Abstract
Organizations using SAP systems encounter the challenge of enhancing services while minimizing costs and response times. To achieve this, SAP offers a comprehensive range of sustainable cloud solutions in the field of Application Management Services (AMS), integrating machine learning, big data, and smart-machine technologies. This research endeavors to offer an alternative, sustainable approach, utilizing non-cloud-based machine learning to improve the efficiency of ticket classification in SAP AMS. This study presents an SAP Extended Warehouse Management (EWM) related case study in which various machine learning algorithms were applied to real-world SAP ticket data to automatically categorize incident tickets. As warehousing plays a major role in streamlining the supply chain, this approach aligns with the goals of sustainable supply chain process optimization. During the analysis, various classification algorithms were compared to achieve the best metrics. Our research did not just analyze the different algorithms for that specific business problem. The best model was integrated and deployed as well to accomplish a sustainable SAP AMS solution in the field of EWM.
