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Qi_Liu
Product and Topic Expert
Product and Topic Expert

To help us gain a quick view of the embedded intelligent predictive capabilities leveraging machine learning in S/4HANA Cloud Public Edition, I will provide brief descriptions and result screenshot of 8 selected simple intelligent scenarios. These are supply chain and sale related scenarios - produce, manufacturing, inventory management, logistics execution and they can be adopted in minutes without extra configuration. I will also introduce the steps to activate a predictive analytics mode by the end of this blog.

Index

Scenario 1: Predict the delivery date for a stock transport order

Scenario 2: Monitor Slow-Moving Materials with the Predictive Analytics Model

Scenario 3: Predict the delivery date of purchase order items

Scenario 4: Predict the quantity consumption of purchase contracts

Scenario 5: Predict Delivery Processing Delay

Scenario 6: Predictive Scenario for Project Cost Forecasting

Scenario 7: Predict Sales Volume

Scenario 8: Predict quotation conversion rates and net value of converted quotations

Steps to activate the predictive analytics model

There will be more to be continuously updated.

Scenario 1: Predict the delivery date for a stock transport order

Use an active version of predictive analytics scenario MATERIAL_OVERDUE_SIT (Stock in Transit Material Overdue) to predict the delivery date and predicted deviation for a stock transport order.

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Optimized Predictive Analytics Settings | SAP Help Portal

Scenario 2: Monitor Slow-Moving Materials with the Predictive Analytics Model

This process enables you to monitor the development of the Slow-Moving Indicator over the selected period. If the predictive model is active, the system calculates a predicted future value, based on your company’s historical data on how the Slow-Moving Indicator will develop over next 3 months (predefined, not modifiable).

The app ‘Slow or Non-Moving Materials’ displays the predicted data as an orange line after activating the following 2 scenarios:

MMSLO_CONSUMPTION_02 (Consumption Data for Slow or Non-Moving Materials)

MMSLO_STOCK_LEVEL_02 (Stock Level Data for Slow or Non-Moving Materials)

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How to Monitor Slow-Moving Materials with the Predictive Analytics Model | SAP Help Portal

Scenario 3: Predict the delivery date of purchase order items

A business user will be interested to know the predicted supplier delivery. Based on multiple parameters, the system can predict a date by which the supplier can deliver the material after activating the scenario SUPLRDELIVPREDICT (Supplier Delivery Prediction).

APP Monitor Purchase Order Items, select one item and click on the ‘Predict Delivery Date’, after several seconds the value is changed from ‘Not Predicted’ to ‘05/17/2024’ which is the predicted delivery date.

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Predictive Scenario for Monitoring Purchase Order Items | SAP Help Portal

Scenario 4: Predict the quantity consumption of purchase contracts

By activating the scenario QTY_CONTRACT_CNSMPN (Quantity Contract Consumption), we can predict the quantity consumption of purchase contracts in APP Quantity Contract Consumption.

Select the Trend then we can see the orange line representing the predictive consumption.

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Predict Consumption of Quantity Contract | SAP Help Portal

Scenario 5: Predict Delivery Processing Delay

This APP ‘Predicted Delivery Delay’ allows you to identify potential delays for open sales orders regarding the predicted delay of the planned delivery to the customer by activating the following 2 scenarios:

PRDTDDELIVCRTNDELAY (Delivery Creation Delay)

PRDTDDELIVPROCGDELAY (Delivery Processing Delay)

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Predicted Delivery Delay | SAP Help Portal

Scenario 6: Predictive Scenario for Project Cost Forecasting

By activating the Predictive Scenario for Project Cost Forecasting ‘PREDICT_PROJECT_COST’ , we can achieve less budget overruns and better project investment decisions based on more realistic estimations and reduce the effort for project cost planning and forecasting while improving accuracy as shown in the APP Monitor Projects.

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Predictive Scenario for Project Cost Forecasting | SAP Help Portal

Scenario 7: Predict Sales Volume

The APP ‘Sales Performance – Predictions’ gives sales manager more insights by providing the predicted sales volume by the end of the month and next month after activating the scenario SALESVOLUME001 (Sales Performance - Prediction).

7.png

Sales Performance – Predictions | SAP Help Portal

Scenario 8: Predict quotation conversion rates and net value of converted quotations

APP Quotation Conversion Rates - Valid/Not Completed can help predict quotation conversion rates and net value of converted quotations in the list of quotations by activating scenario SLSQTANPREDICTION (Sales Quotation Conversion Rate).

8.png

Quotation Conversion Rates - Valid/Not Completed | SAP Help Portal

Steps to activate the predictive analytics model

You enable the above predictive functionality by using an active version of the default model in the corresponding predictive scenario. Let’s take the intelligent scenario MATERIAL_OVERDUE_SIT (Stock in Transit Material Overdue) as an example.

To do so, you create versions by training the model with different sets of data using the Intelligent Scenario Management APP ‘Intelligent Scenario Management’.

Qi_Liu_9-1715938808282.png

After extend the intelligent scenario MM_IM_VDM_STOCK, select line ‘DEFAULT1’ and click ‘Train’

Qi_Liu_10-1715938808285.png

For performance and business reasons, and to ensure you receive good quality data to train the model of your training data, it is important that you focus on filtered data set that makes sense from a business point of view, and which does not receive too many records at once. We recommend that you only set filters for the areas/fields for which you are responsible.

Qi_Liu_11-1715938808290.png

The status of the model is ‘Scheduled’

Qi_Liu_12-1715938808294.png

We can see it is in ‘Training’ status

Qi_Liu_13-1715938808297.png

Come back and check the status is ‘Ready’ and the ‘Activate’ button is back.

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After clicking on the ‘Activate’ button

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Then the status become ‘Active’ and now we can review the predictive delivery date if it is fine tuned.

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The features covered in this article are based on SAP S/4HANA Cloud, Public Edition 2402, please refer to the latest information for changes in subsequent versions.

Hope you LIKE it if it addresses your issue. After that, please feel free to comment after following my account and I will reply ASAP.

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