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This book explains how to embed Artificial Intelligence (AI) in digitized business processes of ERP software by solving the two related substantial challenges: how can Artificial Intelligence be systematically integrated into ERP business processes for ease of consumption, and how can Artificial Intelligence be made enterprise-ready by covering ERP qualities like compliance, lifecycle management, extensibility, or scalability?
As a general introduction, the first part of this book takes the reader through a historical journey towards intelligent ERP systems. In addition, reference processes and a reference architecture for ERP systems are proposed which build the foundation for the suggested subsequent solution concept, including a method for operationalizing intelligence for ERP business processes. Subsequently, in the second part detailed concepts of embedding Artificial Intelligence into ERP software are proposed. In this context the suggested solution architecture is depicted, and specific topics are resolved like data integration, model validation, explainability, data protection and privacy, model degradation and performance. In the last part an implementation framework is suggested which enables the previously introduced concepts and harmonizes the development and operations of Artificial Intelligence ERP applications. This part concludes with case studies considering Artificial Intelligence scenarios of SAP S/4HANA in the areas of logistics, finance and sales which apply the defined solution approach and shows its real-world feasibility.
In the context of ERP systems, Artificial Intelligence is utilized to imbue business processes with intelligence, increasing the automation and optimization levels. Data Science techniques are applied for this purpose. Data Science’s objectives can be distilled into two main points: solving a specific problem and deriving insights from a dataset, with the latter serving as a means to achieve the former. As the volume of data generated and stored grows exponentially, leveraging data to resolve problem statements becomes increasingly appealing, offering more opportunities for Data Science to provide solutions.
Methodology
Part I ERP Fundamentals
Intelligent ERP
ERP Reference Processes
ERP Reference Architecture
ERP Reference Artificial Intelligence Technology
Part II Concepts for Embedding Artifcial Intelligence
Business Requirements and Application Patterns
Solution Architecture
Life Cycle Management
Data Integration
Data Protection and Data Privacy
Configuration
Extensibility
Model Degradation
Explanation of Results
Workload Management and Performance
Legal Auditing.
Model Validation
Interface Design
Embedding Generative AI
Part III Implementation Framework and Case Studies
Implementation Framework
Sales and Research
Sourcing and Procurement
Inventory and Supply Chain
Finance
Epilogue: Ethical Considerations