End Of SAP APO – What Now?

This article was written in cooperation with Gabriel Werner, Vice President EMEA Solutions Advisory at Blue Yonder.

What are the differences? And to what extent can companies benefit by choosing a non-SAP planning tool? The SAP Advanced Planning and Optimization (APO) supply chain planning system is used throughout every industry around the world. Maintenance and support for the solution are expected to be discontinued in 2025. Users will need to look for an alternative by then at the latest. The cloud-based successor solution SAP Integrated Business Planning (IBP) does of course come into consideration here, but the new product only works in conjunction with S/4 Hana. This means that APO’s end of life will involve a major migration effort.

Many companies are understandably undecided as to whether they want to put in this effort for a product that is not yet established on the market. However, anyone looking for a more mature solution shouldn’t wait around. Just looking around and specifying the requirements takes a lot of time, as does implementing the new solution. There is risk involved in needing to revert back to APO if support is no longer provided.

SAP Integrated Business Planning is designed to offer greater performance and close the gap resulting from the end of life of APO. However, the successor solution does not replace all of the previous APO modules. For example, SAP has removed several key detailed planning and order scheduling (order promising) features and integrated them into S/4. Only the modules for demand planning as well as for procurement, production, sales, and transport of materials can be found in IBP in a revised form.

IBP and S/4

Users need to address the question concerning which functions of the existing APO platform will be in S/4 in the future and which ones will be in IBP. Due to the functions being dispersed over two different solutions, coordination within a company’s supply chain management could become significantly more difficult. The distribution of features across different applications entails fragmentation. The market, however, is moving towards integrated end-to-end platforms. They not only provide a holistic view of the supply chain, they also enable processes within the same product family to be analyzed, planned, and executed.

Looking beyond the horizon

It is worth looking beyond the horizon: What alternatives to IBP can be considered as a successor to APO? Blue Yonder’s Luminate technology, for example, comes into consideration here. The AI specialist from Karlsruhe, which was originally a German company, was acquired by JDA Software, a provider of supply chain management software, in 2018. In 2020, the company was renamed Blue Yonder. The renaming of JDA to Blue Yonder reflects the growing importance of cloud and AI solutions.

JDA’s solutions have long been recognized on the market as an alternative to APO due to their end-to-end planning, integration of supply chain planning and execution, modelling functions, and control towers. The AI functionalities that are now available offer additional value. For example, the company develops patented machine learning algorithms and incorporates them immediately into corresponding applications, resulting in holistic machine learning solutions.

The biggest challenge is to integrate machine learning into business processes in a way that is reliable and can be repeated. Other providers are not as far along in this area yet. They merely integrate machine learning algorithms trained in an existing system environment, whose output can then be used in the respective process. This means that they provide an AI platform on which the customer has to develop their own machine learning application. What is special about the Luminate technology is that the algorithms – in terms of advanced analytics – already take effect at the business process level. This means that they not only enable reliable forecasts, but also the ability to predict and resolve disruptions or adjust various parameters along the supply chain, among other features. The solutions can furthermore be scaled to fit large enterprise applications.

Machine learning for SCM

In addition, the result of the machine learning evaluations can be better explained and interpreted through the direct reference to the business process. This increases the acceptance and trust of users in the technology. It is therefore crucial that the machine learning application supports the respective business process for the benefit of the business user. This support comes in the form of process automation. A machine is superior to humans when it comes to repetitive decisions that have to be made frequently in particular. It is faster, more precise, and less prone to error than its human counterparts. However, it is not only ordering and scheduling processes that can be automated. Disruptions along the supply chain can also be detected quickly and countermeasures can be initiated automatically.

If, for example, a delivery of goods is stuck at sea, the system is able to decide on its own where the parts that are now missing can be alternatively procured, taking into account transport costs and routes as well as time and other relevant factors. The algorithms learn from current and historical data. They make forecasts about the probability of an event occurring. All subsequent decisions and the chain of business processes that are initiated as a result of the forecast are based on these. This could involve simply rerouting a lorry or making adjustments to production plans in factories that impact the downstream logistical processes. The more data there is and the better they are maintained, the higher the data quality. This has a direct impact on the quality of the forecasts and therefore on decisions and business processes.

Data diversity and algorithms

Where do the large amounts of data that the algorithms work with come from? Not only do internal data from the ERP system come into play, but data from external sources, referred to as externalities, are also used. This includes information about the weather, upcoming bank holidays, (school) holidays, or even local location data. A good machine learning application should also provide much of this external data. Information provided by the customers themselves is also relevant. In the area of industrial production, this includes historical data on the utilization of machines, order quantities, delivery volumes, and data on the demand for certain products.

If traditional ERP users now want to use a third-party planning tool, the effort for connecting and customizing should not be greater than with IBP. Blue Yonder, for example, has integrated interfaces and adapters into its Luminate platform that allow master data from the SAP ERP system to be seamlessly transferred into its own planning solution. This ensures a smooth real-time exchange of data between the two systems at all times. This means that users can cover traditional ERP tasks such as merchandise management, procurement, accounting, cost accounting, or controlling in their SAP system. Typical SCM tasks such as strategic and detailed planning as well as transport and warehouse management can then be handled by Luminate. In addition, the integrated control tower ensures optimal transparency across the entire supply chain. Nevertheless, if SAP users do not want to go without IBP, a hybrid environment is also possible. In this case, customers can use basic functions from IBP and flexibly add features from Luminate on a software-as-a-service basis.

Conclusion

The expected end of life of SAP APO in 2025 means that users need to act, and they need to act fast. To continue to ensure high-performance supply chain processes, companies must decide on a suitable successor solution soon. Third-party systems, such as Luminate from Blue Yonder, can be considered as a sensible alternative to SAP IBP. The machine learning capabilities built into it enable fine-grained forecasts, automate processes along the supply chain, and optimize demand planning.