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The Future of Supply Chain Management | Part 2 of 6

2025-11-18T09:09:46.257000+01:00

Supply Chain 2035: How Data, AI, and Agents are Changing Supply Chain Planning

In the discussion about the future of supply chain management, one area is increasingly at the center: planning. Supply chain planning not only affects operational efficiency, but directly influences a company’s service quality, fixed capital and responsiveness. Our joint study from SAP Business Consulting and Kühne Logistics University clearly shows that companies that are already planning proactively today can avoid bottlenecks, optimize costs, and exploit potential in a targeted manner.

Supply chain planning is the central lever for anticipating problems before they arise. Companies that anticipate bottlenecks and proactively manage capacity, inventory and resources avoid hectic “fire fighting” in day-to-day operations. Planning thus acts as an early warning system that not only enables stable service, but also more efficient use of resources and proactive action. Effective planning also requires seamless integration and evaluation of a variety of internal and external data sources – from granular sales data to basic economic data, to production, procurement, and distribution plans. Different planning levels and horizons need to be connected, and this is where there are huge opportunities for artificial intelligence that can process data volumes, identify patterns, and improve predictions. Advanced planning processes will become a matter of course over the next ten years. If you do not achieve at least a modern, integrated planning level by 2035, you risk not only a competitive disadvantage, but also the loss of operational control capability.

Our survey of supply chain decision-makers (see figure) provides a clear picture of the future. Many expect planning to be much simpler and more intuitive in 2035 than today. More than half expect generative AI and GPT models to have a significant impact on planning processes, and much of it sees data quality as a critical factor in planning decisions. In addition, many expect the breakthrough of new AI-powered forecasting tools that incorporate additional data sources and significantly improve demand planning. Around a quarter expect advanced risk analysis to provide the ability to proactively detect and mitigate disruptions.

However, the status quo shows how big the gap between aspirations and reality is. Only a small number of companies rated their planning as mature in our survey. Similarly, few are actively experimenting with GenAI or GPT applications in the planning environment. Only a minority has very accurate planning data harmonized from different sources. Highly automated demand planning with minimal human intervention is the exception, as are planning systems that provide clear recommendations for action in the event of disruptions. This discrepancy shows that the path to truly modern, intelligent planning is still long and requires targeted investment, clear priorities and structured action.

In order to close this gap, different approaches must be pursued. Companies where planning is a strategic core process or requires very individual processes can realize enormous potential with agent-based AI. With the advent of new agency tools, most repetitive, error-prone work is automated, allowing planners to focus on value-added analytics and decisions. Companies with standardized processes and a low strategic relevance of planning will be able to procure their planning function more as a service in the future. Technology and platform models could enable true end-to-end planning across company boundaries for the first time. Simulations and digital twins will also play a growing role. Although these often fail today due to complexity, modeling effort, and scalability, AI-powered modeling agents could significantly accelerate and improve the accuracy of simulation creation and updates in the future, making their use more practical in larger networks.

Regardless of the individual target image, there are measures that every company should tackle immediately (“no-regret moves”). This includes building robust data governance and harmonizing master data as the foundation for any data-driven planning. Unfortunately, the principle of “garbage in, garbage out” also applies with AI: without high-quality data, even the best AI system cannot deliver meaningful value. A consistent, company-wide IBP system enabling a S&OP process provides the basis for a common planning goal. Initial pilot projects with agent-based sales forecasting help gather experience with AI-powered planning approaches. Finally, it is important to actively link existing planning platforms, such as SAP IBP, with AI services to unlock integration potential early on.

Supply chain planning is now more important than ever and will become a key differentiator between successful companies and those left behind by 2035. The technologies to make planning smarter, faster, and more resilient already exist – but they must be combined with clear goals, a resilient data foundation, and organizational readiness for change. Those who lay the foundations today will not only be able to plan faster and more precisely tomorrow but can also act strategically before others can even react.

Figure: Results of an online survey of 119 SC managers with people responsibility, conducted on 01/2025 as part of a joint SAP KLU study

This article was written in collaboration with Prof. Kai Hoberg (Kühne Logistics University) in the context of the SAP-KLU study "The Supply Chain of the Future”.

The Future of Supply Chain Management | Part 3 of 6

2025-11-26T09:13:17.706000+01:00

Supply Chain 2035: Is Artificial Intelligence Determining Future Purchasing?

The third part of the joint study by SAP Business Consulting and Kühne Logistics University on the supply chain of the future deals with purchasing. We do not assume the future of purchasing looks the same for all companies. Rather, key trends and key technologies play a big role. Their impact varies greatly depending on the industry and business model.

The shaping trends for purchasing (as well as for the other parts of the supply chain) include the global economic situation and geopolitical tensions, the growing importance of sustainability, the scarcity of raw materials, and the changes for the people involved. These issues are not new, but they will play a major role in the coming years and will sometimes become even bigger.

Key technologies are in the focus, e.g. integrated transactional purchasing applications with new semantic data models, AI agents, AI-based data analyses, and insights from the evaluation of information from business networks. Artificial intelligence is not the only driving technology but will become increasingly relevant in the coming years. No process in modern purchasing organizations will remain unaffected by its influence. Against this background, purchasing organizations have to work out which trends and technologies are particularly important for their company to plan the path to a successful future.

The study conducted an online survey of 174 supply chain managers with people responsibility (April 2025). There was a clear discrepancy between ambitious visions for the future and the sad reality. On the one hand, 28% of respondents expected purchasing managers to play an important role in shaping corporate strategy and security of supply in 2035, while 31% expected buying organizations to achieve significantly better AI-driven outcomes with high quality data. On the other hand, only 12% say that procurement in their organization is now fully integrated into C-level decision-making, and only 3% think AI-powered purchasing analytics are great today.

How should this discrepancy be dealt with? And how to deal with the uncertainty of every look into the future? The clear recommendation for all purchasing organizations is to do their homework first, to take decisions and to perform a couple of no-regret moves. It is clear that only purchasing organizations following these five important points are optimally positioned for the coming years.

Firstly, clearly defined strategies and responsible people are in place for the most important categories and sub-categories. These category strategies must be kept up to date in suitable applications instead of gathering dust in presentations stored on shared drives. The strategies will be the trigger for activities, and the focal point for monitoring the progress of the activities.

Secondly, the operating model of the purchasing organization must be optimized by shifting personnel capacities from administrative activities to value-adding tasks. The goal is to work with a small but highly skilled workforce.

Third, clearly defined and standardized processes are required that exist in Business Process Model and Notation (BPMN) notation and are mapped in a process management solution, not a drawing tool. On this basis, simulations and comparisons of as-is and to-be can be carried out. Also, the processes can be linked to business requirements, test cases, application documentation, driving AI based automation in these related activities.

Fourth, modern purchasing applications should be used to support end-to-end processes, without media breaks and with a steadily growing degree of automation. An end-to-end flow of data and information is essential. Specifications and prices belong into the apps themselves, not in file attachments. These pieces of information are critical parts of the data that drives AI. Similarly, the apps must be integrated so data is not transferred manually or via bots.

Fifth, all relevant data from various procurement apps and third-party data must be available for analysis. The key is that this data is available on an appropriate platform and mapped to a semantic model. This is the only way to get an overview of the entire purchasing process, to derive in-depth insights and to drive more AI use cases.

The last point is of particular importance. Only if data on purchasing processes is available in structured form, across all purchasing processes, in good quality, will the purchasing organization have a solid basis for using AI functionalities of any kind, especially AI-driven analytics and AI agents, which can take on complex tasks on their own.

AI agents sound a bit like science fiction for many persons in the procurement space – that is why the figure below outlines an agentic AI scenario in strategic procurement. The CPO sets a task for a procurement orchestration agent which in turn sets tasks for other, more specialized procurement agents (e.g. spend analysis, sourcing activities, contract creation) to achieve an overall goal, the optimization of a specific sub-category.

Similarly, all other parties involved in the purchasing processes can be supported. Requestors can interact with an operational purchasing agents to quickly and clearly describe their needs. That operational purchasing agent reaches out to other agents leveraging existing contracts or catalogs, adhering to all purchasing guidelines, and covering topics such as account assignment, tax determination, and transportation costs. With the help of their own agents, suppliers will find it easy to participate in complex tenders, bid in Dutch or Japanese auctions, negotiate and sign contracts, and provide price lists and catalogs in specific formats to the purchasing organization. Since suppliers will use their own AI agents, there will be an increasing frequency of interaction between multiple agents.

In summary, it can be said that those purchasing organizations will be optimally positioned for the future, who correctly classify the relevance of future trends and technologies to their own business, and who conclude the five no-regret moves mentioned above.

Figure: AI agents implement a complex task in purchasing

This article was written in collaboration with Prof. Kai Hoberg (Kühne Logistics University) in the context of the SAP-KLU study "The Supply Chain of the Future”.

SAP EWM Labor Management (Engineered Labor Standards)

2025-12-02T00:29:25.572000+01:00

SAP EWM Labor Management is designed to help warehouses gain clear visibility into how efficiently work is performed on the floor. It enables businesses to define labor standards, track planned versus actual execution times, and measure processor performance for different warehouse activities. These standards can be based on various operational parameters such as weight, quantity, product attributes, or activity type, allowing flexible modeling of real-world scenarios. The functionality also supports tracking of both direct tasks and indirect labor efforts, giving a complete picture of workforce utilization. This blog focuses on demonstrating how engineering labor standards work within SAP EWM.

Configurations

1. Activate BC Sets:

T.code: SCPR20

To enable SAP EWM Labor Management (LM) functionality, SAP provides several LM-specific BC Sets. These BC Sets deliver predefined configuration content that supports various LM capabilities such as activity definitions, engineered labor standards, exception codes, performance measurement, and warehouse-level settings.

