Machine Learning: Lieferterminverzögerungen in SAP S/4HANA durch eingebettete KI vorhersagen

Machine Learning: Predicting Delivery Date Delays in SAP S/4HANA with Embedded AI (Part 4)

(4 min read)

In the fourth part of our blog series on the Intelligent Enterprise, we explain how you can integrate machine learning (ML, a subfield of artificial intelligence/AI) into SAP S/4HANA processes, which preconfigured ML scenarios SAP is already delivering in the areas of purchasing, sales and supply chain, and what benefits machine learning brings to your business processes.

Machine Learning in the  SAP Product Portfolio: An Overview

The design and operation of ML models and scenarios implemented in various SAP solutions follows the cross-industry standard process for data mining. The process starts with understanding the business requirement, followed by the steps of «understanding and preparing the relevant data». This is followed by modeling, training, evaluation, deployment, and monitoring of the respective ML model.

CRISP-DM
Source: SAP

Common ML algorithms are used for this purpose. For moderate ML requirements in SAP S/4HANA like forecasting, trending and influencers, the algorithms like regression, clustering, classification and time series can be used. These algorithms are part of the HANA database or are included in the scope of delivery of many SAP products, e.g. SAP S/4HANA or SAP SuccessFactors, keyword here: «Application Embedded AI».

More complex ML requests with external data and more complex algorithms and requirements such as image recognition, sentiment analysis, natural language processing and deep learning are typically offered via a side-by-side scenario through the SAP Business Technology Platform (BTP) and are grouped under the «AI Business Services» product portfolio. AI Business Services are provided as API function calls and can be integrated into any SAP and non-SAP systems.

KI Technologie in SAP Lösungen
Source: SAP

Furthermore, SAP plans to offer an AI Foundation on the BTP in Q3 2021, which will further simplify the management, training, and deployment of ML models (including those from external vendors) across SAP solutions.

Preconfigured Machine Learning Scenarios in SAP S/4HANA

SAP has already delivered about 20 ML scenarios with the SAP S/4HANA 2020 release and 24 ML scenarios with SAP S/4HANA Cloud 2105. Here is an overview of the scenarios from the areas of purchasing, supply chain and sales (excerpt):

Line of Business Scenario
Purchasing Prediction of delivery date for purchase order items
Purchasing Prediction of cash discount losses for invoices with payment blocking
Purchasing Prediction of fulfillment rate of purchase contracts
Supply Chain Prediction of delivery delay for stock transfer order
Supply Chain Early detection of slow / non-moving stocks
Supply Chain Prediction of stock transfer product lead time
Sales Prediction of schedule delays for outbound shipments
Sales Prediction of quotation conversion probability rate
Sales Sales performance prediction

 

The ML models are fully integrated into the processes and generate forecasts based on historical process data. The forecast results are overlaid on the operational and analytical apps, enabling employees to react to specific situations much earlier or proactively.

Let’s take a closer look at the scenario «Predicting schedule delays for outbound deliveries».

Predict Schedule Delays for Outbound Deliveries in SAP S/4HANA

An inside sales representative can oversee the creation and processing of the scheduled delivery to the customer as part of the order fulfillment process. The sales employee is also able to identify problems immediately and initiate appropriate measures. This ranges from the timely supply of the procurement processes to the creation of deliveries as follow-up orders to transport planning, picking, packing, and shipping in the delivery process and transport processes.

In the SAP Fiori app «Predicted Delivery Delay», a delay in days for delivery creation and processing as well as the predicted overall status of the delivery item is displayed for each sales order item in addition to the planned delivery date. In addition, the statistics for all order items are displayed graphically.

Screenshot Predicted Delivery Delay

This enables sales staff to immediately identify delays in the supply chain, take timely action in critical positions and thus increase customer satisfaction.

The machine learning model on which this scenario is based currently contains around 130 standard data fields or input parameters that can be used for training. When training the model, the data field catalog can be restricted or filtered.

Are You Curious for More?

Then read on! You can find more on the topic of «Intelligent technologies in the SAP S/4HANA context» in our Intelligent Enterprise blog series:

 

MORE ABOUT THE INTELLIGENT ENTERPRISE

Dimitri Schweigerdt
Dimitri Schweigerdt
Innovation Manager und Digital Advisor

Dimitri Schweigerdt has been working as an SAP consultant since 2002. His focus areas include the Intelligent Enterprise, Smart Factory, Digital Supply Chain and technologies such as IoT, Machine Learning and Robotic Process Automation. Since 2019, Dimitri Schweigerdt has been part of the NTT DATA Business Solutions team as Innovation Manager and Digital Advisor.

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