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ERP Analytics- Many companies are now implementing ERP solutions to streamline their processes to bring efficiency through advanced automation of routine tasks. These advanced ERP solutions are now adopting AI-based technologies such as Machine Learning and cloud computing across organisations.
ERP systems are becoming more intelligent, and helping businesses become more adaptable. It is empowering many industries such as Oil & Gas, Manufacturing, and asset-intensive industries to enhance business processes. It can further help to reduce partner disputes, improve cash flow, and gain real-time visibility into the financial data by automating transaction processing and introducing role-based tools to manage exceptions.
For manufacturing companies, an end-to-end ERP solution allows customers to capture, invoice, and capitalise project-driven material, and maintenance costs in one integrated solution. Here, real data analytics features in the ERP solution plays a critical role in helping companies reduce the efforts needed to review documents, track the status of projects, and solve project management issues. Embedded incident reporting workflows can conduct investigations, create actions, and track and update incident status.
How Analytics Is Embedded In ERP
AI-based innovations in ERP are designed to support finance and sales teams to quickly adapt to the current economic climate, explore new business models, and improve strategic decision-making. Recently, Oracle announced crucial updates to Oracle Fusion Cloud Enterprise Resource Planning (ERP) and Oracle Fusion Cloud Enterprise Performance Management (EPM), which can support finance teams to use technologies like AI, digital assistants, and analytics and improve efficiency, decrease costs and have more productivity.
Looking at the new AI and machine learning, analytics and security capabilities of ERP from Oracle, we see there is Predictive Planning feature, which helps companies recognise and utilise trends and analytical insights and patterns in financial and operational data. With access to such insights in real-time, financial companies can see prediction and forecast variances, identify variance patterns, and make plan revisions on the go to enhance the quality and speed of decisions.
