CONTROL TECHNOLOGIES
Where AI adds value to ERP
According to software specialist, SYSPRO, there are many areas where AI can benefit a manufacturing ERP.
■ Predicting when maintenance will be required to prevent unexpected downtime and to extend equipment lifespan. systems benefit from more computing power, supporting more robust AI applications.
“ AI transforms ERP by adding intelligence, automation and predictive capabilities to core business processes. The integration of AI within ERP enables systems to go beyond simple data management. Predictive analytics in ERP systems allows manufacturers to forecast demand, optimise production planning and reduce waste. Using AI algorithms, ERP systems analyse historical data and current trends to predict future outcomes,” continued PANGUN.
ERP systems can automate routine tasks using AI and robotic process automation. Tasks such as data entry, invoice processing and order updates can be handled automatically. This reduces manual effort and improves efficiency. AI and automation together ensure that teams can focus on strategic activities, instead of repetitive work.
“ AI-enabled ERP also helps to improve inventory management by predicting stock levels and optimising procurement. Instead of reacting to shortages, companies can plan ahead. This improves supply chain management and reduces holding costs,” added PANGUN.
AI-powered ERP systems improve supplychain visibility and coordination. With realtime insights, companies can track supplier performance, identify delays early and optimise logistics for smoother supply chain operations.
However, implementing AI in ERP systems comes with challenges that organisations must address. Common challenges include data quality issues, integration complexity and employee training. AI requires a unified, consistent dataset, but some companies may operate with multiple, disconnected systems that contain overlapping but inconsistent data. These need to be consolidated before implementing AI to avoid misleading predictions. Data security and data bias can also be issues.
“ Since AI became so prominent in 2023, there has been a perception that AI requires large language models( LLMs) and that the bigger a model, the better it is. Only a few companies can afford the infrastructure and other costs of this type of AI. However, small language models( SLMs) are now appearing that offer significantly improved costs and, more importantly, the fine-tuned and specialised knowledge that manufacturers will require,” outlined software specialist, SYSPRO.
Integrating ERP( Enterprise Resource Planning) systems with AI processes stands as a logical next step in the evolution of manufacturing efficiency. ERP systems act as the back of an organisation, managing various aspects such as finance, human resources and supply chain management. When coupled with AI, these systems can unlock new levels of automation, data-driven decision-making and streamlined operations
Human in the loop
Some of the risks with AI have increased interest in a human-in-the-loop( HITL) AI.
“ This concept is exactly what it says: it keeps humans involved in decisions instead of relying solely on AI algorithms to make them autonomously. By making human oversight part of the process for making inventory, production, resource, and other choices, AI becomes a powerful tool that augments data analysis, gut feel and experience to enhance human decisions,” explained Rootstock ERP.
“ For example, AI can gather and analyse huge volumes of sales history, production schedules, supplier information, market trends and more. It can predict future demand, define inventory levels and create efficient production schedules. Human materials requirements planners can then work with that AI output while using experience, broader market trends, company strategy and collaboration with others to approve, reject or revise the AIgenerated production plans,” it added.
The integration of AI in ERP can help to revolutionise how manufacturers operate. However, the adoption of AI remains low.
■ AI can be used to improve demand forecasting, enabling better inventory management.
■ Vision systems incorporating AI can inspect products for defects in real time, ensuring consistent quality and reducing human error.
■ AI algorithms can analyse production data to optimise schedules and allocation of resources, increasing throughput and reducing production costs.
■ AI can provide real-time insights and analytics, enabling manufacturers to make informed decisions based on accurate data.
■ For product design, generative AI can analyse large datasets and existing designs to come up with new design concepts that would be unlikely with traditional methods.
■ A new use case of AI in ERP is Agentic AI, which can adapt and learn from past data, allowing it to perform complex tasks without constant human supervision. It provides the potential for virtual assistants which can understand and process natural language more effectively.
Some manufacturers have already started smaller AI projects to test the feasibility and success of AI in their operations as well as involve their workforce.
“ To harness AI effectively, manufacturers must approach implementation with due care and without rushing into it. By following a structured roadmap, manufacturers can unlock the potential of AI in ERP, driving efficiency, innovation and competitiveness in an increasingly complex market,” concluded Rootstock ERP. n
26 | ismr. net | ISMR July / August 2026