When we use AI to create an accurate diagnostic landscape for the medical field and apply big data analysis to disease prediction and treatment plan formulation; When we use AI driving technology to achieve intelligent transportation, making travel safer and more efficient; When we use AI to interpret ancient literature, language, and recreate lost cultural arts… these are no longer the future of science fiction, but the current reality, a feast of the digital age is unfolding before us
Undoubtedly, AI artificial intelligence is profoundly changing our lives and completely disrupting the operation and management of enterprises. If AI and ERP are integrated, what kind of innovation sparks will burst out?
The answer lies in the integration of SAP ERP public cloud and artificial intelligence.
Next, let’s use several intelligent scenario application examples and customized AI intelligent scenarios in SAP ERP public cloud machine learning to gain a deeper understanding of how machine learning technology can help enterprises innovate business processes and create competitive advantages in SAP ERP public cloud.
Through the AI technology provided in SAP ERP public cloud, enterprises can predict supplier delivery times in purchase orders. This means that companies can proactively understand whether suppliers may delay delivery. Once a potential delay is detected, companies can quickly take measures, such as communicating with suppliers in a timely manner or seeking alternative solutions, to ensure that goods arrive on time and avoid potential additional costs or production interruptions.
By predicting sales performance, enterprises can gain a more comprehensive insight into the trends and changes in sales performance. Based on these predicted results, each department can also make targeted preparations to prepare for future business development. For example, the sales department can refine resource allocation based on forecast results, the production department can prepare and plan production capacity, and the procurement department can optimize suitable supply chain partners in the selection of long-term materials.
Through the machine learning function embedded in the SAP ERP public cloud system, enterprises can achieve accurate prediction of low turnover identification of materials. When the low turnover indicator is 1, it indicates that the consumption of the material will only be 1%, while when the indicator is 100, it indicates that the current inventory will be fully consumed. This intelligent prediction system enables enterprises to adjust inventory levels in a timely manner, reduce backlog, improve inventory turnover, and effectively reduce inventory costs. At the same time, it avoids excessive low turnover or stagnant materials occupying enterprise funds, which helps to prevent potential liquidity issues.
Using AI technology to predict the consumption of quantity contracts can help companies identify which contracts may reach the consumption limit in the short term or require renegotiation. This enables enterprises to take measures in advance to avoid contract consumption exceeding expectations or risks, thereby optimizing procurement plans and resource allocation strategies. Through such intelligent prediction, enterprises can effectively avoid the problem of excess inventory or insufficient supply, improve operational efficiency, and enhance customer satisfaction.
With the prediction of in transit inventory delivery time, enterprises can more accurately plan and schedule logistics transportation, avoiding production interruptions or decreased customer satisfaction caused by delivery delays. If the goods cannot be delivered on time, the enterprise can take corresponding measures, such as adjusting production plans, rearranging logistics transportation, etc., to ensure timely delivery of customer orders.
In addition to the SAP pre configured intelligent scenarios shown above, we can also easily create personalized intelligent scenarios by using open datasets and supported algorithms, providing flexibility and autonomy for enterprises.
When creating intelligent scenarios, business users select the scenario type and the data model used for training, and then specify the training dataset, application dataset, and prediction values according to their needs.
SAP provides a convenient and intuitive intelligent scene unified management platform, simplifying the construction process without worrying about technical complexity, allowing more business users to easily utilize AI technology without relying on professional data scientists or development teams.
Once the intelligent scene is built, business users can directly use internal enterprise data to train the specified data model, ensuring that the model obtains high-quality real data. During the training process, users can filter the data range as needed to ensure that the model receives the most relevant and representative data. In addition, after the training is completed, users can view the training quality score for feedback and further optimization.
SAP ERP public cloud is equipped with powerful artificial intelligence technology, which not only provides enterprises with rich intelligent scenarios and algorithms, but also helps them quickly deploy personalized solutions according to their own needs, creating unique competitiveness for enterprises.
Acloudear has been deeply involved in the SAP ERP public cloud market for many years and has always been committed to providing advanced SAP cloud solutions for growing enterprises, continuously supporting their rapid development, injecting vitality into their digital transformation with cutting-edge technologies such as AI, and empowering sustainable growth.
*Original author: Hou Yue, an expert in SAP ERP public cloud solutions, this article is for sharing purposes only. Welcome to visit our official website to contact us and learn more about SAP ERP public cloud solutions
This article "SAP ERP Public Cloud x AI, Powerful Engine Boosts Enterprise Digital Transformation" by AcloudEAR. We focus on business applications such as cloud ERP.
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