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Theoretical Perspectives on Intelligent Order Promising: Bridging ERP and AI-Driven Supply Chain Planning

by Rahul Kumar Mishra
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 47
Year of Publication: 2025
Authors: Rahul Kumar Mishra
10.5120/ijca2025925789

Rahul Kumar Mishra . Theoretical Perspectives on Intelligent Order Promising: Bridging ERP and AI-Driven Supply Chain Planning. International Journal of Computer Applications. 187, 47 ( Oct 2025), 18-25. DOI=10.5120/ijca2025925789

@article{ 10.5120/ijca2025925789,
author = { Rahul Kumar Mishra },
title = { Theoretical Perspectives on Intelligent Order Promising: Bridging ERP and AI-Driven Supply Chain Planning },
journal = { International Journal of Computer Applications },
issue_date = { Oct 2025 },
volume = { 187 },
number = { 47 },
month = { Oct },
year = { 2025 },
issn = { 0975-8887 },
pages = { 18-25 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number47/theoretical-perspectives-on-intelligent-order-promising-bridging-erp-and-ai-driven-supply-chain-planning/ },
doi = { 10.5120/ijca2025925789 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-10-23T00:18:06.636620+05:30
%A Rahul Kumar Mishra
%T Theoretical Perspectives on Intelligent Order Promising: Bridging ERP and AI-Driven Supply Chain Planning
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 47
%P 18-25
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Order Promising (OP) has emerged as a critical capability in modern supply chains, serving as the interface between customer demand and supply chain execution. Traditionally, OP has relied on rule-based Available-to-Promise (ATP) and Capable-to-Promise (CTP) models embedded in Enterprise Resource Planning (ERP) systems. However, the increasing complexity of global supply chains, demand volatility, and the rise of digital commerce have exposed the limitations of static promise mechanisms. This paper develops a theoretical framework for Intelligent Order Promising (IOP) that integrates ERP systems with advanced planning platforms, artificial intelligence (AI), and predictive analytics. The study examines OP not only as a logistics execution tool but also as a strategic lever for customer experience, profitability, and resilience. The framework conceptualizes IOP as a dynamic decision-making layer that balances promise reliability, supply chain efficiency, and customer-centricity. The paper contributes to the literature by positioning IOP as the bridge between transactional systems (ERP) and cognitive supply chain planning, highlighting directions for future research in digital and sustainable supply chains.

References
  1. Kilger, C., & Schneeweiss, C. (2000). Supply Chain Management and Advanced Planning: Concepts, Models, Software and Case Studies. Springer.
  2. Bennett, N., & Lemoine, G. J. (2014). What VUCA really means for you. Harvard Business Review, 92(1–2), 27.
  3. Chen, F., & Zhao, L. (2007). A profit-maximizing model for order promising. Operations Research Letters, 35(2), 199–208.
  4. Stadtler, H. (2005). Supply chain management and advanced planning—basics, overview, and challenges. European Journal of Operational Research, 163(3), 575–588.
  5. Saberi, S., Kouhizadeh, M., Sarkis, J., & Shen, L. (2019). Blockchain technology and its relationships to sustainable supply chain management. International Journal of Production Research, 57(7), 2117–2135.
  6. Madhavaram, S., & Varadarajan, R. (2008). Marketing strategy modeling: Theoretical perspectives and decision frameworks. Journal of the Academy of Marketing Science, 36(1), 69–83.
  7. Golgeci, I., Gligor, D. M., & Tatoglu, E. (2020). A relational view of environmental performance: What role do supply chain resilience and agility play? Journal of Business Research, 117, 118–129.
  8. Rong, A., Akkerman, R., & Grunow, M. (2008). An optimization approach for managing the order promising process under capacity constraints. Computers & Operations Research, 35(11), 3396–3414.
  9. Meyr, H., Wagner, M., & Rohde, J. (2005). Structure of Advanced Planning Systems. In H. Stadtler & C. Kilger (Eds.), Supply Chain Management and Advanced Planning (pp. 109–128). Springer.
  10. Kilger, C., Schneeweiss, C., & Zimmermann, J. (2017). Advanced Planning in Supply Chains: SAP APO Case Studies. Springer.
  11. Chen, J. (2018). Profitable-to-promise decisions in supply chain management: Models and methods. International Journal of Production Economics, 197, 1–12.
  12. Galbraith, J. R. (1974). Organization design: An information processing view. Interfaces, 4(3), 28–36.
  13. Chopra, S., & Meindl, P. (2020). Supply Chain Management: Strategy, Planning, and Operation (8th ed.). Pearson.
  14. Power, D. J. (2002). Decision Support Systems: Concepts and Resources for Managers. Greenwood Publishing Group.
  15. Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509–533.
  16. Yin, R. K. (2014). Case Study Research: Design and Methods (5th ed.). Sage Publications.
Index Terms

Computer Science
Information Sciences

Keywords

Order Promising; Supply Chain Management; Decision Support Systems; Optimization; Supply Chain Visibility; Artificial Intelligence; Resilient Supply Chains

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