Predictive Analytics for Supply Chain Disruption Using SAP HANA and Deep Learning
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V7I2P105Keywords:
Supply Chain Management, Predictive Analytics, Sap Hana, Deep Learning, Disruption Management, Neural NetworksAbstract
Global supply chain disruptions have gained popularity in the past few years, with reasons like geopolitical tensions, pandemics, and natural disasters. Such disturbances may have considerable effects on manufacturing, logistics, and inventory management. The ability to overcome these challenges proactively is provided via predictive analytics, which can be enabled through the power of the latest tools (including SAP HANA computing and deep learning algorithms). The paper addresses the incorporation of the in-memory data processing functions in SAP HANA with deep learning models relating to the prediction of disruptions in the supply chain and their management. By utilising comprehensive data collection, preprocessing, model training, and evaluation, we demonstrate that predictive analytics can enhance the resilience of supply chains. The research enhances the emerging sphere of supply chain digitization by introducing a real-time predictive system that is compatible with enterprise systems based on SAP HANA
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