. Leadership and organisational performance in the Nigeria small and medium enterprises (SMEs). American Journal of Business, Economics and Management, 2(5), p.121.
[33] Lawal, A.A., Ajonbadi, H.A. and Otokiti, B.O., 2014. Strategic importance of the Nigerian small and medium enterprises (SMES): Myth or reality. American Journal of Business, Economics and Management, 2(4), pp.94-104.
[34] Lee, V. and Lo, A., 2016. Sustainability: A cross-industry study. Journal of Applied Management and Entrepreneurship, 21(4), p.31.
[35] Lengnick-Hall, C.A. and Beck, T.E., 2016. Resilience capacity and strategic agility: Prerequisites for thriving in a dynamic environment. In Resilience Engineering Perspectives, Volume 2 (pp. 61-92). CRC Press.
[36] Li, F., Nucciarelli, A., Roden, S. and Graham, G., 2016. How smart cities transform operations models: A new research agenda for operations management in the digital economy. Production Planning & Control, 27(6), pp.514-528.
[37] Liang, H., Wang, N., Xue, Y. and Ge, S., 2017. Unraveling the alignment paradox: how does business—IT alignment shape organizational agility?. Information Systems Research, 28(4), pp.863-879.
[38] Maksimović, M. and Vujović, V., 2017. Internet of things based e-health systems: ideas, expectations and concerns. In Handbook of large-scale distributed computing in smart healthcare (pp. 241-280). Cham: Springer International Publishing.
[39] Martin, G.P., Wiseman, R.M. and Gomez‐Mejia, L.R., 2016. Going short‐term or long‐term? CEO stock options and temporal orientation in the presence of slack. Strategic Management Journal, 37(12), pp.2463-2480.
[40] Ngulube, P., Mathipa, E.R. and Gumbo, M.T., 2015. Theoretical and conceptual frameworks in the social and management sciences. Addressing research challenges: Making headway in developing researchers, 43, p.66.
[41] Olwal, T.O., Djouani, K. and Kurien, A.M., 2016. A survey of resource management toward 5G radio access networks. IEEE Communications Surveys & Tutorials, 18(3), pp.1656-1686.
[42] Oni, O., Adeshina, Y.T., Iloeje, K.F. and Olatunji, O.O., ARTIFICIAL INTELLIGENCE MODEL FAIRNESS AUDITOR FOR LOAN SYSTEMS. Journal ID, 8993, p.1162.
[43] Osabuohien, F.O., 2017. Review of the environmental impact of polymer degradation. Communication in Physical Sciences, 2(1).
[44] Otokiti, B.O. and Akorede, A.F., 2018. Advancing sustainability through change and innovation: A co-evolutionary perspective. Innovation: Taking creativity to the market. Book of Readings in Honour of Professor SO Otokiti, 1(1), pp.161-167.
[45] Otokiti, B.O., 2012. Mode of entry of multinational corporation and their performance in the Nigeria market (Doctoral dissertation, Covenant University).
[46] Otokiti, B.O., 2017. A study of management practices and organisational performance of selected MNCs in emerging market-A Case of Nigeria. International Journal of Business and Management Invention, 6(6), pp.1-7.
[47] Peng, M., Wang, C., Li, J., Xiang, H. and Lau, V., 2015. Recent advances in underlay heterogeneous networks: Interference control, resource allocation, and self-organization. IEEE Communications Surveys & Tutorials, 17(2), pp.700-729.
[48] Piccinini, E., Hanelt, A., Gregory, R. and Kolbe, L., 2015. Transforming industrial business: The impact of digital transformation on automotive organizations.
[49] Porter, M.E. and Heppelmann, J.E., 2015. How smart, connected products are transforming companies. Harvard business review, 93(10), pp.96-114.
[50] Prabhu, C.S.R., 2017. OVERVIEW-FOG COMPUTING AND INTERNET-OF-THINGS (IOT). EAI endorsed transactions on cloud systems, 3(10).
[51] Puche, J., Ponte, B., Costas, J., Pino, R. and De la Fuente, D., 2016. Systemic approach to supply chain management through the viable system model and the theory of constraints. Production planning & control, 27(5), pp.421-430.