/SCWM/LM
/SCWM/LM_ACTIVITY
/SCWM/LM_ELS
/SCWM/LM_EXCEPTION_CODES
/SCWM/LM_EXTERNAL_PROCESS_STEPS
/SCWM/LM_NR_EPD
/SCWM/LM_NR_INDIRECT_LABOR_TASK
/SCWM/LM_PLAN_AREA
/SCWM/LM_PREPROCESSING
/SCWM/LM_WHS_NUMBER
/SCWM/PRC_LM_10
/SCWM/PRC_LM_15

2. Activate Labor Management
Path: SAP Customizing Implementation Guide -> SCM Extended Warehouse Management -> Extended Warehouse Management -> Labor Management -> Activate Labor Management

3. Define External Process steps

Path: SAP Customizing Implementation Guide -> SCM Extended Warehouse Management -> Extended Warehouse Management -> Labor Management -> Define External Process Steps

4. Assign External Process steps to warehouse Activity

Path: SAP Customizing Implementation Guide -> SCM Extended Warehouse Management -> Extended Warehouse Management -> Labor Management -> Assign External Process Step to Warehouse Activity

5. Assign Planned Activity Areas

Path: SAP Customizing Implementation Guide -> SCM Extended Warehouse Management -> Extended Warehouse Management -> Labor Management -> Assign Planning Activity Areas

6. Define Number Range for Performance Document

Path : SAP Customizing Implementation Guide -> SCM Extended Warehouse Management -> Extended Warehouse Management -> Labor Management -> Define Number Range for Performance Document

7. Define Delivery Date/Time for Preprocessing

Path: SAP Customizing Implementation Guide -> SCM Extended Warehouse Management -> Extended Warehouse Management -> Labor Management -> Define Delivery Date/Time for Preprocessing

8. Activate Change Documents in Labor Management

Path: SAP Customizing Implementation Guide -> SCM Extended Warehouse Management -> Extended Warehouse Management -> Labor Management -> Activate Change Documents in Labor Management

Activate Change Documents for Processor

Activate Change Documents for Performance Document

Activate Change Documents for Time and Attendance

9. Define Processor Types

Path : SAP Customizing Implementation Guide -> SCM Extended Warehouse Management -> Extended Warehouse Management -> Labor Management -> Processors -> Define Processor Types

10. Define Reporting Groups for Processors

Path: SAP Customizing Implementation Guide -> SCM Extended Warehouse Management -> Extended Warehouse Management -> Labor Management -> Processors -> Define Reporting Groups for Processors

11. Define Work Categories

Path: SAP Customizing Implementation Guide -> SCM Extended Warehouse Management -> Extended Warehouse Management -> Labor Management -> Processors -> Define Work Categories

12. Define Capacity Variants

Path: SAP Customizing Implementation Guide -> SCM Extended Warehouse Management -> Extended Warehouse Management -> Labor Management -> Shift Management -> Define Capacity Variants

13. Maintain Shift Management Defaults

Path: SAP Customizing Implementation Guide -> SCM Extended Warehouse Management -> Extended Warehouse Management -> Labor Management -> Shift Management -> Maintain Shift Management Defaults

14. Create a Processor

T.Code: /N/SCMB/PRR1

15. Assign User to BP created

16. Conditions for Engineered Labor Standards

T.code: /N/SCWM/LM_CE

17. Formula for Engineered Labor Standards

18. Determine Engineering Labor Standards

Path: SAP Customizing Implementation Guide -> SCM Extended Warehouse Management -> Extended Warehouse Management -> Labor Management -> Determine Engineered Labor Standards

Test :

Pre- requisites:
Create two deliveries and distribute them to EWM, ensuring each delivery has a different weight, one below 20 kg and one above 20 kg, to match the conditions we configured.

Let’s first take an example with a weight below 20 kg, execute it, and review the workload. After that, we’ll do the same for a case above 20 kg.

Maintain Inbound Deliveries

T.code: /N/SCWM/PRDI

As you can see, the delivery mentioned above includes a handling unit weighing 4.400 kg. Let’s create the warehouse task for unloading and execute it through the RF screen to validate the processor’s performance.

Let’s create a warehouse Task using Monitor-

T.code: /N/SCWM/PRDI>> Inbound Delivery>>Follow on Functions>>Warehouse Task

By clicking “Create,” the system generates a warehouse task to move the handling unit from the Door to the GR-ZONE (staging area). Next, let’s review the Warehouse Order for “LM Active.” As shown, the LM Active checkbox is enabled, indicating that Labor Management is activated for this step.

Now let’s execute the activity in RF screen
Path: Inbound>> Unloading>> Unload by Delivery

Upon scanning the HU, the system captures the start timestamp and initiates the timer to measure the total time taken by the associate or processor to complete the activity.

Once you scan the destination bin and confirm “Yes” for Completely Unloaded, the timer stops. Now let’s review the workload in the monitor.

The screenshot above is from the workload monitor, which displays the execution details. As shown, the processor “NICK WILDE” executed the unloading task, with the recorded weight of 4.2 kg. The planned execution time is 5 minutes, which is a critical value derived from the condition and formula maintained in configuration steps 16 and 17.

The monitor also shows the actual execution details start and end timestamps, planned duration, and the actual duration taken by the processor. In this example, the processor took 7.467 minutes to complete the task, compared to the planned 5 minutes. Based on this variance, the system calculated the processor’s efficiency as 66.96%.

Now, let’s take another delivery with a weight of 42.200 kg and review how the system determines the planned execution time for unloading, using the same configuration rules from steps 16 and 17.

Let’s execute the step and find out the performance

After execution, the planned time is 7 minutes, calculated based on the configured formula. The actual duration recorded is 18.317 minutes, resulting in an efficiency of 38.22%, since the processor took significantly longer than the planned time.

Through these scenarios, we validated how SAP EWM Labor Management calculates planned, actual, and efficient metrics using condition-based formulas. By executing unloading tasks for deliveries below and above the 20 kg threshold, we observed how the system dynamically derives planned execution time and evaluates processor performance via RF-driven timestamps. Importantly, these calculations are not limited to weight alone any relevant operational parameter (such as quantity, HU type, product group, packaging material, distance, activity type, or process step) can be incorporated into the condition and formula framework.

This flexibility allows organizations to tailor labor standards to real-world warehouse processes. Overall, these examples demonstrate how workload monitoring offers clear visibility into performance, highlights gaps between planned and actual durations, and enables data-driven optimization of warehouse efficiency in SAP EWM.

The Future of Supply Chain Management | Part 4 of 6

2025-12-03T10:22:29.612000+01:00

Supply Chain 2035: The Factory of the Future: Production in the Age of AI, Circular Economy, and Autonomous Systems

In the debate around the supply chain of the future, one aspect is taking center stage: production as the core of modern value creation for many companies. In times of global change, digital disruption, and growing demands for sustainability, it has a key role to play.

The production of the future is networked, flexible, and data driven. Leveraging technologies such as robotics, artificial intelligence, and the Internet of Things, it is increasingly growing together with other areas of the supply chain. This development is giving rise to a digital ecosystem in which real-time data availability enables faster and more accurate decision-making across the entire supply chain.

At the same time, production is gaining strategic importance: Companies are relocating manufacturing processes closer to sales markets to reduce their dependency on global disruptions – a trend that has been further amplified by the pandemic, energy crisis, and geopolitical tensions. In addition, there is increasing regulatory pressure and simultaneous efforts to produce more sustainably and to use resources more efficiently. All of this must be achieved while maintaining the lowest possible production costs.

In short, production is not merely one element of the supply chain, but the driving force behind its future. It determines how resilient, climate-friendly, and innovative businesses can operate tomorrow. The fourth part of the study by SAP Business Consulting and Kühne Logistics University on “Supply Chain 2035” therefore focuses on production.

The result of the study clearly highlights that the vision of a connected, AI-powered, and sustainable factory has long since arrived in the minds of many manufacturing managers – yet its implementation is still at the beginning in many places. There is a noticeable gap in maturity between the ambitions for 2035 and today's reality. While many organizations have already formulated a clear vision for the future, operational execution often remains fragmented and experimental.

The gap in sustainability and the circular economy is particularly striking. Circular production models and closed-loop material cycles are seen as key pillars of future value creation but are currently only implemented sporadically. Product designs that facilitate recycling, reuse, and modular construction are often still in the early stages of development.

Achieving digital continuity represents another major challenge. While connected, data-driven control is considered a prerequisite for the smart factory; data landscapes are often fragmented. The lack of interoperability and inconsistent standards makes it difficult to leverage production and supply chain information end-to-end.

Similarly, the deployment of artificial intelligence falls short of expectations. Many companies are experimenting with AI-powered applications, but the leap from pilot projects to widespread deployment is still pending. In addition, data quality and availability remain critical bottlenecks. Without clean, consistent, and connected data, every digital initiative remains piecemeal. It is only on this basis that intelligent systems can be scaled, and factories can be managed holistically.

In addition, organizational structures are undergoing transformation. Many companies are moving from experimentation to scaling – a process that requires not only new technologies, but also new skills, structures, and leadership approaches. Transforming production therefore entails not just digitalization but a fundamental shift in the interaction between people, machines, and data.

The study underscores that while the path towards the factory of the future is charted, many pillars are still under construction. Success will depend on consistently linking technological and organizational building blocks. The factory of the future will not emerge from isolated innovations, but from the integrated interplay of data, technologies, and resource-efficient processes.

