10 Ways To Improve Cloud ERP With AI & Machine Learning
Legacy Enterprise Resource Planning (ERP) systems aren’t delivering what digital business models need today to scale and grow because of the constant customization in recent years. What they need is Cloud based ERP systems to handle the load so companies can respond quickly to immediate needs with smart decisions for new digital business models to succeed. By integrating Artificial Intelligence (AI) and machine learning into their platforms, Cloud ERP providers can fill the gap legacy ERP systems simply can’t. Cloud platforms provide more integration options and more flexibility to customize applications and better usability.
Here are 10 ways to improve Cloud ERP with AI and machine learning, and bridging the information gap with legacy ERP systems:
- Cloud ERP platforms need to create and strengthen a self-learning knowledge system that orchestrates AI and machine learning from the shop floor to the top floor and across supplier networks.
- Virtual agents have the potential to redefine many areas of manufacturing operations, from pick-by-voice systems to advanced diagnostics.
- Design in the Internet of Things (IoT) support at the data structure level to realize quick wins as data collection pilots go live and scale.
- AI and machine learning can provide insights into how Overall Equipment Effectiveness (OEE) can be improved that aren’t apparent today.
- Designing machine learning algorithms into track-and-traceability to predict which lots from which suppliers are most likely to be of the highest or lowest quality.
- Cloud ERP providers need to pay attention to how they can help close the configuration gap that exists between PLM, CAD, ERP and CRM systems by using AI and machine learning.
- Improving demand forecasting accuracy and enabling better collaboration with suppliers based on insights from machine learning-based predictive models is attainable with higher quality data.
- Reducing equipment breakdowns and increasing asset utilization by analyzing machine-level data to determine when a given part needs to be replaced.
- Implementing self-learning algorithms that use production incident reports to predict production problems on assembly lines needs to happen in Cloud ERP platforms.
- Improving product quality by having machine learning algorithms aggregate, analyze and continually learn from supplier inspection, quality control, Return Material Authorization (RMA) and product failure data.