[52] Raj, P. and Kumar, S.A., 2017. Big data analytics processes and platforms facilitating smart cities. Smart cities: Foundations, principles, and applications, pp.23-52.
[53] Richey Jr, R.G., Morgan, T.R., Lindsey-Hall, K. and Adams, F.G., 2016. A global exploration of big data in the supply chain. International Journal of Physical Distribution & Logistics Management, 46(8), pp.710-739.’
[54] Sin, C., Veiga, A. and Amaral, A., 2016. European policy implementation and higher education. Analysing the Bologna Process.
[55] Soltanpoor, R. and Sellis, T., 2016, September. Prescriptive analytics for big data. In Australasian database conference (pp. 245-256). Cham: Springer International Publishing.
[56] Sparrow, P.R. and Makram, H., 2015. What is the value of talent management? Building value-driven processes within a talent management architecture. Human resource management review, 25(3), pp.249-263.
[57] Sussan, F. and Acs, Z.J., 2017. The digital entrepreneurial ecosystem. Small business economics, 49(1), pp.55-73.
[58] Tan, S., De, D., Song, W.Z., Yang, J. and Das, S.K., 2016. Survey of security advances in smart grid: A data driven approach. IEEE Communications Surveys & Tutorials, 19(1), pp.397-422.
[59] Wang, Y. and Hajli, N., 2017. Exploring the path to big data analytics success in healthcare. Journal of Business Research, 70, pp.287-299.
[60] Yousafzai, A., Gani, A., Noor, R.M., Sookhak, M., Talebian, H., Shiraz, M. and Khan, M.K., 2017. Cloud resource allocation schemes: review, taxonomy, and opportunities. Knowledge and information systems, 50(2), pp.347-381.
[61] Zollo, M., Bettinazzi, E.L., Neumann, K. and Snoeren, P., 2016. Toward a comprehensive model of organizational evolution: Dynamic capabilities for innovation and adaptation of the enterprise model. Global Strategy Journal, 6(3), pp.225-244.
How to cite this paper
@article{1710817,
author = {Adesola Abdul-Gafar Arowogbadamu, Stanley Tochukwu Oziri, Omorinsola Bibire Seyi-Lande},
title = {A Comprehensive Framework for High-Value Analytical Integration to Optimize Network Resource Allocation and Strategic Growth},
journal = {Iconic Research And Engineering Journals},
year = {2018},
volume = {1},
number = {11},
pages = {76-91},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1710817.pdf},
abstract = {The increasing complexity of global networks demands innovative approaches to optimize resource allocation while simultaneously driving strategic growth. Traditional methods, often limited by fragmented data systems and reactive decision-making, are insufficient for sustaining competitiveness in dynamic environments. This proposes a comprehensive framework for high-value analytical integration designed to align advanced data-driven insights with network resource optimization and long-term organizational strategy. The framework integrates four core dimensions: data infrastructure, advanced analytics, resource optimization mechanisms, and strategic alignment. First, a robust data infrastructure ensures seamless integration of real-time and historical data across heterogeneous systems, enhancing interoperability and decision accuracy. Second, advanced analytical capabilities?encompassing predictive, prescriptive, and machine learning models?enable proactive scenario planning, risk management, and opportunity identification. Third, resource optimization mechanisms apply dynamic allocation algorithms and cost?benefit models to maximize efficiency while maintaining resilience against uncertainties. Finally, a strategic alignment layer connects operational insights with corporate objectives, embedding feedback loops that drive continuous improvement and sustainable performance. The proposed framework offers significant benefits, including improved operational efficiency, enhanced agility in managing disruptions, and strengthened pathways for sustainable, innovation-led growth. It also highlights critical challenges such as data silos, integration costs, and organizational resistance, while suggesting phased adoption, governance models, and leadership engagement as mitigation strategies. Looking forward, emerging technologies such as edge computing, blockchain, and generative AI are identified as key enablers that will further expand the framework?s applicability. By uniting analytical integration with resource allocation and strategy, the framework provides organizations with a structured and scalable pathway to achieve efficiency, resilience, and competitiveness in complex, resource-constrained environments.},
month = {May},
}