Figure 1: What the Data Really Shows – Current Studies briefly

The factory of 2035 will differ fundamentally from what we are used to today. It is no longer a production system of siloed processes, but part of an intelligent, globally connected value-creating ecosystem. Data forms its central raw material – flowing through all systems in real time, connecting design, manufacturing, logistics, and recycling to a dynamic and adaptive cycle. The decisions are increasingly taken by artificial intelligence. It controls material flows, optimizes production plans, and detects deviations and disruptions early and preventively. Human expertise will thus shift toward strategic, creative, and monitoring tasks – AI becomes the central instance of production.

The physical factory itself becomes autonomous and modular. Production lines adapt in real time to changing orders, materials, or energy availability. Mobile robots, self-learning systems, and digital platforms orchestrate processes largely independently.

At the same time, the paradigm shifts from linear to circular production. Closed loops are emerging; products are designed in a modular manner and optimized for reuse or remanufacturing. Digital twins and IoT data connect the physical and digital worlds. Each manufacturing step, component, and usage generates data that makes the life cycle of a product fully traceable. This digital memory not only enables precise traceability, but also predictive maintenance, accurate material returns, and the continuous improvement of future product designs.

In summary: Industrial transformation is more than technological modernization – it is a cultural change. The factory of 2035 becomes a reality where data intelligence and circular thinking become part of the company’s DNA – not as an isolated project, but as a foundational principle. Those who actively shape this change not only strengthen their efficiency, but also their ability to reinvent themselves in an increasingly interconnected and dynamic industry.

This article was written in collaboration with Prof. Kai Hoberg (Kühne Logistics University) in the context of the SAP-KLU study "The Supply Chain of the Future”.

The Future of Supply Chain Management | Part 5 of 6

2025-12-09T10:10:24.187000+01:00

Supply Chain 2035: The New Era of Logistics: AI and Robotics in Action?

Part 5 of the “Supply Chain of the Future” series focuses on transportation and warehouse logistics where speed, reliability, and transparency determine costs, service, and resilience. The joint study by SAP Business Consulting and Kühne Logistics University clearly shows that companies who tightly integrate their processes digitally can prevent bottlenecks, reduce costs, and increase service levels. At the same time, however, the survey also makes it clear that the ambitions of many companies in the field of transport and warehouse logistics for 2035 are surprising – less because the technologies are lacking, but because their implementation is difficult alongside current operational challenges.

Global competitive pressure is increasing, and the pressure to act in terms of efficiency and cost optimization remains high. Energy, transport and staff are becoming more expensive, growth stimuli in the industrial sector are scarce in many developed countries, and efficiency is no longer achieved incidentally. Even small improvements in lead times, utilization, or error rates have an immediate impact on delivery dates and costs. In manufacturing companies, logistics is the backbone of stable processes, and standstill is therefore not an option. At the same time, the shortage of skilled workers often exacerbates the pressure to act: In Germany and other countries, jobs in warehouses and transport are increasingly difficult to fill, especially where processes are still lived manually and fragmented.

Tasks must be solved differently in the future – through higher automation, targeted assistance systems, the use of AI, and the courageous rethinking of processes. Against the backdrop of increased pressure to act, the results of a survey conducted in April 2025 show a mixed picture: 39 percent of respondents expect significantly stronger digitalization and data-driven warehouse processes with significant advances in responsiveness and transparency by 2035. 28 percent expect the use of advanced technologies such as autonomous vehicles or drones in transport and material flow scenarios; another 28 percent see AI agents as integral support tools for dispatchers, warehouse managers and drivers. In addition, 20 percent expect connected ecosystems with real-time data exchange across company boundaries. So, the direction is clear, but expectations seem moderate compared to the technological perspectives and already existing use cases. One of the possible reasons: the vision exists, but there is often a lack of clear implementation plans, as time, know-how and resources are scarce. Many companies are lagging behind with the modernization of their processes and systems and can therefore integrate the potential of innovative technologies and real-time data into their daily work and decision-making to the required extent.

At the same time, the survey reveals weaknesses in the current situation: Only 17 percent of the 180 surveyed companies use real-time data comprehensively to optimize warehousing. Only 9 percent are committed to real-time transparency in the transportation sector with accurate arrival time forecasts, and only 6 percent have established collaborative planning and execution models with partners. It is positive to mention that 19% of companies have already gained initial experience with generative AI – so this is a new field where progress has been made. Overall, however, the figures show that the comprehensive implementation of digital innovations in practice still falls significantly short of the possibilities.

What needs to be done? Successful innovation requires a clear, prioritized strategy and readiness to adapt processes and technologies, supported by comprehensive and high-quality data, intelligent analytics, and effective orchestration. On the one hand, a resilient digital backbone which integrates process and sensor data into a consistent situational picture across transport, yard and warehouse is required. Orchestration within the company as well as data exchange across company boundaries is crucial. This data forms the basis for AI-driven decision logic, such as predictive ETA forecasts, dynamic capacity and slot controls, and anomaly detection. AI agents should perform routine tasks, structure exceptions, and provide appropriate options for action. In addition, AI technologies are increasingly moving into physical logistics processes, where many experts expect further groundbreaking developments – both in the software and hardware sectors. This includes increasingly flexible robots and cobots paired with innovative business models (e.g., renting robots to flexibly absorb seasonal peaks). Fast integration and the ability to up- and downscale these solutions within existing systems are crucial.

To achieve the transition to innovative technologies and processes, it is essential to proactively address cultural resistance and implement effective change management. To drive innovation at every level of the business, leaders must deliver a holistic vision and actively involve employees in the change process. An open communication culture and continuous training of employees help to create a positive environment in which technological innovations are accepted.

Why do ambitions remain subdued? Many companies are running multiple transformation projects at the same time. Logistics is still often seen as a pure cost center and shifts backwards in priority – although it often delivers quick and measurable effects. The first step to systematically unlocking this potential can be, for example, to develop an innovation roadmap, depending on the business context. This identifies clear areas of action and bottlenecks and links business goals with the appropriate technology choice: not only “Which robot?”, but “What problem, which flow, which bottleneck – and which measure scales across locations?".

Our appeal: start now rather than catch up too late. The technologies are up and running, there is a need for action, and market pressure is increasing. Understanding the implementation as a continuous process with a clear roadmap, iterative scaling, and measurable effects closes the gap between ambition and reality – and transforms logistics from an often more reactive element of the value chain into a strategic advantage.

Figure: Vision 2035 and Reality 2025 in Transportation and Logistics

This article was written in collaboration with Prof. Kai Hoberg (Kühne Logistics University) in the context of the SAP-KLU study "The Supply Chain of the Future”.

AI-Driven Enterprise Data Architecture for Predictive Supply Chain Optimization in SAP Landscapes

2025-12-17T17:01:21.151000+01:00

Abstract :

AI-driven supply chain optimization has emerged as a critical capability for enterprises facing demand volatility, supplier uncertainty, and operational complexity. However, many initiatives fail to deliver sustainable value due to fragmented data foundations and poorly aligned architectures. This article presents a structured enterprise data architecture that enables scalable, explainable, and production-ready AI/ML solutions for supply chain optimization. By integrating SAP S/4HANA, SAP Datasphere, SAP HANA, SAP Analytics Cloud, and SAP BTP, the proposed framework supports predictive use cases such as demand forecasting, goods receipt delay prediction, and inventory optimization. The study emphasizes architecture-first design, feature engineering, and operational integration over algorithmic experimentation alone. Methodologies, workflows, equations, and comparative insights are provided to guide practitioners in transitioning from reactive reporting to predictive and prescriptive decision-making. The article aims to bridge the gap between enterprise data engineering and applied machine learning within SAP landscapes.

Introduction

Global supply chains are increasingly exposed to disruptions driven by market volatility, geopolitical risks, and evolving customer expectations. Traditional supply chain systems rely heavily on descriptive reporting and rule-based planning, which limits their ability to anticipate future risks and opportunities. As a result, enterprises are turning to artificial intelligence and machine learning to improve forecasting accuracy, inventory resilience, and operational responsiveness.

Despite growing interest, many AI initiatives struggle due to siloed data, inconsistent semantics, and lack of integration with business processes. This highlights the importance of enterprise data architecture as the foundation for successful AI adoption. Within SAP ecosystems, rich transactional data already exists, but it must be curated, governed, and structured for predictive consumption. This article introduces a practical architecture and workflow that enables AI-driven supply chain optimization by aligning data engineering, analytics, and operational execution within SAP platforms.

Purpose & Scope :

The purpose of this article is to define a practical and reusable enterprise data architecture for AI/ML-driven supply chain optimization in SAP environments. The scope includes data ingestion, harmonization, feature engineering, model lifecycle management, and business consumption of predictions. Rather than focusing on a single algorithm or tool, the article emphasizes architectural design principles that ensure scalability, governance, and explainability.

The scope covers core supply chain use cases such as demand forecasting, goods receipt delay prediction, and inventory optimization. It also addresses integration with SAP Analytics Cloud for planning and SAP BTP for operational workflows. Out-of-scope areas include low-level algorithm tuning and vendor-specific benchmarking. The goal is to provide architects, data engineers, and analytics practitioners with a structured blueprint that can be adapted across industries and SAP landscapes.

Background :

Supply chain analytics has traditionally focused on historical reporting and KPI tracking. While useful, these approaches are inherently reactive. Advances in machine learning enable predictive modeling, but success depends on data quality, feature consistency, and system integration. SAP ecosystems provide a strong transactional backbone, yet AI adoption requires rethinking how data is modeled, governed, and operationalized. Enterprise data architecture plays a critical role in transforming raw transactional data into predictive intelligence.

Category Identification :

This work falls under the categories of Enterprise Data Architecture, AI/ML-Driven Analytics, and Supply Chain Optimization. From a research perspective, it aligns with applied industrial analytics and decision support systems. From a practitioner perspective, it addresses architecture, governance, and operational integration within SAP platforms. The article bridges research-oriented machine learning concepts with enterprise-grade system design.

