How the blend of AI, ML, IoT, and Cloud makes an ERP smarter
The new digital business-models and, growth opportunities introduced by them are encouraging businesses to re-assess ERP’s role for their processes and operations. Many have evaluated that legacy EPR systems, which have turned inflexible after years of customization, aren’t delivering what businesses should achieve today. They aren’t able to scale and grow as per the current market requirements.
When legacy ERP systems were first built, the aim was to excel consistency in the production at the expense of customization and responsiveness as per the changing requirements of customers. But today’s business cases are rather different and, thus they require integration of AI and ML. Also, these ERPs need to be in the Cloud to fill the gap created by legacy ERP systems.
Companies grow stably when they are capable of responding quickly to unknown dilemmas with smart and rapid decisions. Legacy ERP systems fail at delivering this sort of functioning. They aren’t designed to generate or deliver the data required most. But when an ERP system is combined with the technologies like AI, ML and Cloud, it provides businesses the flexibility that they need to prioritize growth strategies over IT limitations.
Following are the 10 ways how the blend of AI, ML, IoT, and Cloud makes an ERP smarter:
- AI and ML will turn an ERP into a self-learning knowledge system
When combined with AI, ML, and Cloud, an ERP becomes a self-learning knowledge system that has capabilities to organize a business from shop floor to the top floor. The cloud infrastructure having integrated core ERP web services, apps and the real-time monitoring accelerates the quick learning for the entire system. It includes APIs and several web services that allow a business to connect with many suppliers or buyers outside the walls of a manufacturer.
- AI-ML-enabled virtual agent and an enterprise resource planning system
Businesses are exploring the potentials of virtual agents to redefine many areas in manufacturing operations. Amazon, Alexa, Google Voice, and Cortana are top voice assistants with the potential of being modified as per operational requirements of a business. Businesses engaged in machine manufacturing are piloting voice agents having integration with ERP to provide detailed instructions to streamline configure-to-order and engineer-to-order production.
- Using IoT data for AI and ML enabled could ERP
When an ERP gets the IoT integration, it supports at the data structure level to realize quick wins. A cloud ERP has the capability to capitalize on massive data streams generated by IoT devices. When AI and ML integrated with ERP uses the IoT-based data, the system bridges intelligence gaps faced by many companies pursuing new business models.
- Using AI and ML capabilities to get insights into OEE
AI and ML make an ERP capable of providing insights into how Overall Equipment Effectiveness (OEE) can be enhanced that does not present currently. This functioning is the most preferred requirement of manufacturers which always seek for the greater insights to stabilize and then normalized OEE performance on all shop floors. A cloud based, AL-ML-IoT-enabled ERP system serves as an always learning knowledge system, monitors data in the real-time, and generates much-needed insights for enhancements.
- Using ML capabilities to track and trace product-lots to predict their quality standards
The machine learning technology provides an ERP development with capabilities of tracking and tracing. These capabilities can be applied to predict which lot of product may carry higher chances to fail in the quality check. An ML algorithm learns this from the previous patterns and data sets.
- Filling gaps existing between PLM, CAD, ERP and CRM system by using AI and ML
Businesses using legacy ERPs also use other systems like PLM, CAD, and CRM. In most of the cases, huge gaps exist between these systems whereas a business has to attain a single goal out of them all. The Cloud ERP can help in closing the gaps to a great extent. Conflicts like how engineering dept designs a product using CAD and PLM, how sales & and marketing dept sells it with CRM, and how manufacturing dept builds it with the ERP can be alleviated with the use of cloud-based ERP.
- Decreasing equipment breakdowns and increasing asset utilization
An ERP with machine learning capabilities can decrease equipment breakdowns and increase asset utilization. The ERP analyzes the machine-level data to determine when a specific part has to be serviced or replaced to avoid a breakdown. The data helps in keeping tabs on a machine’s health in order to increase its utilization and keeping its functioning continuous.
- Predicting product-related problems by implementing self-learning algorithm
Legacy ERPs can hardly predict product-related problems. They just go unnoticed, but a cloud ERP with AI/ML capabilities can do it by comparing past incident reports. The self learning ML algorithm uses production incident reports to predict problems related to production.
- Enhancing product quality by using machine learning algorithms
A cloud ERP can be scaled across the entire lifecycle of a product and capture the quality data from suppliers to customers. It provides the actual reports on why products fail and with ML, insights to get there are also made available.
- Getting accurate forecasting for demand and enabled better collaborations
A cloud ERP with improved data latency rates leads a business to have higher forecast accuracy. This is achieved through the high quality data used by a self-learning knowledge system. When the same data is used in sales, marketing, and promotional programs, more fine-tines can be forecasted accurately.