Trend Note: Enterprise Resource Planning
Percentages reflect 2023, 2024 R&D project times
Enterprise Resource Planning (ERP) and Business Analytics (30%)
As a bellwether for the ERP industry, a significant portion of research and development efforts are focused on enhancing core ERP and business analytics capabilities. Observations from industry work indicate deep expertise in data management and storage, with innovations around virtual database tables, implicit data partitioning, and efficient data compression techniques. This allows ERP systems to handle large volumes of enterprise data and provide fast, reliable access to critical business information.
The R&D also highlights a focus on query optimization and processing, which is essential for powering advanced business analytics and reporting features. Innovations like partition-aware query processing and column-based data handling enable ERP and analytics solutions to deliver powerful insights and support complex decision-making. For example, one project describes a technique for pruning irrelevant table partitions from a calculation scenario, improving query performance and efficiency.
Cloud Computing and Microservices (20%)
As the ERP industry continues to shift towards cloud-based deployments, a significant portion of R&D efforts are dedicated to cloud computing and microservices architectures. Observations indicate a focus on developing cloud-native application development and deployment capabilities, such as container-based solutions and plugin management systems. This allows ERP vendors to provide customers with more easily adoptable and integrated ERP functionality within their evolving cloud landscapes.
Furthermore, the R&D explores techniques for building scalable and elastic cloud infrastructure, addressing challenges like multi-tenancy and data isolation. For instance, one project describes a method for implicitly partitioning data across a distributed database system, enabling efficient management of data ranges and high-performance query processing.
Machine Learning and Artificial Intelligence (15%)
The integration of machine learning (ML) and artificial intelligence (AI) technologies into enterprise applications and workflows is another key area of research and development in the ERP industry. Observations from the industry's work demonstrate efforts to leverage ML/AI capabilities to enhance various aspects of ERP and business analytics offerings.
For example, one project describes a method for auto-detection of favorable and unfavorable outliers using unsupervised clustering, which can help organizations identify anomalies and outliers within their business data. Another project focuses on the use of attention mechanisms in natural language processing to improve entity matching and filtering, enhancing features like search and data integration within ERP systems.
Security and Privacy (15%)
As enterprise data becomes increasingly valuable and sensitive, the ERP industry has placed a strong emphasis on ensuring the security and privacy of its solutions. Observations from the industry's R&D efforts cover a range of security and compliance-related innovations, such as secure data processing in untrusted environments, identity and access management, and vulnerability detection and mitigation.
Application Lifecycle Management (10%)
Recognizing the importance of efficient application development, deployment, and maintenance, the ERP industry has dedicated a portion of its R&D efforts to improving the lifecycle management of enterprise software. Observations from the industry's work cover various aspects of this, including code analysis and testing, transport management for software updates, and maintenance processes like database migration and schema management.
Internet of Things (IoT) and Sensor Networks (5%)
While the ERP industry is increasingly integrating with IoT and sensor-driven data sources, the observations suggest that this area represents a relatively smaller portion of the overall research and development efforts compared to the other areas. However, the work in this area still demonstrates the industry's interest in leveraging IoT data for enhanced ERP and business analytics capabilities.
Process Automation and Workflow Management (5%)
Rounding out the research and development focus in the ERP industry is the area of process automation and workflow management, which can significantly enhance the efficiency and effectiveness of enterprise-wide business processes. Observations from the industry's work explore innovations like declarative workflow definition, task scheduling and resource optimization, and the integration of robotic process automation into ERP applications.
Bottom Line
The ERP industry's research and development efforts demonstrate a strong focus on enhancing core ERP and business analytics capabilities, with innovations around virtual database tables, implicit data partitioning, and efficient data compression techniques. This allows ERP systems to handle large volumes of enterprise data and provide fast, reliable access to critical business information.
The industry is also dedicating significant R&D efforts to cloud computing and microservices architectures, developing cloud-native application development and deployment capabilities to enable more easily adoptable and integrated ERP functionality within evolving cloud landscapes.
Furthermore, the integration of machine learning (ML) and artificial intelligence (AI) technologies into enterprise applications and workflows is a key area of research, with the industry leveraging these capabilities to enhance various aspects of ERP and business analytics offerings, such as auto-detection of anomalies and improved entity matching and filtering.
Recognizing the importance of security and privacy as enterprise data becomes increasingly valuable and sensitive, the ERP industry has placed a strong emphasis on innovations in areas like secure data processing, identity and access management, and compliance management.
Collectively, these research and development trends position the ERP industry to deliver increasingly sophisticated, adaptable, and secure business management solutions that can help organizations streamline operations, gain deeper insights, and stay competitive in the digital age.
Examples
Innovations in virtual database tables, implicit data partitioning, and efficient data compression:
Importance: Allows ERP systems to handle large volumes of enterprise data and provide fast, reliable access to critical business information, enabling organizations to make data-driven decisions.
Development of cloud-native application deployment capabilities, such as container-based solutions and plugin management systems:
Importance: Enables ERP vendors to provide customers with more easily adoptable and integrated ERP functionality within their evolving cloud landscapes, supporting the industry's shift towards cloud-based deployments.
Leveraging machine learning and artificial intelligence to enhance ERP and business analytics offerings:
Importance: Helps organizations identify anomalies, improve data integration, and derive deeper insights from their business data, driving more informed decision-making and competitive advantages.
Innovations in secure data processing, identity and access management, and compliance management:
Importance: Addresses the growing need for robust security and privacy measures to protect increasingly valuable and sensitive enterprise data, ensuring regulatory compliance and building customer trust.
Improvements in application lifecycle management, including code analysis, software updates, and database maintenance:
Importance: Enhances the efficiency, reliability, and integrity of ERP systems, reducing downtime and ensuring seamless updates and upgrades for organizations.