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We’re kicking off the new year with a release that will elevate automation, resilience, precision, and integration in supply chain planning to new heights.

Today, February 2, 2024, SAP Integrated Business Planning for Supply Chain 2402 was generally released as planned. 

With the integration of the Everstream Analytics Platform, we now allow our customers to incorporate external risk data that helps to uncover potential pitfalls in the supply chain. 

With SAP IBP 2402 we also enable an integrated, intelligent plan-to-fulfill process with SAP S/4HANA Cloud Public Edition, making SAP IBP available to our customers in the public cloud.

Another exciting integration feature is the exchange of transportation, production, and supplier lead time data from SAP S/4HANA and SAP ERP that enables you to optimize your supply lead time calculations.

Let's dive into the first release highlights of the year and discover what else is in store for you:

Supply Chain Control Tower

Incorporate Best-in-Class Risk Insights with Integration of Everstream Analytics Platform

In today’s ever-changing and interconnected business environment it’s crucial for companies to identify and assess potential risks as soon as possible to ensure a resilient, reliable, and sustainable operation of their supply chains. They can’t rely only on their internal data but need external information sources as well.

With the integration of the Everstream Analytics Platform, we now enable our planners to use the data of one of the best-in-class risk insights providers to proactively address upcoming risks and avoid supply chain disruptions through early risk mitigation. Planners can import risk data like risk category, duration, and severity into SAP IBP and set up customized alerts based on risk scores.

Please note that this feature requires an Everstream Analytics license. 


IBP General

Forecasting with Integrated Plan-to-Fulfill Process with SAP S/4HANA Cloud Public Edition

We now offer a bidirectional integration with SAP S/4HANA Cloud Public Edition for both master and transactional data, enabling our customers to run advanced demand planning and management processes. This feature drives planning accuracy and customer service through an integrated, intelligent plan-to-fulfill process with SAP S/4HANA Cloud Public Edition using the machine learning-based forecasting algorithms of SAP IBP.

We provide a standard integration package in SAP Integration Suite that allows to integrate product, location, customer master data as well as sales history data from SAP S/4HANA Cloud Public Edition to SAP IBP. In addition, planners can also integrate the final consensus demand forecast from SAP IBP to SAP S/4HANA Cloud Public Edition.

Seamless Approval Workflow for Process Step Completion

We now provide a seamless, workflow-enabled approval process in SAP IBP based on the robust workflow engine of SAP Build Process Automation. This advanced feature ensures that each process in SAP IBP proceeds to the subsequent steps only after gaining the necessary approvals from the reviewers of the completed process step. It offers enhanced monitoring and safeguards by incorporating a four-eyes principle.

The new setup utilizes workflow content made available through SAP Build Process Automation. SAP IBP will automatically trigger the workflow when the end conditions of a process step are fulfilled and then reviewers can use the My Inbox app to manage process approvals efficiently.

Please note that this feature requires an existing SAP Build Process Automation license.


Effortless and Continuous Improvement of your Master Data with Automated Maintenance

Rules for master data maintenance can now be applied automatically. These rules run in real-time when master data is created or changed in the relevant rule components. For example, you can automatically generate source of supply master data for each new product created in your system or trigger the maintenance of transportation lead times if your distribution capabilities change.

By automating the maintenance process, you reduce recurring manual intervention and job scheduling efforts and ensure that your master data is always up to date. You can automate rule-based master data changes in both simple and complex master data management scenarios.



Improved Forecast Accuracy with New Algorithm “Extreme Gradient Boosting” (XGB)

Extreme gradient boosting (XGB) is an ensemble machine learning algorithm that helps you to make better predictions and decisions, for example, in forecasting sales or identifying problem areas in inventory or delivery. Like gradient boosting, XGB combines the predictions of many decision trees, but is doing this for each lag, meaning for each forecasted week, separately. It iteratively learns from mistakes of previous predictions and corrects those mistakes moving forward. Compared to gradient boosting of decision trees, XGB is better at fine tuning its models and more efficient in terms of computation time.

XGB can be used within the best-fit forecast model. It’s highly automated and includes built-in parameters to handle smoothing of data and error minimization. Please note that only weekly granularity is supported.


Inventory and Supply

Accurate Supply Lead Time Recommendation

Few parameters have as significant an impact on supply chain planning as lead time. Lead time values used by the SAP IBP system can drift over time from actual execution times, or worse, reflect worst-case lead times. This new feature keeps lead time values in SAP IBP current through a safe, automated process. By considering lead time variability, customers not only include the uncertainty at demand level (via the forecast error) but also get input on the uncertainty at the supply side. Inventory and supply planning accuracy improves and working capital is reduced by eliminating over-estimation of lead time.

Aggregated, time-phased lead times for transportation, production, and procurement are passed from SAP S/4HANA or SAP ECC to SAP IBP, where a new operator recommends values for lead time mean, variance, and confidence interval. After the operator runs, planners may compare new and old values, validate new values through scenario analysis, and where appropriate, explicitly promote new values to baseline.

Lead time values are most commonly used by inventory and supply optimizers. Planners may also use recommended values to create supplier score cards or compare recent execution trends to macro industry trends. A sophisticated ML “prediction” of future lead times may be configured in conjunction with an SAP IBP forecasting algorithm such as gradient boosting.


Best Practices

Top-Notch Analysis with Preconfigured Analytics Stories

Preconfigured analytics stories for global demand planner, local demand planner, demand planning process expert, and supply chain risk analysis are now available as part of the sample planning area SAPIBP1.

With the 2402 release, SAP Best Practices for SAP IBP deliver comprehensive, powerful, and user-friendly examples for different roles demonstrating best-in-class analysis.

The analytics stories provide an inspiring starting point for your own implementation showing a large variety of possible features.

Also check out our new demo video:

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