More than just transaction management: What the future holds for ERP

Enterprise resource planning (ERP) is a foundational tool for many organisations. Just earlier this year, Forbes reported that 95% of companies surveyed found that ERP had improved business processes. Now, with artificial intelligence (AI) budgets ramping up – IDC, for example, forecasts spending in the Asia-Pacific to reach USD90.7 billion by 2027 – ERP’s value in enabling more agile supply chain management could be multiplied manifold.

For example, Plassein Industry Sdn Bhd is a Malaysian plastic injection moulding manufacturer who produces products for the OEM and aftermarket industries, with the majority of their manufacturing on a ‘make-to-order’ basis. For manufacturers like Plassein Industry, an ERP system is critical to streamline customers’ demand, supply chain costs, manage raw material sourcing – while maintaining lean inventory holdings, aligning lead times for efficient production planning, and reduced product lead times for customers. As Stella Thean, Sales Manager of Plassein Industry, stated, “Epicor is very important to Plassein Industry and the customers we serve. Our business functions more effectively with Epicor providing us with good visibility of our operations.”

The Building Blocks of AI Integration – Data Management and GovernanceAs the repository of some of the cleanest data within an organisation, the ERP system plays a critical role in business functions. However, harnessing AI’s potential hinges on robust data management practices around the ERP – because AI is only as good as the data it is fed. For instance, using associated applications for reporting and decision-making requires mature data governance skills.

Data management and governance are essential for effectively integrating AI into ERP systems. High-quality and reliable data can drive accurate insights and inform decision-making. Moreover, organisations must recognise that AI systems process large amounts of data, creating significant cybersecurity implications. Organisations need to ensure that, as they deploy AI, they are equipped with a robust security arsenal to defend their data – particularly, sensitive data – from cyber threats.

Pushing Forward with Digital TransformationHowever, juggling between leveraging this technology effectively and securing data can be challenging. This is where the establishment of data lakes becomes a foundational step. Data lakes are centralised stockrooms that hold large volumes of raw, unstructured, and semi-structured data in its native format. This setup enables exploratory analytical functions, which is essential for testing new algorithms, generating insights, and addressing a broader set of business challenges.

Having your own data lake allows you to apply AI tools and methodologies directly to the data, which empowers:• Rapid Integration and AI Readiness – A data lake enables quick consolidation of data across different business units, source systems, and subsidiaries. This quick integration creates tangible value, especially when pursuing buy-and-build strategies. This integration happens within weeks – significantly faster than the months it might take to build a traditional data warehouse. Such speed in setting up a data foundation is crucial for leveraging AI analytics tools when they become available, making data readily accessible for AI applications, and providing the business with a significant competitive edge.• Scalability – Inherently scalable, data lakes are designed for low-cost storage solutions. This architecture allows organisations to store a high volume of data at relatively low prices. This scalability is particularly valuable for addressing the 'volume' aspect of big data, ensuring that storage solutions can grow alongside data without incurring prohibitive costs.• Advanced Analytics and Machine Learning – Data lakes serve as a rich feeding ground for machine learning algorithms, which are inherently data hungry. The sheer volume and variety of data within a data lake fuels model development and unlocks the true potential of AI and predictive analytics. Having all data types in one location facilitates discovery of new patterns or insights across various sources, driving innovation and enhancing decision-making.• Security and Compliance – Private data lakes provide businesses with complete control over their data security and governance. This control is vital for compliance with regulations like General Data Protection Regulation (GDPR), Health Insurance Portability and Accountability Act (HIPAA), and industry-specific standards. By establishing robust security measures, organisations can effectively mitigate risks and protect sensitive information.• Data Sovereignty – In a private data lake, companies retain ownership and control over their data, ensuring that sensitive information remains within the secure boundaries of their infrastructure.• Customisation and Control – Private data lakes can be customised to fit the organisation’s unique needs, providing the flexibility to integrate with existing systems and workflows.• Enhancing Visibility and CollaborationAI enhances visibility, collaboration, and efficiency across the organisation. It streamlines communication processes and keeps stakeholders informed with intelligent alerts, especially in the case of supply chain disruption. As noted in the Plassein case, “The data and information we get from Epicor have improved our manufacturing and production processes, making us efficient and cost-effective.”

While AI is capable of helping organisations transform their operations, overcome traditional challenges, and seize new opportunities, it is not a silver bullet. Instead, organisations must prepare to navigate this new era of AI that is heavily dependent on big data. This means harnessing AI’s potential hinges on having the right solutions and partnerships in place to ensure a secure, compliant, and efficient data infrastructure. Only then can they fully leverage the advantages that AI has to offer.