An Analytics-Enabled Framework for Intelligent Procurement and Inventory Management in Government Agencies
DOI:
https://doi.org/10.65141/jessah.v3i1.n3Keywords:
procurement and inventory management, government resource management, business process reengineering, predictive analytics, forecastingAbstract
Transparency, efficiency, and regulatory compliance are essential to effective government resource management. Despite the adoption of digital procurement and inventory systems, many public-sector agencies continue to encounter challenges such as fragmented data, limited inventory visibility, delayed processes, and insufficient decision-support capabilities. These issues can hinder timely planning and efficient utilization of public resources. This study aimed to develop and evaluate an analytics-enabled framework for intelligent procurement and inventory management to support data-driven decision-making in government operations. A design-oriented research approach was employed, incorporating process analysis, framework modeling, prototype development, and simulation-based evaluation. Historical procurement and inventory records were utilized to support diagnostic and predictive analytics, enabling the identification of process bottlenecks, demand forecasting, and analysis of equipment lifecycle patterns. The developed framework integrates operational modules, analytics pipelines, forecasting components, and decision-support dashboards within a unified architecture. Prototype simulation and forecasting evaluation demonstrated the capability of the framework to generate analytical insights that support procurement planning, inventory monitoring, and asset management. In addition, expert validation indicated that the proposed framework is practical and aligned with operational requirements commonly observed in government agencies. In general, the framework demonstrates the potential of analytics-enabled process integration to enhance transparency, accountability, and resource management. Future studies may focus on validating the framework in actual operational environments, extending analytical capabilities, and integrating the framework with existing government information systems.
References
[1] Alam, Md. K., Thakur, O. A., & Islam, F. T. (2023). Inventory management systems of small and medium enterprises in Bangladesh. Rajagiri Management Journal, 18(1), 8–19. https://doi.org/10.1108/ramj-09-2022-0145
[2] Andersson, P. E., Arbin, K., & Rosenqvist, C. (2025). Assessing the value of artificial intelligence (AI) in governmental public procurement. Journal of Public Procurement, 25(1), 120–139. https://doi.org/10.1108/jopp-05-2024-0057
[3] Cocciolo, S., Samaddar, S., & Fazekas, M. (2023). Government analytics using procurement data. World Bank.
[4] Davenport, T. H. (1993). Process innovation: Reengineering work through information technology. Harvard Business School Press.
[5] Goltsos, T. E., Syntetos, A. A., Glock, C. H., & Ioannou, G. (2021). Inventory – forecasting: Mind the gap. European Journal of Operational Research, 299(2). https://doi.org/10.1016/j.ejor.2021.07.040
[6] Guenduez, A. A., Demircioglu, M. A., Mueller, E. M., & Cinar, E. (2025). Digital innovation strategies in the public sector. Research Policy, 54(8), 1–17. https://doi.org/10.1016/j.respol.2025.105274
[7] Hammer, M., & Champy, J. (1993). Reengineering the corporation: A manifesto for business revolution. Harper Business.
[8] Harahap, A. Z. M. K., Rahim, M. K. I. A., Malinjasari, N., Salleh, S. M., & Ma’arof, R. A. (2025). Enhancing the inventory management through demand forecasting. International Journal of Research and Innovation in Social Science, 9(1), 2737–2744. https://doi.org/10.47772/IJRISS.2025.9010221
[9] Karttunen, E., Matela, M., Hallikas, J., & Immonen, M. (2022). Public procurement as an attractive customer: A supplier perspective. International Journal of Operations & Production Management, 42(13), 79–102. https://doi.org/10.1108/ijopm-05-2021-0346
[10] Kundu, O., Uyarra, E., Ortega-Argiles, R., Tirado, M. M., Kitsos, T., & Yuan, P.-Y. (2024). Impacts of policy-driven public procurement: A methodological review. Science and Public Policy, 52(1). https://doi.org/10.1093/scipol/scae058
[11] Mishra, S., Shinde, M., Yadav, A., Ayyub, B., & Rao, A. (2024). An AI-driven data mesh architecture enhancing decision-making in infrastructure construction and public procurement. ArXiv (Cornell University). https://doi.org/10.48550/arxiv.2412.00224
[12] Mokeeva, T., & Yurko, K. (2023). Using digital tools in government procurement analysis: Detecting suspicious purchases with control indicators. International Journal of Technology, 14(8), 1821–1821. https://doi.org/10.14716/ijtech.v14i8.6851
[13] Organisation for Economic Co-operation and Development. (2025). Digital transformation of public procurement. OECD Publishing.
[14] Stütz, J.-D., Karras, O., Oelen, A., & Auer, S. (2023, March 22). A next-generation digital procurement workspace focusing on information integration, automation, analytics, and sustainability. ArXiv.org. https://doi.org/10.48550/arXiv.2303.03882
[15] Thai, K. V. (2019). International handbook of public procurement (2nd ed., Vol. 146, pp. 1–793). Auerbach Publications Taylor & Francis Group.
[16] van der Aalst, W. (2016). Process Mining (2nd ed.). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-662-49851-4
[17] Wamba, S. F., Akter, S., Edwards, A., Chopin, G., & Gnanzou, D. (2015). How “big data” can make big impact: Findings from a systematic review and a longitudinal case study. International Journal of Production Economics, 165, 234–246. https://doi.org/10.1016/j.ijpe.2014.12.031
[18] World Bank. (2021). Government procurement and digital transformation. World Bank.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 JESSAH - Isabela State University Linker: Journal of Education, Social Sciences and Allied Health
This work is licensed under a Creative Commons Attribution 4.0 International License.
