Predict Your Future with SAP S/4HANA Cloud

SAP S/4HANA Cloud reimagines businesses for the digital economy. In this space, SAP has been doing quite a lot of work, recently.  The entire value chain is getting digitized, including the digital core that serves as the foundation for business innovation and optimization. SAP Predictive Analytics is now integrated into the SAP S/4HANA Cloud business processes. The Predictive Analytics integrator is enabling SAP S/4HANA Cloud to easily access the predictive capabilities within the digital core. Customers can use the predictive content delivered out-of-the-box by SAP S/4HANA Cloud or create their own Predictive Scenarios specific to their unique business needs.

Following are some of the different approaches to deliver various predictive services to the customers. There are many more approaches but we are focusing on a few here:

• Delivering embedded predictive analytics in various S/4HANA Cloud business processes where in the SAP Predictive Analytics algorithms are integrated into the S/4HANA digital core.
• Providing business content for the SAP Analytics Cloud that utilizes S/4HANA Cloud functionality which could leverage the in-built predictive services in SAP Analytics Cloud.
• Utilizing the SAP Predictive Analytics tool to create new models as a data scientist and import into the embedded S/4HANA Cloud business processes. We are working on this new functionality which is planned to be released sometime next year.

In the same way, predictive capabilities are being embedded into other SAP products such as Ariba, SuccessFactors, Concur, Fieldglass, Hybris, SAP Cloud Platform, Leonardo etc.,

Embedding Predictive capabilities into SAP Products

Figure 1: Embedding Predictive capabilities into SAP Products

Just to recap, while the end user or the transactional user could leverage predictive capabilities in S/4HANA Cloud business processes, the business user or the business analyst could leverage the SAP Analytics Cloud in performing predictive functions on their business content while connecting to the S/4HANA Cloud and a data scientist or a developer could also leverage additional predictive services by creating newer predictive models and importing them into S/4HANA Cloud.

Let us now focus on a few of the embedded S/4HANA predictive scenarios that were released in 2017 which highlight: “Predicting contract consumption rates” in Operational contract management; “Predictive shipment dates” for stock in transit; “Forecasting number of expected master data changes” and processing time per requests in master data management etc.

Contract Consumption in Procurement
If we look into the Operational contract management, it is essential that buyers have an effective and efficient system support for monitoring contracts. With embedded predictive analytics, the Contract Management app is enhanced with ‘Predicted Consumption Dates’ for each contract to allow Buyers to proactively engage with Suppliers. The key functionality here would include the ability to predict a contract if it is consumed in the next few days and weeks. The predicted results of a contract expiration is based on the customer specific data for yielding the best results. Historical data based on the past contracts and various other factors are used in the regression algorithm techniques.

Figure 2: Example of Quality Contract Consumption app

Predicting when a contract will be consumed helps the buyer to re-negotiate the contract ahead of time. In the bar chart shown below, the blue bar indicates the percentage of the contract consumed, while the green bar shows or predicts when the contract is consumed completely.

Figure 3: Example of Release and Target Amount app

Stock in Transit for Warehouse and Inventory Management
For companies in the world of the basic warehouse and inventory management, issuing and receiving goods from and to their plants, it is important to track the status of the materials in transit in order to take action in case of problems. The “Materials Overdue – Stock in Transit” app gives an overview of the open shipments allowing the business users to take action. With embedded predictive analytics, the app is enhanced with ‘Predicted Shipment Dates’ for each Goods Movement to allow users to take action in managing delivery delays. Here historical data based on the earlier stock transit scenarios and various other factors are fed to the regression algorithm techniques to arrive at a probable delay in the number of days. The delay in the delivery of the stock in transit is made available so that the customer can benefit with additional planning and scheduling actions. On the micro chart below, the first column shows the actual days and delayed days in shipping while the predicted delivery date column provides the forecasted delivery date after applying the predictive algorithm.

Figure 4: Example of Overdue Materials SIT app

As shown in Figure 5 (below) in the screenshot, for those POs, the goods are delivered as predicted by the Figure 4 (above) “Overdue Materials SIT” app, you could now go and manually post the goods receipt and complete the process.

Figure 5: Example of Good Receipts app

A lot has changed with SAP and how predictive analytics could be leveraged in SAP applications to make the world a better place. Happy predicting!