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As announced in my last blog, this year, all efforts for SAP S/4HANA Cloud are targeted towards powering the customer journey to the intelligent enterprise. We have been off to a great start with 1802 already and now with 1805 we are not only keeping the momentum going, but we are taking it to the next level! Just in this single release we ship 4 new Machine Learning highlights.

Before diving right into what the new release has in store for you, I want to announce that a new book on Procurement in SAP S/4HANA has been published. If you are interested in Procurement, this is a good way to familiarize yourself with our solution in more detail. You can get a preread here.

Propose Resolution for Invoice Payment Block

This Machine Learning highlight supports the realization of cash discounts offered by suppliers. Even though the invoicing process is already a highly digitized and automated process, there are still many exceptions that require manual interaction from purchasers or accounts payable experts. Whenever a blocking reason for a supplier invoice occurs, the responsible purchaser has to solve the underlying issue manually. To not jeopardize the cash discount, fast remedy is required. Of course, those exceptions are laborious and expensive.

Leveraging an out-of-the-box predictive model for the resolution of payment blocks, this intelligent innovation significantly reduces the manual effort related to such exceptions and enables the payment of the invoice still in time to meet the cash discount timeline. For all blocked invoiced due to quantity reasons, the delay of the goods receipts is forecasted utilizing the predictive modeling integration. Based on this prediction, the accounts payable accountant can make an informed decision on whether it is smart to pay the supplier invoice in order to realize the cash discount. For organizations with huge purchasing volumes that can result in millions of savings and be a true game changer!

Reduce Free-Text Items Using Machine Learning

Our second Machine Learning highlight, helps procurement departments to execute on the common goal to reduce free-text items. The challenges associated with free-text items are obvious: organizations frequently struggle with accurate reporting on purchasing spend and cost control for those items. With this innovation, whenever there are many free-text items for similar material generated, a Machine Learning algorithm will automatically propose to create a new catalog item for the respective material.

Based on our new situation handling framework we introduced in 1802 (in case you missed it, you can catch up here), purchasers will then automatically receive a notification for such new catalog item proposals. Clicking on the notification, a situation page will open where you will receive even more details.

As you can see on the screenshot, the situation page has an embedded analytics screen that displays how often the specific material has been used as a free-text item in purchase requisitions over the last months. On the right portion of the screen, it displays information on the respective item; this information will be used to create the new catalog item. First you can decide on the name you want to give the new catalog entry in the “Description” field; the system will already provide you some proposals based on the descriptions of the previous free-text items. Secondly, you have the option to select the supplier; again, the system will provide you with different options. After this information has been entered, it only takes one click to create the new catalog item. Once that is finalized, purchasers can start buying without having to create a free-text item for the respective material.

Reducing the number of free-text items to the minimum, brings about a lot of benefits, such as enhanced and accurate reporting on purchasing spend and higher cost control.

Propose Material Group

The third Machine Learning highlight is also dealing with free-text items. In practice, free-text items are often assigned to wrong material groups, which in turn also causes the assignment of wrong accounting information in the request. Therefore, purchase requisitions with free-text items are frequently assigned to the wrong operational purchaser. The incorrectly assigned purchaser then needs to change the material group and assign the correct accounting information manually so that the corresponding colleague can take over. Subsequently the invoice can then be posted to the right GL account. As you can imagine, those manual tasks are time consuming and expensive.

With 1805, we ship a Machine Learning algorithm that will suggest the appropriate material group during the requisition process. As a consequence, manual efforts related to the wrong assignment of material groups can be eliminated, boosting the efficiency of the requisitioning of free-text items.

Smart Buying

The new smart buying functionality is not only relevant for procurement departments, but rather targets all employees in an organization. Most employees will have to order a laptop, cellphone or office material at one point. Typically, users that don`t purchase material for their work frequently, struggle with finding the right catalog with the desired product. Based on the natural language interaction functionality of the SAP, endusers throughout the organization can now easily order goods they require using text or voice. You can watch a video of the functionality here.