Research Background :

Existing research on supply chain optimization highlights the effectiveness of machine learning in demand forecasting, lead-time prediction, and inventory management. However, much of the literature assumes clean, centralized datasets and overlooks enterprise integration challenges. Practitioner studies reveal that architecture misalignment, feature inconsistency, and lack of monitoring are primary causes of AI failure in production. This work builds on prior research by explicitly embedding ML workflows within enterprise SAP architectures, emphasizing governance, explainability, and lifecycle management as first-class concerns.

Novel Contribution :

The novel contribution of this article lies in its architecture-centric framing of AI-driven supply chain optimization. Rather than proposing a new algorithm, it introduces a structured enterprise architecture that operationalizes ML models within SAP ecosystems. The integration of feature engineering, MLOps, and business consumption into a single workflow differentiates this work from isolated analytical studies.

Methodology & Workflow :

Architectural Workflow

The methodology follows a layered enterprise architecture:

Layer	Components	Function
Source	SAP S/4HANA, External Systems	Transactional data
Data Foundation	SAP Datasphere, SAP HANA	Harmonization & semantics
Feature Layer	HANA Views, Python	Feature engineering
ML Layer	HANA PAL, Python ML	Training & inference
Consumption	SAC, Fiori, APIs	Decision support

Feature Engineering

Key predictive features include:

Feature Type	Examples
Temporal	Month, seasonality index
Vendor	Reliability score, failure rate
Logistics	Transit variance, route delay
Inventory	Stock turnover, safety stock

Core Equations

Demand Forecasting (Regression):

Delay Probability (Classification):

Inventory Safety Stock:

Model Lifecycle

Models are trained on historical data, validated using back-testing, deployed via SAP BTP APIs, and monitored for drift and accuracy degradation. Governance ensures role-based access and auditability.

Comparative Insight

Compared to traditional rule-based planning, the proposed architecture enables proactive decision-making. Unlike isolated ML implementations, this framework ensures consistency, reuse, and operational stability. Centralized feature engineering and governed data models significantly improve prediction reliability while reducing duplication across teams.

Potential Applications

Applications include predictive demand planning, supplier risk scoring, dynamic inventory optimization, and logistics delay mitigation. The architecture is adaptable across manufacturing, retail, and logistics industries.

Broader Implications

Beyond supply chain optimization, this architecture demonstrates how enterprise data foundations enable trustworthy AI adoption. It supports explainability, regulatory compliance, and organizational trust in AI-driven decisions.

Full References

References include foundational works on supply chain analytics, enterprise data architecture, and applied machine learning, along with SAP documentation on Datasphere, HANA PAL, and Analytics Cloud. These sources collectively inform the architectural and methodological choices presented.

Ethics + Review Framework

Ethical considerations include data privacy, bias mitigation, and transparency of predictions. Models are reviewed using explainability metrics, accuracy thresholds, and periodic audits. A human-in-the-loop framework ensures AI augments, rather than replaces, expert decision-making.

The Future of Supply Chain Management | Part 6 of 6

2025-12-18T11:58:44.397000+01:00

Supply Chain 2035: How Will Everyday Work Change in the Future of the Supply Chain?

In the first five parts of this joint series, SAP Business Consulting and Kühne Logistics University examined various aspects of the supply chain of the future. In this sixth and final part, we discuss how the everyday work of supply chain managers will change. Key technological drivers include the rapid spread of artificial intelligence (AI) and cloud technologies. This is reinforced by a socioeconomic trend: a shortage of skilled workers. While the knowledge required to handle many tasks is becoming increasingly specialized, demographic developments are shrinking the available workforce. This is already leading to highly polarizing views: on one side, some fear that many workers could lose their jobs to AI; on the other, many see AI as the best hope for staying competitive and overcoming new challenges amid the emerging shortage of skilled employees.

The reality will vary by employee and company. Although reliable forecasts are difficult given the rapid pace of change, three developments are already noticeable today. First, the range of tasks in Supply Chain Management will shift from operational to strategic activities. Second, the interaction between people and technology will change significantly. Finally, education and training for the Supply Chain Managers of the future will – and must – change.

Shifting the range of tasks from operational to strategic activities

With ongoing digitalization and use of AI, manual work is already shrinking in employees’ daily routines. By 2035, many companies will automate most routine decisions across the end-to-end supply chain – from demand forecasting and purchase order processing to dynamic route planning in logistics. Time once spent on “transaction” work – collecting, cleaning, reviewing, and documenting – will shift to coordinating a system of data platforms, decision intelligence, and AI agents.

This shifts the focus of tasks:

From execution to design: People set objectives, guidelines, and policies for how systems act, and design the decision logic (“guardrails”) for trade-offs across price, volume, service, and sustainability.
From reaction to anticipation: The focus moves to scenario planning, Digital Twin simulations, and portfolio choices (e.g., higher service level versus less capital tied up).
From KPIs to Outcomes: Move from purely operational metrics (e.g., on-time delivery at the level of individual orders) to results-focused targets such as risk-adjusted contribution margin, customer experience, resilience levels, and sustainability impact.

The day-to-day work of 2035 becomes more strategic: In the morning, the Control-Tower environment offers an overview of the situation with current risks, opportunities, and suggestions from AI agents. Employees talk with colleagues and copilots to understand the impact of different scenarios (e.g., a supplier outage on high-margin segments), adjust policies (such as safety stocks in critical regions), run experiments in the Digital Twin, and make a few exceptional but high-impact decisions. This is accompanied by new roles such as Network Orchestrator, Scenario Strategist for end-to-end decisions, and Data Steward.

Changing how people and technology work together

Based on these changes, it’s clear that technology will move from being just a tool for completing tasks to being a partner working alongside people. The following focus areas are already emerging:

Copilots instead of masks: natural-language, context-enabled assistants explain suggestions, simulate alternatives, and implement policy changes. Prompting shifts from ad‑hoc commands to structured briefs that state the goal, constraints, and evaluation criteria.
Explainability and uncertainty: Recommendations include clear justifications, key drivers, confidence levels, and uncertainty ranges. Explainable AI and standard why/what‑if reports become the norm to build trust and support audits.
Continuous learning: Systems learn from everyday use. Employees provide structured feedback that flows into the models and further improves the results.

As employees hand over more control and responsibility to technology, governance needs extra care. This includes using agent-on-agent controls (agents that monitor other agents) and setting clear human release criteria and approval steps.

The training of the supply chain managers of the future

The described role shifts and the new form of interaction require a profound change in training and further development. The ideal supply chain manager of the future will mix deep business experience with fluency in new tools and adaptability. As one interviewee put it: “We need a person who is experienced and young. Experienced, in the sense that she knows the company and the products and stays calm when something unexpected happens. Young, in the sense that she knows all new tools and technologies and is flexible enough to adapt to new possibilities. “

Although this can be covered by a team rather than by individuals, it also suggests that T-shaped profiles will be in high demand in the future: a broad set of basic digital competencies combined with deep domain expertise. Everyone in the future supply chain should understand the end-to-end processes, identify the dependencies, optimize the network as a whole, and stay current on rapid tech advances.

Therefore, we recommend that in the future training and further training be more cross-functional – with stations in purchasing, production, logistics, sales and data & analytics. At the same time, employees should be trained with technology-enabled learning in the context of work. This includes building learning environments where new technologies, methods, and concepts can be applied and reflected directly in the work process. The focus is less on formal training, but on the ability to experiment with digital tools, to critically question their results and to derive impulses to improve processes and decisions.

Conclusion: The Supply Chain Managers of 2035

In 2035, Supply Chain Managers will spend less time on routine transactions and more on building and running complex, connected decision-making environments. Technology will act as a true team member – preparing options, executing decisions, and learning from outcomes within clear guardrails. People remain the decisive success factor: bringing sharpened strategic judgment, strong data and technology fluency, a deep sense of responsibility, and the ability to orchestrate ecosystems. Organizations that embrace this transformation now will not only become more productive and resilient but also build supply chains that deliver sustainable competitive advantage.

Figure (AI generated): The interaction between humans and technology is shifting from a focus on transactional data to dialog-based copilots.

This article was written in collaboration with Prof. Kai Hoberg (Kühne Logistics University) in the context of the SAP-KLU study "The Supply Chain of the Future.”

Part 1: How Future Supply Chain Management Works
Part 2: How Data, AI, and Agents are Changing Supply Chain Planning
Part 3: Is Artificial Intelligence Determining Future Purchasing?
Part 4: The Factory of the Future: Production in the Age of AI, Circular Economy, and Autonomous Systems
Part 5: The New Era of Logistics: AI and Robotics in Action?

Resolve Overprotection

2025-12-18T15:13:54.485000+01:00

Resolve Overprotection

Customers easily understand the benefits of supply protection. Protecting quantities for specific customers, regions, or any defined group is a common use case in shortage situations (to learn more about supply protection check the blog titled Supply Protection in a Nutshell).

However, protecting too much can run into the issue known as overprotection.

S/4HANA 2025 offers a solution to identify and mitigate overprotection.

What is Overprotection?

Every requirement, such as a sales order, must adhere to the restrictions defined by supply protection. If there is stock available but not every request receive confirmation due to supply protection, this situation is referred to as overprotection.

The following illustration helps to understand the definition of overprotection.

Illustration 1: Overprotection

On the left side of the illustration, a blue bar shows the available stock or supply. Colored boxes indicate the defined remaining protection for France (purple), Italy (green) and Germany (orange). Grey boxes represent the restrictions from the perspective of the related supply protection objects. In this example, the horizontal red line represents the available stock. For a demand matching the supply protection object for Germany, the grey boxes for Italy (3000) and France (4500) are to be considered as restrictions. Therefore, even a demand matching the priority 1 protection group for Germany doesn’t receive confirmation. All other potential demands also exceed the red line. This means, in all cases, the restrictions surpass availability.

