SAP S/4HANA extended Service Parts Planning: 2022 ...

Diaa_Abdelaziz · ‎10-13-2022

We are excited to announce that the 2022 Release of SAP S/4HANA for Extended Service Parts Planning (eSPP) has been successfully released on October 12 and continues its journey towards a Next Generation Service Parts Planning Solution!

SAP is investing even further in eSPP development on areas of Forecasting, Optimisation and Simulation! Let's have a deeper look at the top highlights shipped with this year's release.

Digital Supply Chain Edition, multi-back-end version (Side-by-Side Planning)

Previously, to implement eSPP, it required an implementation or upgrade from ECC to S/4HANA 2020 release or higher. However, implementations or upgrades to the ERP system can be undesirable due to time constraints, investments, and impact on other business functions.

As of this year's release, eSPP is available as part of the SAP Digital Supply Chain Management (DSC), edition for SAP S/4HANA, and we offer the ability to connect a single eSPP planning system with one or several SAP ECC and/or SAP S/4HANA back-end systems (multi-back-end capability). Both systems are connected via Core Interface (CIF): Master data is integrated from the ERP execution system with the eSPP system, and transactional data is exchanged bi-directionally. The integration is controlled via integration models created and activated in the ERP execution system. The solution is based on three design principles: Modularity, Synergy & Integration, and One code line with SAP S/4HANA systems.

Furthermore, now it is possible to call aATP in the back-end system to determine the available quantity as input for eSPP Deployment to calculate the maximum deployable quantity to lower-level locations of the planning network (BOD) Bill of Distribution. Further insights can be found in this blog.

Advanced Forecasting Capabilities

eSPP delivers a wide selection of aftermarket-specific forecasting models, and with this latest release, three new algorithms are being offered:

The ARIMA Forecast model (by integration of PAL Forecasting Library) improves time series analysis with Auto-Regressive Integrated Moving Average (ARIMA) and can increase forecasting accuracy in case of trend & seasonal demand patterns. It can also be used for econometrics, statistics, and time series analysis.

Gradient boosting of decision trees (by integration of PAL Forecasting Library) is the first machine learning-based technique brought into eSPP and is used for regression problems in Forecasting.

The Leading Indicator Forecast generates a forecast that is not (only) based on historical sales values but on other factors like the installed base, operation time, or the number of uses.

Inventory Optimization

In the 2022 release, the multi-echelon Inventory optimization Engine (MEIO) is embedded into the S/4HANA logic. The newly introduced embedded option brings optimized performance, simplified configuration, and operation.

This brings true optimization into the inventory calculation and offers the chance to further reduce inventory while maintaining a high customer service level.

Furthermore, from now on, Pricing Scales for Purchasing Costs from Suppliers can be considered for the calculation of Economic Order Quantity (EOQ) and SFT (safety stock), as often higher volumes come with lower prices per piece.

Capture Demand (Event Management)

With the new functionality of Event Management, the captured demand can be separated into baseline and promotion demand to keep apart non-recurring demand patterns in Forecasting, like a promotion, but also like an unexpected dip due to disruptions or distortions.

Distribution Requirements Planning (DRP)

DRP now allows for defining subcontracting scenarios in kitting process to plan procurement & manufacturing of kits and can automatically create subcontracting orders in case of external kitting.

SAP is investing further into S/4HANA eSPP!

We are excited that SAP has made the decision to invest further in the S/4HANA eSPP development, introducing further innovations to the roadmap and accelerating it.

A key investment area in eSPP is on Forecasting tools and methods that can intelligently be combined and can improve, learn, and adopt during usage. In that regard, we plan to offer Long-term Forecasting capabilities, including the integration of multiple external data streams, with ARIMAX and Gradient Boosting of Decision Trees forecasting algorithms.

Additional planned features includes; OEM-Managed Inventory for the Integration of retail-level planning through OEM-managed inventory and Simulation Capabilities for Capture Demand and Forecasting

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