Predictive Analytics Model Training - Supply Chain (20N)

Predictive Analytics Model Training - Supply Chain (20N)

With this scope item, the customers can utilize the embedded predictive analytics functionality in the supply chain scenarios - produce, manufacturing, inventory management, logistics execution etc. It includes parameter selection in predictive model training and activation. Currently the following use case leverage the embedded predictive modeling.

Track materials in transit/open stock transport orders for which no goods receipt has been posted by the receiving plant yet which have already exceeded the estimated time in transit.

Key Process Steps Covered

  • Train a predictive model for stock in transit with available data
  • Update the forecasted date column which shows the predicted delivery date by leveraging the embedded predictive model
  • Update to the Stock in transit process, stock transport order process with predictions

Benefits

  • Out of the box predictive model that can be leveraged in the stock in transit app
  • Get a predicted date for the stock in transit based on the delivery data for the customer
  • Adjust time schedule based on empirical data
  • Overall more reliable planning / scheduling of goods in transit process

Where is Predictive Analytics Model Training - Supply Chain(20N) being used?

This Scope item is used in the following way:

  • As a core function of Production Operations within Manufacturing Scope Item Group

No Process Flow details available for scope item Predictive Analytics Model Training - Supply Chain(20N)-s4h-2021
Ref: Manufacturing of SAP S4H-2021
Best Practices related to S/4HANA*1) Yearly Updates are released per September of each year
*2) The Quarterly updates are released per Februari, May, August and November
Details of future releases can be found in the SAP Roadmap section of each product. Go to SAP Roadmap Product Finder.
Knowledge Center Scheer Nederland | The Process Experts Visit also the SAP Cloud ERP knowledge center of Scheer Netherlands. The following topics are covered:
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