Other critical Situations

Illustration 2: Blocked SUP Objects

Assuming the stock is slightly higher, there won’t be overprotection. Demands that match the priority 1 protection for SUP Germany could receive confirmation. However, even this situation might still be considered as critical because complete markets, such as France and Italy, are blocked. In some cases, it might be acceptable that complete supply protection objects are blocked, but, in other situations, this is likely unintended and treated as a critical problem.

If there is more supply (illustration 3), supply protection works as intended – some demands are blocked, and only high-prioritized demands receive confirmation. Depending on the situation, this can also be seen as critical, especially if the planned protection is defined with the setting Restriction outside planned Protection. It’s obvious that other demands (not matching a protection) won’t receive confirmation.

Illustration 3: Blocked SUP Groups

Identify Overprotection and other critical Situations

Overprotection was considered as issue because it was difficult to identify. It doesn’t appear in any app that shows availability. Within a sales order, users can see that supply protection prevents order confirmation. However, they can’t see that all other orders for the same material-plant-combination are also blocked.

Overprotection can occur at the Restricted Product level. A restricted product is the combination of a plant-material and the defined planning level, such as plant/storage location, plant/segment, or plant/batch, for which supply protection is defined and active.

Since supply protection is only a virtual restriction, depending on the context, the system didn’t indicate critical situations involving blocked supply protection objects.

This has changed with the new Resolve Overprotection app which displays overprotection and other critical situations. Additionally, users can use Situation Management to receive alerts.

Alerting for Overprotection

Customers can use the situation template CA_ATP_SUP_OVERPROTECTION to customize alerts based on their needs. Users can choose whether to receive alerts for overprotection situations or for other critical situations. Situation Management offers various settings for alert recipients, technology, and relevant criteria.

App Resolve Overprotection

The Resolve Overprotection app provides users with a quick overview of the restricted products. By default, it sorts these restricted products on the criticality of the check result status.

Illustration 4: The "Resolve Overprotection" App

Illustration 5: “Resolve Overprotection” Result List

Users can view the planning level of the restricted product, the check result status, and the number of blocked supply protection objects and blocked supply protection groups.

They can also navigate to the details of a restricted product. Here, users can see if and how much available quantity is accessible for each related protection group.

Illustration 6: Restricted Product Details View

Mitigate Overprotection

Starting from the overprotection details, users can navigate to the individual supply protection objects. They can adjust the protected quantities to improve the situation. Returning to the details page of the restricted product displays the updated check result status after retrieving the updated data using the Refresh button.

As an alternative, you can also switch to edit mode by clicking the Edit button to simulate different mitigation options. Three different options are displayed:

Prioritized reduction
The planned protection of all time buckets is reduced by a certain percentage. Protection groups with high priority get a low percentage reduction, while protection groups with low priority will get a high percentage reduction.

Zero protection for the lowest priority
Only the protected quantities of the protection group with the lowest priority for each supply protection object are reduced. The reduction is 100%, completely removing the restriction by this group.

Reduced Restriction Quantity by Percentage
The protected quantity of all related time buckets is reduced by the same percentage.

Users can simulate the different options and decide whether to save the changes or choose another mitigation strategy. The app provides information if there is a positive available quantity accessible for the individual protection groups. There isn’t a clear recommendation for the best mitigation strategy. Users can apply the same or a different mitigation strategy after the initial one.

Illustration 7: Mitigate Overprotection

“Overprotection” not Addressed by the App

The Resolve Overprotection app identifies situations where supply protection restrictions exceed the available stock or supply. However, some lock issues won’t be identified as overprotection.

For example: Suppose there is protection for one or more groups that could use the protection because there is unrestricted stock. However, matching demands are blocked by other restrictions, such as a credit block or a fully consumed product allocation (PAL) bucket. In this case, these blocked groups can’t use the defined protection, and other demands from other groups are also blocked by this non-consumable restriction. This issue arises from the setup of protection combined with the setup of product allocation.

Summary

With the Resolve Overprotection app, along with its alerting feature, makes it easy to identify issues like overprotection and other critical situations. You can also easily mitigate the issues and overcome mutual blocking caused by supply protection.

Nevertheless, it is crucial to set up supply protection correctly and protect only minimum quantities. There are best practices available to avoid overprotection already at an early stage.

The setup of Supply Protection (SUP) together with Product Allocation (PAL) should be done with care. It’s recommended to use similar characteristics when defining groups in SUP and PAL. In PAL, you typically define a maximum quantity while supply protection represents minimum quantities. The interplay of both methods works best when there’s a corridor between these quantities.

Video: Resolve Overprotection

Release Navigator for Supply Chain Management - What's New in 2602

2026-01-14T20:48:33.329000+01:00

Welcome to the 2026 Supply Chain Management Community!

We’re excited to share the latest enhancements coming to the Release Navigator for Supply Chain Management, including new enablement content and expanded product coverage. Our newest update occur over the next few weeks, finalizing on February 20, 2026, bringing you fresh resources designed to help you get the most value from each release.

Tool Overview

The Release Navigator for Supply Chain Management, provided through SAP Enterprise Support, is your central hub for understanding and managing new software releases across the supply chain portfolio.

This resource enables you to:

Easily explore release content through an organized, intuitive interface.
Quickly locate relevant materials, including updates, tools, and documentation for your SAP solutions.
Access guided enablement, such as webinars, workshops, and training modules, to help you fully adopt new features and meet your operational goals.

We welcome your ideas for future enhancements—share your suggestions using our brief, anonymous two‑question survey.

New Products

With the 2602 update, we’re pleased to introduce SAP Logistics Management into the Release Navigator.

SAP Logistics Management is a cloud-based logistics solution that unifies warehouse and transportation management to streamline operations through network‑centric collaboration. You can now explore the latest features, innovations, and enablement materials directly within the Release Navigator.

Content Insights

Supply Chain Management includes a wide spectrum of products, and staying aligned with the latest documentation can be challenging. To make this easier, we’ve compiled a summary of the newest and continuously updated content available to you.

New Content

Release Blogs via SAP Community:

SAP Asset Performance Management
SAP Field Service Management
SAP Integrated Business Planning
SAP S/4HANA Cloud Public Edition for Transformation Management
SAP S/4HANA Cloud Public Edition for Warehouse Management

Walk-Thrus via Release Navigator:

What's New in 2602

Continuously Update Content

SAP Help Portal Documentation:

SAP Asset Performance Management
SAP Business Network for Asset Collaboration
SAP Business Network for Logistics
SAP Business Network for Procurement & Supply Chain
SAP Digital Manufacturing
SAP Integrated Product Development
SAP S/4HANA Cloud Public Edition for Transformation Management
SAP S/4HANA Cloud Public Edition for Warehouse Management

Retiring Content

Release Blogs via SAP Community:

SAP Service & Asset Manager

Innovation Schedule

To ensure a smooth user experience, the Release Navigator introduces new user‑experience updates once per year in August, alongside refreshed release content.

The Release Navigator will update automatically over the coming weeks—no action is required on your end.

Disclaimer: These dates are for informational purposes only and may change. The Readiness@Scale team has the final decision over any UX modifications.

Connect with Us

Your feedback helps shape future enhancements to the Release Navigator. We invite you to:

Complete our feedback survey
Share ideas or requests in the comments section
Learn more about the Readiness@Scale team
Explore additional content

Thank you for your continued engagement and support.

Best Run,
Readiness@Scale

SAP Enterprise Support

Auto Packing with Nested Handling Units in Inbound Delivery – SAP S/4HANA Public Cloud

2026-01-20T06:49:22.619000+01:00

Automatic Packing with Nested Handling Units in Inbound Delivery – SAP S/4HANA Public Cloud

Nested Handling Units (HUs) mean packing one Handling Unit inside another Handling Unit.
For example, cartons packed into a pallet, where each carton and the pallet have their own HU number.

Step 1: Maintain Packaging Material Types. The menu path is shown in the screenshot below.

Note: I have created 2 Packaging Material types:

Pallet
Carton

Step 2: Define Material group for Packaging Materials

Created Packing Material groups for Pallet and Carton.

Step 3: Define Allowed Packaging Materials

Map the Packing Material Type with the Packing Material Group

Note - For Nested HUs, it is mandatory to cross-map Packing Material Types with Packing Material Groups.

This means:

Carton groups must allow pallet types, and
Pallet groups must allow carton types,
so that inner HUs can be packed into outer HUs without errors.

Step 4: Assign Number Range intervals to Packaging Material Type

Assign a number range to the created Packing Material Types for the respective plant and storage location.

Step 5: Created packing materials in MM01 using Material Type VERP and assign the relevant Packing Material Group and Pack. Material Type in the “Matl Grp Pack. Matls, Packing Material type” fields under the Basic Data view/Sales: General Plant data. Additionally, assign the appropriate HU Type and also created SFG Material.

In this scenario, two packing materials are created:

CARTONBOX with Packing Material Type CARTON
PALLET1 with Packing Material Type PALLET
42 with SFG type.

Step 6: Create Packing Instructions using T-Code: POP1

Please note that the Handling Unit (HU) number is generated only for the Load Carrier maintained in a Packaging Instruction.
As a result, to generate HUs for both cartons and pallets, each must be defined as a Load Carrier. Since a Packaging Instruction can contain only one Load Carrier, separate Packaging Instructions are required for cartons and pallets.
Based on this approach, the following Packaging Instructions have been created:

Created Packing Instruction “AutoPack”, added Components for Cotton Box.

Maintain check profile.

