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As digital transformation accelerates, enterprises are beginning to reassess the role of ERP. After years of rigid customization, traditional ERP pursues consistency of production too much, ignoring changes in customer needs, resulting in a lack of flexibility in the system, which has been unable to meet the growth needs of today's digital business model. At present, artificial intelligence (AI) and machine learning are developing rapidly and have become essential helpers for many enterprises. Cloud ERP suppliers may need the help of these two kings if they want to solve the problems of traditional ERP systems!

Saving traditional ERP systems with greater intelligence and insight

For new business models to succeed, companies need to respond quickly to unexpected situations and make timely response strategies. However, this is almost impossible for traditional ERP systems because traditional ERP technology stacks and systems are not developed based on the most important data in delivery.

Successful business models are necessarily based on successful cloud ERP. Powered by technology, cloud ERP platforms and applications provide flexibility for businesses. Many people integrate traditional ERP systems using application programming interfaces (APIs) to capture incremental data. In today's cloud ERP era, re-architecting IT based on cloud platforms can lead to faster speeds, greater scale, and customer transparency, but that doesn't seem to be the case.

As ERP systems continue to learn and improve, new business models begin to flourish, and this is one of the greatest roles ERP platforms can play. Cloud platforms can provide stronger integration options and greater flexibility to customize applications and improve availability.

Here are 10 ways AI can improve cloud ERP:

Cloud ERP platforms need to create a knowledge-learning system that is deployed with artificial intelligence, from field operations to architectural design, and across supplier networks. Create a cloud-based infrastructure that integrates core ERP Web services, applications, and real-time monitoring to provide a steady stream of data for AI and machine learning algorithms to accelerate learning throughout the system. Cloud ERP platform integration roadmaps need to include APIs and Web services to connect with vendor and buyer systems, as well as integration with legacy ERP systems to collate and analyze the data they generate.

Boston Consulting Group, Artificial Intelligence for the Factory of the Future, April 2018

From voice systems to advanced diagnostics, virtual agents have the potential to redefine the manufacturing landscape. Apple's Siri, Amazon's Alexa, Google Voice, and Microsoft Cortana all have the potential to be modified to simplify operational tasks and processes and provide guidance for complex tasks. Machine manufacturers, for example, are experimenting with voice agent-provided work orders to streamline configuration-to-order, production-to-order workflows. Amazon has successfully partnered with automakers and won numerous awards.

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Design the Internet of Things (IoT) at the data structure level. By designing at the data structure level, cloud ERP platforms can leverage the massive data streams generated by IoT devices to provide IoT data to AI and machine learning applications, bridging the intelligence gap that many companies have in pursuing new business models. Capgemini provided an IoT use case analysis (shown below) highlighting how production asset maintenance and asset follow-up can be implemented. Among them, cloud ERP platform can accelerate the whole process through IoT support.

Source: Capgemini Internet of Things (IOT) Study, Unlocking the Business Value of IoT in Operations

Artificial intelligence can improve overall equipment efficiency (OEE), but the effect is not obvious at present. Through AI and machine learning, manufacturers will have the opportunity to gain insight into OEE and then smooth OEE performance. When cloud ERP platforms become a continuous learning system, real-time monitoring of machines and production assets can better maintain the smooth operation of the workshop.

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Machine learning algorithms are designed to be trackable and traceable to predict supplier and product quality. Machine learning excels at finding patterns in different data sets through constraint-based algorithms. Vendors vary widely in their quality and delivery plan performance levels, and using machine learning, applications can be tracked to determine the risk size of suppliers.

Cloud ERP vendors can bridge the configuration gap between PLM, CAD, ERP and CRM systems through AI and machine learning. A successful product configuration strategy relies on a life-cycle-based view of the product configuration, which relieves not only engineers of design stress, but also sales and marketing, as well as manufacturers of conflicts in building the product. AI can enable lifecycle configuration management, simplify CPQ and product configuration policies in the process, and avoid time waste.

With high-quality data, the accuracy of demand forecasting can be improved and better collaboration with suppliers can be achieved based on machine learning forecasting models. By creating a self-learning knowledge system, cloud ERP vendors can dramatically improve data latency and thus prediction accuracy.

Reduce equipment failures and improve asset utilization by analyzing machine data to determine when a given component needs to be replaced. Using sensors equipped with IP addresses, a steady stream of data can be captured at the health level of each machine. Cloud ERP vendors have the opportunity to capture machine-level data, allowing machine learning techniques to look through the entire dataset of the production floor to find patterns in production performance, which is extremely important in the event of a machine failure.

Using production incident reports to predict possible production problems needs to be implemented on a cloud ERP platform. Local aircraft manufacturers, for example, are using predictive models and machine learning to compare past accident reports. Traditional ERP systems fail to detect these problems and eventually slow down or even stop production.

10. Improve product quality by aggregating and analyzing supplier inspection, quality control, return material review (RMA) and product failure data through machine learning algorithms. Cloud ERP platforms are uniquely positioned to scale throughout the product lifecycle and capture high-quality data from suppliers to customers. For traditional ERP systems, manufacturers typically analyze scrap by type. So it's now necessary to figure out why the product failed, and machine learning happens to do just that.

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