Reimagining ERP Optimization with Quantum AI: A Feasibility Study Using Oracle Cloud Simulations

Abstract

As corporate operations get increasingly complex, traditional ERP optimization techniques struggle to handle large-scale combinatorial problems (Pinedo, 2016; Johnson & McGeoch, 2002). By allowing things like supply chain routing, inventory balancing, resource allocation, and financial optimization more faster, new applications of quantum computers could result in significant benefits (Farhi et al., 2014; McClean et al., 2016). In this paper, we investigate the usage of Quantum Artificial Intelligence (QAI) in Enterprise Resource Planning (ERP) contexts. For instance, we consider Oracle Fusion Cloud ERP (Keller et al., 2020; Li et al., 2021). Using simulated annealing, variational quantum eigensolvers (VQE), and quantum-enhanced reinforcement learning (QRL) approaches, it tests Oracle's recent advancements in AI and machine learning (Oracle, 2024) against Farhi et al., 2014; McClean et al., 2016; Jerbi et al., 2021). Benchmark scenarios might be keeping real-time inventory tracking, arranging output across several factories, and maximizing the best utilization of capital investments. Particularly in cases of complex data and limited resources, our simulation results suggest that QAI has great potential to speed and improve ERP procedures. We also discuss the technological, financial, and organizational issues that make adoption difficult and compile a strategy for more research and development to make Oracle Fusion ERP environments better with QAI (European Union, 2016; U.S. Congress, 2002; European Union, 2016).