Check and save.

A separate Packaging Instruction is created for the pallet by adding the PALLET material as Item Category “P” and the AUTOPACK packaging instruction as Item Category “I”. Maintain the required quantities, then check and save.

Step 7: For automatic determination of the Packaging Instruction, create a condition record using T-code POF1.

Select “RCPT” standard for Inbound delivery.

Assign the SFG material and the ‘PALLET’ Packaging Instruction in the determination record.

Step 8: Enable Auto Packing in Delivery Types

Selected Standard “EL” delivery type for Inbound delivery
Enabled “Automatic Packing” and save

The above are the configuration steps. The functional flow is explained below.

Created PO by selecting Confirmation key.

Create the inbound delivery, select the line item, and click ‘Auto Packing’. The system automatically packs the material.

In this scenario, Material 42 – SFG Material is maintained.

Each Carton Box is defined to carry 25 KG of SFG material.
Multiple Carton Boxes are then packed into a Pallet, where one pallet contains 4 carton boxes.
A Purchase Order is created for 100 KG of 42 SFG material.

During Inbound Delivery Auto Packing, the system performs the packing automatically as follows:

100 KG of SFG material is packed into 4 Carton HUs (25 KG each).
These 4 Carton HUs are nested and packed into 1 Pallet HU.

This demonstrates Nested Handling Unit (HU) auto packing, where inner Carton HUs are packed into an outer Pallet HU, and each level is assigned its own HU number automatically by the system.

SAP Practice and Certification Journey

2026-01-27T11:44:54.201000+01:00

In today’s fast-evolving logistics industry, digital transformation and integrated systems play a critical role in ensuring efficiency, transparency, and sustainability. As someone who has received formal education in logistics and is actively continuing my professional development in this field, I strongly believe that learning SAP S/4HANA Transportation Management (TM) is a natural and strategic step in my career journey.

My motivation to learn SAP stems from my desire to deepen my understanding of logistics beyond operational processes and into system-driven decision making. Transportation Management is one of the core pillars of supply chain operations, and SAP S/4HANA TM provides an end-to-end perspective—from planning and execution to cost settlement and performance analysis. Gaining hands-on practice with this system will allow me to better understand how logistics processes are designed, optimized, and managed within global organizations.

Additionally, learning SAP S/4HANA TM will help me develop a more analytical and structured mindset. Exposure to different ERP systems not only enhances technical competence but also enables logistics professionals to think holistically, connecting operations, finance, and strategy. I believe this knowledge will significantly contribute to my ability to analyze complex logistics scenarios and propose effective, data-driven solutions.

Through SAP Practice and Certification, my goal is to strengthen my professional skill set, bridge the gap between theory and real-world applications, and position myself as a more capable and future-ready logistics profession

Accelerating SAP Digital Manufacturing with SAP Test Automation by Tricentis in SAP Cloud ALM

2026-01-30T15:17:23.978000+01:00

In today’s fast‑moving manufacturing landscape, standing still is not an option. With quarterly SAP Digital Manufacturing releases, evolving shop‑floor processes, and constant pressure to scale, maintaining a stable and reliable system becomes a challenge. Every rollout, upgrade, or plant onboarding demands repeated testing and when done manually, this quickly turns into a time‑consuming bottleneck.

The good news? SAP Test Automation solution by Tricentis allows you to accelerate delivery, tighten quality, and free your team from repetitive validation work, and many SAP customers can get started using tools they already own. Whether you’re operating one plant or fifty, automation helps ensure your SAP Digital Manufacturing processes run smoothly, release after release.

Why automate testing in SAP Digital Manufacturing?

Across SAP Digital Manufacturing implementations, similar challenges appear:
manual regression cycles are time‑intensive, multiple process variants must be validated repeatedly, and quarterly SAP releases require consistent checks to ensure integrations and shop floor processes behave as expected.

Automation tackles these pain points by:

Reducing repetitive manual testing, especially across multiple plants
Shortening regression cycles, enabling faster rollout of new features
Increasing stability and coverage, even as processes evolve
Freeing business users for meaningful testing, not repetitive validation

Instead of re-running known scenarios every quarter, teams can focus their time on what’s new or business‑critical.

🧰 What’s in your toolkit? (You already own it!)

If you have SAP Enterprise Support, cloud edition(s), you likely already have access to:

SAP Cloud ALM

Your central hub for test preparation, planning, orchestration, and defect management.
✔ Included in your subscription
✔ Up to 24 GB data

For more information, please refer to usage rights for SAP Cloud ALM.

SAP Test Automation solution by Tricentis

Included entitlement with:

✔ 5 named users

✔ 5 execution agents

✔ 500 automated test runs/month

✔ 2.5 GB storage

To learn more, please refer to usage rights for SAP Test Automation solution by Tricentis

The setup is done very easily. Tricentis integration in SAP Cloud ALM can be ready in just minutes. Click here to learn more.

From manual to automated: How it works

1️⃣ Document scenarios in SAP Cloud ALM

Capture end‑to‑end flows, from order creation all the way to shop‑floor execution.

2️⃣ Mark tests as automated

SAP Cloud ALM directly links your test cases to Tricentis for execution.

3️⃣Scan and build automation in Tricentis

Capture Operator PODs and SAP DM UI elements
Build automation flows with drag‑and‑drop modules
Parameterize your data for reuse across plants, variants, and releases

4️⃣ Execute & analyze

Run your automated tests from Tricentis or SAP Cloud ALM
Review logs, screenshots, and step‑by‑step recordings
Track defects and trigger re‑runs directly in SAP Cloud ALM

Best practices

Begin with stable, repetitive processes
Combine UI automation with API validations
Use parameterized data — avoid hard-coded materials/SFCs
Keep automation updated with each release
Assign clear ownership for automation assets

Common pitfalls when starting test automation

Implementing SAP Test Automation in SAP Digital Manufacturing is not only a technical task. It requires the right mix of business process knowledge, test methodology expertise, and automation skills. A small, dedicated expert team is essential to set the foundation correctly from the start, and many organizations benefit from engaging specialized partners from SAP’s growing test automation ecosystem.

There are several pitfalls that frequently occur when customers begin their automation journey — from organizational setup to preparation and script implementation. A few key ones include:

🎯 Starting automation without a clear test strategy or SAP DM–specific objectives
Ensure clarity on what you want to achieve, how testing will be organized, and which processes automation must protect.

👥 Assigning automation solely to business users
Testing requires dedicated ownership; business experts contribute scenarios, but automation should be owned and maintained by a skilled team.

🌱 Trying to automate everything at once
Start small, focus on stable and high‑value scenarios, and grow automation iteratively.

🧩 Using static or hard‑coded data
This limits flexibility across plants, variants, and releases, and reduces confidence in test results.

These represent only a subset of common challenges. We will continue expanding this guidance, and customers are encouraged to collaborate with partners who bring proven methodology and automation experience.

Further resources

What's next?

To help you build a strong SAP Test Automation foundation in SAP Digital Manufacturing and make the most of it, we will be launching a dedicated webcast series soon. Stay tuned, more details are coming soon! 🚀

Characteristics-Based Planning: Shelf Life Meets Compliance

2026-02-01T18:06:03.388000+01:00

The Problem: When Systems Can't See What Matters

In pharmaceutical, food and beverage, and consumer goods industries, not all units are equal. Products cannot be any more treated as homogeneous SKUs. Batches have remaining shelf life. They carry regulatory approvals for specific markets. They age every day the planning system runs

In regulated industries, batches within the same SKU aren't interchangeable. A pharmaceutical batch with 60 days of remaining shelf life approved for US and EU markets is not the same as a batch with 90 days remaining approved only for EU. They cannot be treated equally in planning.

The result? Planners export data to spreadsheets, manually check approval matrices and expiration dates, then adjust plans accordingly. When demand changes or new batches arrive, the process repeats.

The consequences cascade differently across organizations:

Business leaders see the waste-compliance squeeze. Conservative planners avoid compliance risks by scrapping usable inventory. Aggressive planners maximize utilization but this can trigger audit findings

IT teams inherit the integration burden. ERP systems track batch expiry dates. Regulatory/Quality management systems hold approval records. Planning generates demand plans. Someone has to connect these further into feasible Supply!

What's Changed:

CBP with enhanced Shelf-Life planning is now available for SAP7F and Harmonized Planning Area (IBP2)!!

Release 2602 marks a significant step forward. Characteristics-Based Planning (CBP) is now available on the Harmonized planning Area and we've fused the two critical dimensions: Flexibility of CBP and shelf life management --> unified with regulatory approval constraints.

With this here is what you get:

Guaranteed Compliance: Regulatory + Quality(Shelf-Life) constraints evaluated simultaneously - no more choosing between fresh or compliant.

IBP now plans at the batch + CBP characteristic level together. It understands that Batch A has 60 days remaining shelf life and is approved for EU and US markets, while Batch B has 100 days remaining but only US approval. When US demand arrives requiring minimum shelf life of 80 days, the system automatically routes Batch B to this demand - all within the standard planning run.

No spreadsheet exports. No manual approval checking. No custom code calculating time decay.

The technical challenge this solves is significant: as you add characteristics (regulatory approvals, quality grades) with Shelf life, the combinatorial complexity explodes. Planning must evaluate every supply against every demand requirement while time continues to erode shelf life. The system handles all constraints natively, during your regular planning cycles.

