Enhancing Supply Chain Resilience with AI/ML in Oracle Fusion SCM
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V7I4P104Keywords:
AI/Ml, Supply Chain Resilience, Oracle Fusion Scm, Predictive Maintenance, Demand Sensing, Risk AnalysisAbstract
With these increasingly complex yet volatile supply chains, organizations need a strong set of tactics that can help predict disruptions and streamline operations as well as being able to handle the risks effectively. The study involves the implementation of Artificial Intelligence (AI) and Machine Learning (ML) within the supply chain of Oracle Fusion Supply Chain Management (SCM) and how it can be improved through the introduction of the two technologies. The strategy depends on three significant applications namely, predictive maintenance, demand sensing, and supply risk analysis. Predictive maintenance employs Fusion SCM to improve the reliability of equipment by making use of IoT sensor readings and equipment history in the SCM and preventing unexpected malfunctions and planned downtime as well as reducing operational wastace. The advanced machine learning algorithms employed in demand sensing, such as time-series translation, deep learning to extract dynamic signal in the market and enhance their accuracy in demand consequently maintaining optimal levels of services and managing inventory.
Moreover, supply risk analysis uses classification model and scenario-based simulation with the aim of determining the reliability of suppliers, geopolitical risk, and lead-time volatility enabling organizations to predict and efficiently respond to the vulnerability of their networks. A case study that was conducted on enterprise data shows that AI/ML integration into the Oracle Fusion SCM offers significant advantages in the accuracy of forecasts, the visibility of risks, and the resilience of operations versus traditional methods based on rules. The study identifies the ground-breaking contributions of AI-based analytics in enterprise resource planning systems, which has the potential to deliver data-driven, responsive, and absorbent supply chains in response to disruption in the still ever-changing world
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