Supply Chain Management Blogs by SAP
Expand your SAP SCM knowledge and stay informed about supply chain management technology and solutions with blog posts by SAP. Follow and stay connected.
Showing results for 
Search instead for 
Did you mean: 
Are Machine Learning and Artificial Intelligence just buzz words?

Machine Learning and Artificial Intelligence are terms so widely used in the market and many companies ask for how to consume it best and turn it into action for their supply chain. Yet, they often fail to describe or define it precisely and even more in implementing such concepts well.

So, the ultimate question remains, how can companies turn Artificial Intelligence (AI) into business value from a supply chain planning angle?

Key is here to define in which area you want to initially start with using AI & ML capabilities. For example, do you want to improve forecast accuracy, or rather optimize the supply plan, or enhance data quality or even automate a process?

Now let us investigate, how you can take advantage of ML/AI by using SAP Integrated Business Planning.

Embedded Machine Learning capabilities in SAP IBP

Embedded machine learning capabilities in SAP IBP significantly drive planning accuracy and automation. SAP IBP is leveraging machine learning capabilities in multiple aspects of planning such as forecasting, alerting, master data and operations.

Embedded natively in the solution, machine learning capabilities are available out of the box with automatic access to all relevant planning data needed to train the models.

The target is to get touchless planning and better planning results with Machine Learning

Extending SAP IBP Machine Learning capabilities with SAP Data Intelligence on SAP BTP

Extend machine learning (ML) capabilities for Data Scientists by using Data Intelligence on SAP Business Technology Platform (BTP)

Create and deploy custom machine learning models including external solvers and machine learning algorithms in SAP HANA Predictive Analysis Library (PAL), Python, R, TensorFlow, etc.

Orchestrate E2E machine learning models to read from SAP IBP, run machine learning models and write back to SAP IBP.

Let us see how SAP Integrated Business Planning creates value with data and intelligence provided by SAP BTP.

Lead time prediction in SAP IBP by using SAP Data Intelligence from SAP BTP

Improved Plan Quality with Intelligent Lead Time Prediction is available via Machine Learning Extension for IBP in SAP BTP Data Intelligence.

Lead time is one of the most important control parameters in Supply Chain.

Intelligent lead time prediction is modeled as a sample Machine Learning (ML) extension use case in SAP Data Intelligence and is delivered with an SAP BTP mission in SAP Discovery Center.

The mission allows you to extract the historical lead times from goods movement data in SAP S/4 HANA and to analyze outliers and key influential factors for lead time changes.

You can then train ML models using granular transport data, set up and execute AI algorithms that predict future lead times based on the corrected values and upload the result to SAP IBP for planning and analysis.


  • Dynamic Lead Time based on execution instead of static lead times, used as input to drive inventory and supply plans in SAP IBP.

  • Derive historical lead times from goods movement data and analyze outliers and key influential factors for Lead Time changes.

  • Consider external causal factors like weather, seasonality, calendars to fit ML model to the lead time patterns.

  • Train machine learning model using granular transport data and use to predict lead times. Lead times sent to SAP IBP as key figures and/or update lead times at source.

  • Flexible scenario modeling using SAP BTP/Data Intelligence that can be extended by customers and partners.


  • Drive better decisions in SAP IBP by recommending the lead times based on actuals of stock transfers / purchase orders / production orders.

  • Improve quality of planning parameters considered by inventory and supply planning algorithms.

  • Improve plan adherence by considering dynamic nature of the actual lead times and future trends as input to planning runs.

  • Improve safety stock recommendation based on improved accuracy of lead time variability.

Fell free to learn more about this ML-based lead time prediction via this YouTube Video

SAP IBP - Better Planning Results with Intelligent Lead Time Prediction - YouTube