Other Benefits

Enhanced Shelf-Life Planning capabilities
Ability to use CBP and Enhanced Shelf-Life planning independently or together!
For, SAP7F, enable the enhanced function by setting a global configuration parameter
For HPA, only the new enhanced Shelf-Life solution will be available – No special parameter for activation

Enhanced Shelf-Life Planning capabilities include:

improved precision with Lot Sizing: Old Min/Max building versus new lot grouping id as used for CBP
Shelf-Life Propagation to components and inheritance: Finished product’s usable life is constrained by the remaining Shelf-Life of a critical lower-level BoM component (currently restricted to one component)
there's more to come in subsequent releases

Business Outcomes:

Executives can finally eliminate the waste-versus-compliance tradeoff. The organization reduces spoilage while maintaining its compliance posture. Supply chain leaders reclaim planner productivity. Teams stop spending hours manually routing batches and start focusing on exception management and strategic decisions. IT organizations operate using standard SAP capabilities including integration to ERP --> TCO drop potential.

Whats Next:

Shelf life planning with regulatory compliance is available now in SAP IBP 2602. Watch our demo video to it in action, or connect with your SAP team to assess fit for your organization.

Video: https://www.youtube.com/watch?v=tFybO70a0Ls

This release aligns into our harmonized ways for planning and paves the way forward for subsequent releases where we build on this for some complex use cases. Stay tuned!

This is the third blog in the series: First two blogs can be found here:

Unlock the Potential of Characteristics-Based Planning (CBP) in SAP IBP

Breaking the Silos:Orchestrate end to end Characteristics Based Plans across Planning and Execution

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Accelerate Your Role in SAP Supply Chain Transformation: Join Cloud Acceleration Partner Sessions

2026-02-03T22:04:53.556000+01:00

Today’s supply chains are evolving faster than ever—and SAP’s customers are moving with urgency to modernize both their manufacturing and planning landscapes. To help partners lead these critical transformations, SAP is offering two dedicated sessions that introduce the SAP Supply Chain Management Evolution Program, designed to equip you with the insight and confidence needed to guide customers through their next steps.

Why These Sessions Matter

Across manufacturing and planning, SAP customers are shifting from long‑established on‑premise solutions to the next generation of cloud‑based platforms:

SAP ME/MII → SAP Digital Manufacturing
SAP APO and ERP-based planning → SAP Integrated Business Planning (SAP IBP)

Both transformations represent major opportunities for partners. But they also require a clear understanding of the Customer Evolution Program—SAP’s structured approach to helping customers modernize with minimal risk and maximum long‑term value.

What You’ll Gain by Attending

Each session is designed to give partners a practical understanding of:

How SAP is guiding customers through cloud transformation in manufacturing and planning
The purpose and structure of the SAP Supply Chain Management Evolution Program
The vital role partners play in helping customers adopt SAP Digital Manufacturing and SAP IBP
Real examples of customer transformation journeys and what drives success
Where partners can access resources, enablement, and next steps

By attending, you’ll leave with a clearer view of how to position the program with your customers—and how to identify opportunities to accelerate their transformation journey.

Register for the Sessions

We encourage all partners supporting manufacturing and supply chain planning to join:

February 26 – SAP ME/MII → SAP Digital Manufacturing
March 4 – SAP APO/ERP → SAP Integrated Business Planning

These sessions are your opportunity to understand how SAP is helping customers evolve—and how you, as a partner, can play a central role in that journey.

Welcome to our Supply Chain Management Learning Group!

2026-02-13T10:45:52.154000+01:00

WELCOME!

We are very excited to launch this brand-new learning group for all learners of SAP solutions in the Supply Chain space.

This is a space where learning, collaboration, and real-world supply chain experience come together — a place where ideas grow, questions are welcome, and learning becomes a shared journey rather than a solo effort. Whether you ask questions, share insights, or simply follow along and learn at your own pace, you are part of this community.

Here you can connect with SAP experts and fellow learners across all SAP Supply Chain topics and be among the first to hear about new courses, live sessions, and certification updates.

The focus of this community is learning. It complements the existing SAP product communities, which remain the right place for detailed product discussions and system troubleshooting. Here, the emphasis is on understanding concepts, developing skills, and progressing along your SAP Supply Chain learning journey.

This is a safe space to discuss SAP learning content such as courses, learning journeys, hands-on exercises, and certifications. No question is too basic — we are all learners here. Questions related to learning systems and training exercises are very welcome.

The community is moderated by SAP experts who are involved in creating and delivering official learning content and live sessions, so you are connecting directly with the team behind the training.

The solutions that we plan to cover in this community include:

Production Planning
Manufacturing & Digital Manufacturing
Project Systems & Portfolio and Project Management
Extended Warehouse Management
Variant Configuration
Product Engineering

Before you leave, make sure to click Join Group so you can ask questions, participate in discussions, and share your thoughts. Also remember to subscribe to the learning community so you don’t miss any updates.

SAP Community provides a separate place for discussions and questions, but we thought we’d try and simply things for you by combining them. After all, a question often leads to a discussion and vice-versa. So please use the Discussions button to start a discussion and to ask questions.

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Let’s connect, learn, and grow together – we look forward to building this Supply Chain learning community with you.

Upcoming: SAP Solutions for PLM Info Days 2026 (Virtual Event)

2026-02-13T10:50:13.129000+01:00

Let’s Spread the Word: SAP Solutions for PLM Info Days 2026

From February 24–26, 2026, SAP will host the SAP Solutions for PLM Info Days as a three-day virtual event. The event will showcase the latest innovations and solution highlights across the SAP PLM portfolio, with a strong focus on collaboration, efficiency, and digital transformation.

Agenda overview:

Day 1 – Feb 24, 2026: PLM solutions for discrete industries
Day 2 – Feb 25, 2026: PLM solutions for process industries
Day 3 – Feb 26, 2026: Project & Portfolio Management

Participants will also have the opportunity to connect directly with SAP experts and gain insights into current developments and best practices.

The virtual format also allows for flexible participation — you can join for all three days or attend selected sessions that best fit your interests and schedule.

If you are interested, you can register for the event here.

This is a valuable opportunity to expand your knowledge and engage with SAP experts. We look forward to your participation!

New Certification Available: SAP Certified Associate – SAP S/4HANA Cloud Private Edition, Production

2026-02-13T10:50:50.099000+01:00

New Certification Available: SAP Certified Associate – SAP S/4HANA Cloud Private Edition, Production

We are excited to announce that the new certification SAP Certified Associate – SAP S/4HANA Cloud Private Edition, Production Planning and Manufacturing is now live.

This certification is designed for consultants working in the SAP S/4HANA Manufacturing environment and validates core skills required to actively contribute to implementation projects. It applies to both SAP S/4HANA Cloud Private Edition and SAP S/4HANA on-premise solutions.

What This Certification Confirms

The certification verifies that candidates have a solid understanding of:

Manufacturing processes in SAP S/4HANA
Production Planning
Production execution and shop floor processes
Material components and master data
Capacity planning
Advanced Planning with PP/DS

It demonstrates both business process knowledge and technical capabilities relevant for real-world project environments.

Recommended Learning Journey

To prepare for the certification, SAP offers the learning journey:

Implementing Manufacturing in SAP S/4HANA Cloud Private Edition

This comprehensive program covers:

Core manufacturing business processes
Production Planning fundamentals
Advanced Planning with PP/DS
Shopfloor Control for discrete and process industries
From master data setup to production order execution
Capacity planning and optimization

The learning journey provides structured guidance and hands-on knowledge to ensure participants are fully prepared for the certification exam.

Staying Certified

SAP certifications are valid for 12 months.
To keep your certification active, you can extend it by successfully completing the required assessment before expiration. SAP provides personalized notifications to help you stay up to date.

Continuous learning and regular certification updates ensure that your skills remain aligned with the latest SAP innovations.

Get Started

You can find detailed information about the certification here:

Public Cloud Release 2602 Enhancement: Goods Movement Posting from Manage Reservation Items App

2026-02-16T07:14:26.726000+01:00

S/4 HANA Public Cloud Release 2602 Enhancement: Direct Goods Movement Posting from Manage Reservation Items App

Introduction

In warehouse and production processes, material movements such as Goods Receipt (101), Goods Issue (201, 261) and Transfer Posting (311) are commonly executed against reservations in SAP S/4HANA Public Cloud.

After the 2602 upgrade in my public cloud system, I explored the enhancement made to the Manage Reservation Items app. The update allows goods movements to be posted directly against reservations from the same app, without navigating to a separate goods movement application.

Scenario:

To understand the behavior in detail, I tested the functionality using movement type 311. I created a manual reservation for intra-plant stock transfer and processed it directly through the Manage Reservation Items app to verify how the system handles the posting.

Example:

Plant	From Storage Location	To Storage Location
1000	RM Store	Production Store

Before Release 2602 – My Observation

Before the 2602 upgrade, when I created a reservation (either manually or through MRP), the goods movement could not be posted directly from the reservation screen.

After creating the reservation, I had to navigate to the separate Goods Movement app, manually enter movement type 311, reference the reservation, and then post the document.

From a user perspective, this involved switching between apps and entering data again, which increased the chances of manual errors. For warehouse users handling multiple reservations, this process was slightly time-consuming.

What I Observed After Release 2602

After the 2602 upgrade, I tested the same scenario again. I noticed that the Manage Reservation Items app now allows direct posting of goods movements against the reservation itself. I was able to select the reservation and create the goods movement without navigating to another app. The system automatically generated the material document once the posting was completed.

This change reduces navigation steps and makes the process more convenient for warehouse users.

Step 1:

Create a manual reservation using movement type 311 in Manage Reservation Items app.

Below screen shot of Process Flow

Step 2:

Select the created reservation (001), choose "Create Goods Movement", and post the document.

Benefits:

Multiple reservation items can be processed in a single posting. Partial quantity posting is supported.

Below is the material document generated through the "Manage Reservation Items" app for your reference.