Looking for a specific product, users can ask the SAP CoPilot for this material. Now this is where the SAP CoPilot drastically improves user experience: in the background the SAP CoPilot, will browse all catalogs maintained in the system (also SAP Ariba catalogs, as long as an OCI web service is in place) for the respective search term. Subsequently, the SAP CoPilot will display a list of all relevant catalog entries. This list can be narrowed down further, if the user asks the SAP CoPilot to filter, e.g. for those items less than 400€. Once the user has selected the desired catalog item he wants to purchase, he can ask the SAP CoPilot to create a purchase requisition for it. The SAP CoPilot will then automatically create it. As you can see this is a completely transformed experience of buying material and only minimal input from the enduser is required – all powered by our intelligent assistant for our Cloud ERP.

Central Purchasing

We also ship a big enhancement for SAP S/4HANA Cloud for Central Procurement, which is the official new name for SAP S/4HANA Cloud for Extended Procurement. You can read up on functionality delivered previously here and here. Now with 1805, we also enable central purchasing, which is an integral building block of the procurement hub solution. With this new functionality, global purchasers gain access to purchase requisitions and purchase orders maintained in the connected backend ERP systems.

Global purchasers can assign an appropriate source of supply to open purchase requisitions and subsequently trigger the conversion to purchase orders. In addition, centralized approval by the headquarter is enabled for subsidiary purchase orders.

Using this new functionality, companies benefit in a lot of ways. Due to the global source of supply assignment, global purchasers can drastically reduce maverick spend in subsidiaries. In addition, procurement organizations can lower their costs based on scaled purchasing activities.

Material Price Variance Analytics

In the area of purchasing analytics, we ship a new analytical list page that displays the variance in material prices. Using the analytical list page, purchasers get full transparency in the price volatility for specific material. The app is taking a feature we shipped with 1711 to next level by improving user experience and also taking into consideration purchasing contracts. As for any analytical list page, the graphical charts on the upper section can be used to filter, e.g. by number of changes, released amount or invoice amount.

For a particular material, the price deviations in purchasing contracts and purchase info records are displayed graphically using a trend line in the chart section of the app.

Leveraging the information from the embedded analytics chart, buyers can analyze and monitor material price variances and identify trends. For some materials, the volatility in prices is severe and might also be influenced by seasonal trends. Having gained that insight, buyers can make informed buying decision that fully exploit the potential to save costs impacting the bottom line dramatically. This is an excellent showcase for our instant insight to action paradigm.

SAP S/4HANA Cloud Integration with SAP Ariba Guided Buying

Last but not least, I want to point out one 1805 highlight that tells the story of “two are more powerful than one” quite well. The new integration between SAP S/4HANA Cloud and SAP Ariba Guided Buying combines the strong suits of each individual of the two solutions, so that users can benefit from a superior digital buying experience.

Guided buying serves as a central place where all buyers can shop both goods and services and satisfies with a simple and intuitive user experience. Using the guided buying capability, procurement and individual line-of-business (LoB) owners can specify procurement policies and inform or guide users whenever policy conflicts might arise. Buyers can find all items that are contracted through supplier-provided catalogs and punch-out catalogs.

Using the pre-built integration with SAP S/4HANA Cloud, budget checks can be performed before submitting the shopping cart. Approved shopping carts will be replicated to SAP S/4HANA Cloud as purchase requisitions, where they will automatically be created into purchase orders. Both the goods receipt and the supplier invoice will be posted in SAP S/4HANA Cloud as well.

Combining the guided buying capability of SAP Ariba with SAP S/4HANA Cloud, procurement departments benefit from an intuitive and elegant purchasing experience will having tight integration to the core.


For more information on SAP S/4HANA Cloud, check out the following links:

Follow us via @SAP and #S4HANA, or myself via @KathHellmich and LinkedIn