SAP IBP, a cloud-based offering for supply chain planning that aligns supply chain operations with corporate planning, supports business goals. It offers real time visibility, sophisticated planning, and helps organizations act on evolving trends based on analytical data provision for making informed decision. SAP S/4HANA on the other hand is a software for ERP business suit which enables companies to conduct business based on their different needs in various fields as finance, production, and logistics chains. It comes with in-memory computing abilities that enable on-time processing of data making perfect for businesses operating under digital environments.

SAP IBP provides an incredible opportunity to the customers to integrate their valuable data with S4HANA, perform the necessary forecasting, planning and move the data back to S4HANA. Firstly, the master data such as Materials, plants, organizational data, and transactional data such as sales orders, planned orders and purchase requisitions are created in S4HANA landscape. Secondly, with the CI-DS (Cloud Integration for Data Services) the created data would be integrated with SAP IBP by creating tasks for Master data and transactional data. The transactional data is now called key figure in IBP terms. Finally, the tasks are executed to get the necessary data in the IBP system. Once, the data has been arrived in the IBP system, it is the responsibility of the Planner to check the correctness and consistency of the Master data. This means the data which has been shipped from the S4HANA system should reach the IBP system without any breakage and that it is reliable. With over millions of product location combination this becomes cumbersome to check each data manually and verify. Hence, we make use of the Machine Learning algorithm which checks for the data consistency.

The algorithm helps in identifying semantic relations and patterns in Master data and derive association rules. From these rules, the ML recognizes outliers with high confidence and suggests outliers. The planner can decide based on the rules obtained if they can be adhered to his/her business processes. Later, necessary changes can be made to the Master data either in IBP or in S4HANA system. The algorithm can be trained and tested once, or many times based on the master data type. Once the results are obtained, it is in the for of a .csv file which has the information on number of records which were analyzed, number of rules, and number of suggestions. [1]

An example of a Learned rule with practical data is as follows.

The above example interprets that if the PRODGROUP is L004, then PRODTYPE is FERT. A confidence of 1 means that in 100% of the data, PRODTYPE equals FERT when PRODGROUP is L004. A Lift of 3 means that FERT is observed in a row of data, the probability to find L004 as PRODGROUP is 3 times higher than any random row. A Support of 0.33 implies 33% of data, FERT is found together with L004. A complexity of 1 means that there is only one element in the antecedent, namely PRODTYPE: FERT.

In conclusion, the Integration of SAP IBP with S4HANA plays a very important role for seamless data transfer and to obtain forecasted and planned data. The necessary data such as Master data and transactional data are generated in S4HANA system. It is then integrated with SAP IBP with the CI-DS in place. An ML algorithm is run to check the data consistency as well as some rules and recommendations. Thus, ML algorithm helps in stabilizing the information obtained.

Reference:

1. https://help.sap.com/docs/PRODUCT_ID/feae3cea3cc549aaa9d9de7d363a83e6/00ee821f854d40458d4c9eb251483222.html?state=DRAFT&version=latest&locale=en-US

Acronyms:

IBP – Integrated Business Planning

S4HANA – Suite for HANA

ML – Machine Learning