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Table 1: A comparative overview of the machine learning techniques commonly used for process automation in SAP, along with their key applications and benefit 2.4 Challenges Integrated Machine Learning in SAP While machine learning can be a very powerful addition to your SAP environment, there are hurdles to getting that benefit. Inserting machine learning models into current SAP systems is an important hurdle. Therefore, machine learning models are not easily applicable to SAP environments because their processes, business logic, and data structures are highly customized and contain massive amounts of historical legacy data, which need to

Table 1 A comparative overview of the machine learning techniques commonly used for process automation in SAP, along with their key applications and benefit 2.4 Challenges Integrated Machine Learning in SAP While machine learning can be a very powerful addition to your SAP environment, there are hurdles to getting that benefit. Inserting machine learning models into current SAP systems is an important hurdle. Therefore, machine learning models are not easily applicable to SAP environments because their processes, business logic, and data structures are highly customized and contain massive amounts of historical legacy data, which need to

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riven by the digital transformation of industries, one of thé primary drivers behind the growing adoption o achine learning in the SAP environment achine learning as they attempt to become is that. More and more organizations are realizing the value o more data-driven and bring insights from large data sets in a forn at humans can act upon. With its cloud | iodules to business process capabilities — ERP portfolio, SAP has got everything — from a ton of busines: which is just the platform for embedding the machine learnins gorithms that can automatically analyze the data, detect anomalies, predict the trends, or optimize workflows 1 addition, improvements in machine learning tools such as deep learning and natural language processins NLP) allow increasing the current process automation capabilities of SAP systems by tackling more elaborate sks with a higher level of accuracy.
riven by the digital transformation of industries, one of thé primary drivers behind the growing adoption o achine learning in the SAP environment achine learning as they attempt to become is that. More and more organizations are realizing the value o more data-driven and bring insights from large data sets in a forn at humans can act upon. With its cloud | iodules to business process capabilities — ERP portfolio, SAP has got everything — from a ton of busines: which is just the platform for embedding the machine learnins gorithms that can automatically analyze the data, detect anomalies, predict the trends, or optimize workflows 1 addition, improvements in machine learning tools such as deep learning and natural language processins NLP) allow increasing the current process automation capabilities of SAP systems by tackling more elaborate sks with a higher level of accuracy.
A qualitative research method, case studies, was chosen to provide a more detailed exploration of how SAP is using machine learning in particular organizations. Specifically, the machine learning case studies selected come from the manufacturing, finance, and retail sectors across a broad range of business processes, including supply chain optimization, fraud detection, and customer relationship management (CRM).
A qualitative research method, case studies, was chosen to provide a more detailed exploration of how SAP is using machine learning in particular organizations. Specifically, the machine learning case studies selected come from the manufacturing, finance, and retail sectors across a broad range of business processes, including supply chain optimization, fraud detection, and customer relationship management (CRM).
FIG 4:-The enharicement of the SAP process through ML integration. Besides analyzing individual case studies, we conducted a comparative analysis that can present common trends and differences among different industries. For example, we compared the impact of machine learning on supply chain optimization in manufacturing to its impact on fraud detection in the finance sector. The main contribution of this comparative approach was to show how various business functions benefit from machine learning in SAP environments.
FIG 4:-The enharicement of the SAP process through ML integration. Besides analyzing individual case studies, we conducted a comparative analysis that can present common trends and differences among different industries. For example, we compared the impact of machine learning on supply chain optimization in manufacturing to its impact on fraud detection in the finance sector. The main contribution of this comparative approach was to show how various business functions benefit from machine learning in SAP environments.
The table below presents a summary of the process efficiency improvements reported by the case study organizations:
The table below presents a summary of the process efficiency improvements reported by the case study organizations:
Related topics:
Machine LearningPredictive AnalyticsProcess Automation
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