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Original Article
Enhancing Procurement Efficiency through Integrated Master Data Management and System Interoperability
Kartheek Chandra Ambati1
1 Sr. Systems Engineer, CSCS, USA
Published Online: January-April 2026
Pages: 600-607
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20260501071
References
1. P. Siva Kumar and R. Anbanandam, “Theory building on supply chain resilience: A SAP–LAP analysis,” Global Journal of Flexible Systems Management, vol. 21, no. 2, pp. 113–133, 2020.
2. K. Schwertner, “Digital transformation of business,” Trakia Journal of Sciences, vol. 15, no. 1, pp. 388–393, 2017.
3. Batran, A. Erben, R. Schulz, and F. Sperl, Procurement 4.0: A Survival Guide in a Digital, Disruptive World. Frankfurt, Germany: Campus Verlag, 2017.
4. Moretto, S. Ronchi, and A. S. Patrucco, “Increasing the effectiveness of procurement decisions: The value of big data in the procurement process,” International Journal of RF Technologies, vol. 8, no. 3, pp. 79–103, 2017.
5. O. Givehchi, K. Landsdorf, P. Simoens, and A. W. Colombo, “Interoperability for industrial cyber-physical systems: An approach for legacy systems,” IEEE Transactions on Industrial Informatics, vol. 13, no. 6, pp. 3370–3378, 2017.
6. Daraio and W. Glänzel, “Grand challenges in data integration—State of the art and future perspectives: An introduction,” Scientometrics, vol. 108, no. 1, pp. 391–400, 2016.
7. L. Bruck, “Challenges and opportunities of data governance in private and public organizations,” M.S. thesis, Vienna Univ. of Economics and Business, Vienna, Austria, 2017.
8. M. Giannakis and M. Louis, “A multi-agent based system with big data processing for enhanced supply chain agility,” Journal of Enterprise Information Management, vol. 29, no. 5, pp. 706–727, 2016.
9. T. Hazen, J. B. Skipper, J. D. Ezell, and C. A. Boone, “Big data and predictive analytics for supply chain sustainability: A theory-driven research agenda,” Computers & Industrial Engineering, vol. 101, pp. 592–598, 2016.
10. S. L. Koh, S. Saad, and S. Arunachalam, “Competing in the 21st century supply chain through supply chain management and enterprise resource planning integration,” International Journal of Physical Distribution & Logistics Management, vol. 36, no. 6, pp. 455–465, 2006.
11. H. Liang, N. Wang, Y. Xue, and S. Ge, “Unraveling the alignment paradox: How does business–IT alignment shape organizational agility?” Information Systems Research, vol. 28, no. 4, pp. 863–879, 2017.
12. J. Vaidya and J. Campbell, “Multidisciplinary approach to defining public e-procurement and evaluating its impact on procurement efficiency,” Information Systems Frontiers, vol. 18, no. 2, pp. 333–348, 2016.
13. M. Mikalef, A. Pateli, R. S. Batenburg, and R. van de Wetering, “Purchasing alignment under multiple contingencies: A configuration theory approach,” Industrial Management & Data Systems, vol. 115, no. 4, pp. 625–645, 2015.
14. J. P. Saldanha, J. E. Mello, A. M. Knemeyer, and T. A. S. Vijayaraghavan, “Implementing supply chain technologies in emerging markets: An institutional theory perspective,” Journal of Supply Chain Management, vol. 51, no. 1, pp. 5–26, 2015.
15. Raghuvanshi et al., “Chaotic grey wolf optimization based framework for efficient task scheduling in cloud fog computing,” Bulletin of Electrical Engineering and Informatics, vol. 14, no. 3, pp. 2066–2076, 2025, doi: 10.11591/eei.v14i3.8098.
2. K. Schwertner, “Digital transformation of business,” Trakia Journal of Sciences, vol. 15, no. 1, pp. 388–393, 2017.
3. Batran, A. Erben, R. Schulz, and F. Sperl, Procurement 4.0: A Survival Guide in a Digital, Disruptive World. Frankfurt, Germany: Campus Verlag, 2017.
4. Moretto, S. Ronchi, and A. S. Patrucco, “Increasing the effectiveness of procurement decisions: The value of big data in the procurement process,” International Journal of RF Technologies, vol. 8, no. 3, pp. 79–103, 2017.
5. O. Givehchi, K. Landsdorf, P. Simoens, and A. W. Colombo, “Interoperability for industrial cyber-physical systems: An approach for legacy systems,” IEEE Transactions on Industrial Informatics, vol. 13, no. 6, pp. 3370–3378, 2017.
6. Daraio and W. Glänzel, “Grand challenges in data integration—State of the art and future perspectives: An introduction,” Scientometrics, vol. 108, no. 1, pp. 391–400, 2016.
7. L. Bruck, “Challenges and opportunities of data governance in private and public organizations,” M.S. thesis, Vienna Univ. of Economics and Business, Vienna, Austria, 2017.
8. M. Giannakis and M. Louis, “A multi-agent based system with big data processing for enhanced supply chain agility,” Journal of Enterprise Information Management, vol. 29, no. 5, pp. 706–727, 2016.
9. T. Hazen, J. B. Skipper, J. D. Ezell, and C. A. Boone, “Big data and predictive analytics for supply chain sustainability: A theory-driven research agenda,” Computers & Industrial Engineering, vol. 101, pp. 592–598, 2016.
10. S. L. Koh, S. Saad, and S. Arunachalam, “Competing in the 21st century supply chain through supply chain management and enterprise resource planning integration,” International Journal of Physical Distribution & Logistics Management, vol. 36, no. 6, pp. 455–465, 2006.
11. H. Liang, N. Wang, Y. Xue, and S. Ge, “Unraveling the alignment paradox: How does business–IT alignment shape organizational agility?” Information Systems Research, vol. 28, no. 4, pp. 863–879, 2017.
12. J. Vaidya and J. Campbell, “Multidisciplinary approach to defining public e-procurement and evaluating its impact on procurement efficiency,” Information Systems Frontiers, vol. 18, no. 2, pp. 333–348, 2016.
13. M. Mikalef, A. Pateli, R. S. Batenburg, and R. van de Wetering, “Purchasing alignment under multiple contingencies: A configuration theory approach,” Industrial Management & Data Systems, vol. 115, no. 4, pp. 625–645, 2015.
14. J. P. Saldanha, J. E. Mello, A. M. Knemeyer, and T. A. S. Vijayaraghavan, “Implementing supply chain technologies in emerging markets: An institutional theory perspective,” Journal of Supply Chain Management, vol. 51, no. 1, pp. 5–26, 2015.
15. Raghuvanshi et al., “Chaotic grey wolf optimization based framework for efficient task scheduling in cloud fog computing,” Bulletin of Electrical Engineering and Informatics, vol. 14, no. 3, pp. 2066–2076, 2025, doi: 10.11591/eei.v14i3.8098.
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