Conclusion:

The enhancement in Release 2602 enables warehouse users to post goods movements (Goods Issue and Goods Receipt) directly from the Manage Reservation Items app against manual and MRP-generated reservations. This reduces manual effort, eliminates app switching, and improves operational efficiency.

What's New in SAP S/4HANA 2025 - Advance Available to Promise (aATP) Business Uses and Configuration

2026-02-22T03:30:29.617000+01:00

1. Product Availability Check (PAC) Enhancements in SAP S/4HANA 2025

1.1 Flexible Reservation & Locking Strategies

PAC now supports three locking approaches:

What’s new: PAC now supports three locking approaches:

Optimistic reservation

Temporary confirmation without immediate DB lock
Lock applied only during final commit
Improves performance for high-volume order entry

Exclusive locking

Locks supply immediately during check
Prevents parallel confirmations consuming same stock
Best for scarce or regulated inventory

Automatic selection

System dynamically chooses locking logic

Business scenario example:

E-commerce bulk orders → Optimistic locking improves response time
Aerospace spare parts → Exclusive locking ensures accuracy

Configuration steps:

SPRO → Sales and Distribution → Advanced ATP → Product Availability Check → Configure Reservation / Locking

Fiori apps applicable:

Configure Product Availability Check
Check Product Availability
Review Availability Check Result

1.2 Enhanced Scope of Check Logic

Bucket Logic Options

Progressive → confirms earliest feasible supply (may split dates)
Conservative → confirms only if full quantity available

Check Without Batch

ATP ignores batch segmentation during check.

When it’s useful:

Batch assigned later by warehouse
Quality inspection determines batch
Batch irrelevant at order stage

Subcontracting Supply/Demand Handling

PAC now better accounts for:

Components issued to subcontractor
Expected finished-goods receipt

Configuration steps:

SPRO → SD → Advanced ATP → Product Availability Check → Define Scope of Check

Fiori apps applicable:

Configure Product Availability Check
Check Product Availability

1.3 Horizon for Late Supply

What it does: If supply arrives later than expected:

Confirmation can shift automatically
Within configured tolerance window

Prevents unnecessary:

Cancellation
Rescheduling
Customer communication noise

Example:

Production delay = 2 days
Tolerance horizon = 3 days
Confirmation auto-adjusted

Configuration steps

SPRO → SD → Advanced ATP → Product Availability Check → Maintain Check Horizon

1.4 Automatic Quantity Rounding

New behavior in SAP S/4HANA 2025:

Confirmed quantities validated against:

Delivery unit
Packaging rules
Rounding profile

Example:

Order: 97 units
Delivery unit: 10
Confirmation: 90 units

Configuration steps:

SPRO → SD → Advanced ATP → Product Availability Check → Maintain Rounding Rules

2. Backorder Processing (BOP) Enhancements in SAP S/4HANA 2025

2.1 Availability Change Log (ACL)

What’s new - System logs every event affecting availability:

Goods receipts
Production completion
Purchase order updates
Allocation changes
Supply protection updates

Result - BOP now:

Reprocesses only impacted orders
Runs faster
Supports near-real-time prioritization

Configuration steps:

SPRO → SD → Advanced ATP → Backorder Processing → Activate Availability Change Log

Fiori apps applicable:

Backorder Processing
Backorder Processing Monitor

2.2 Event-Driven Availability Updates (RAP Integration)

aATP now aligns with modern ABAP RESTful event handling:

Cleaner extensibility model
Supports automation workflows
Enables responsive ATP recalculation

Useful for:

Custom logic
Cloud extensions
API-driven processes

2.3 Interactive Backorder Processing Improvements

Major capabilities - Business can now:

Drag-and-drop reorder priorities
Upload worklists
Override allocation/supply protection temporarily
Fix confirmation date or quantity
Process orders by segment

Business value - Planner can:

Instantly prioritize VIP customer
Reclaim supply from low-priority order
Confirm urgent shipment in seconds

Configuration Steps:

SPRO → SD → Advanced ATP → Backorder Processing → Define BOP Segments & Strategies

Fiori Apps applicable:

Manage Confirmations for Backorders

3. Alternative-Based Confirmation (ABC) Enhancements in SAP S/4HANA 2025

3.1 Storage Location Constraint

ABC strategy can now enforce:

Confirmation must consider original storage location or
Restrict substitution to allowed locations

Useful for:

Regulated goods
Ownership-based stock
Quality-specific inventory

3.2 Storage Location Constraint New Rating Attributes

Maximum Sequence Number: Limits number of alternative sources checked.

Minimum Consumption Priority: Ensures preferred sources used first.

Benefit - Improves:

Performance
Sourcing efficiency
Predictability

3.3 Maximum Partial Deliveries Control

System respects limit on: number of partial shipments

Avoids:

Excessive delivery fragmentation
Logistics complexity

3.4 Enhanced Logging & BAdI

Logging: Improved traceability in SLG1.

BAdI: BADI_ATP_ABC_ENRCD_SUBSTN_RSLT

Allows:

Dynamic substitution logic
Attribute modification

Configuration Steps:

SPRO → SD → Advanced ATP → Alternative-Based Confirmation → Maintain Strategies

Fiori apps applicable:

Configure Alternative-Based Confirmation
Review Availability Check Result

4. Third-Party Order Processing Integration in SAP S/4HANA 2025

4.1 Availability-Controlled Purchasing

ABC can now dynamically do the following:

If internal supply insufficient → evaluate vendor availability → confirm via third-party sourcing.

Supported scenarios

Vendor → Customer (drop-ship)
Vendor → Internal plant → Customer

4.2 Backorder Follow-Up Strategies

Shortage quantity can automatically and reduces manual interventions:

Trigger supplier sourcing
Convert into third-party fulfillment

4.3 Unified Confirmation Visibility

Third-party confirmations appear in: Review Availability Check Result

Business sees:

Internal supply result
Supplier confirmation
Substitution logic

4.4 Business Process Scheduling Integration

Custom scheduling schemas allow:

Supplier-specific lead time calculation
Dynamic ETA determination

Configuration steps:

SPRO → Cross-Application Components → Business Process Scheduling

Fiori apps applicable:

Review Availability Check Result
Display Sales Order Fulfillment Issues

5. Automatic STO Creation (Plant Consolidation) in SAP S/4HANA 2025

5.1 How it works in SAP S/4HANA 2025

If: Supplying plant ≠ Customer-facing plant

ABC can:

Create Stock Transport Order automatically
Move stock internally
Confirm delivery from customer plant

Business impact

Without consolidation:

Multiple plants ship separately

With consolidation:

Single shipment
Reduced freight cost
Simplified delivery process

Configuration steps:

SPRO → SD → Advanced ATP → Alternative-Based Confirmation → Configure Plant Substitution / STO Creation

Fiori apps applicable:

Review Availability Check Result
Manage Stock Transfer Orders
Manage Sales Orders

6. Product Allocation (PAL) in SAP S/4HANA 2025

6.1 Flexible Allocation Periods

Previously

Rigid weekly/monthly buckets.

Now

You can:

Create any duration period
Auto-generate period sequences
Upload/download planning data
Migrate existing objects

Business Example:

5-day launch period
12-day seasonal period

Configuration Steps:

SPRO → SD → Advanced ATP → Product Allocation → Maintain Allocation Objects & Period Types

Fiori apps applicable:

Manage Product Allocation Planning Data
Monitor Product Allocation

6.2 CDS Extraction Views for Analytics

Planning Data available in:

I_PRODALLOCOBJTIMESERIESDEX

Consumption Data available in:

I_PRODALLOCQTYASSIGNMENTDEX

Enables below view:

SAC dashboards
Predictive analysis
Custom reporting

7. Supply Protection (SUP) in SAP S/4HANA 2025

7.1 Resolve Overprotection App

Problem addressed: Protection rules can unintentionally block all confirmations, resulting in no quantities being confirmed for sales or delivery requests.

The new app helps you proactively identify issues by detecting:
• Protection objects that are fully blocked and preventing confirmations
• Conflicting or overlapping protection rules causing unintended restrictions
• Situations that could lead to zero confirmations, impacting order fulfillment and customer commitments

7.2 Automated Mitigation Suggestions

Priority-based protection reduction: The system evaluates protection hierarchy and rule priority in aATP and proposes decreasing protected quantities at selected priority levels to resolve confirmation shortages while maintaining higher-priority allocations.

Lowest-segment protection optimization: Identifies protection segments (customer/channel/region) with surplus reserved stock and recommends reducing protection in the lowest-priority segment to release supply with minimal impact on strategic demand.

Restricted-quantity recalibration: Analyzes protection object limits and confirmation logic, suggesting adjustments to restricted quantities or release thresholds so that available stock can participate in ATP checks and avoid zero-confirmation outcomes.

7.3 Situation Management Alerts

Automatic planner notification: When aATP detects a supply–demand imbalance (for example, protection-rule conflicts, shortages, or zero-confirmation risk), a Situation Management alert is generated and automatically assigned to the responsible planner via the situation template and recipient determination.

Event-driven alert creation: The system monitors predefined ATP-relevant conditions and thresholds; once violated, it creates a situation instance with contextual data (product, location, order, confirmation status) for immediate analysis and action.

Workflow-triggered response: The situation can automatically trigger follow-up workflows—such as task creation, email/Fiori notification, or escalation—enabling planners to review protection settings, adjust allocations, or re-run confirmation checks without manual monitoring.

Configuration Steps:

SPRO → SD → Advanced ATP → Supply Protection → Maintain Protection Objects & Groups

Fiori apps applicable:

Resolve Overprotection
Monitor Supply Protection

8. Supply-Creation Based Confirmation in SAP S/4HANA 2025

8.1 Length-Based ATP

Functionality

System can confirm orders using longer available material, planning cutting/splitting to meet requested length.
Tolerance rules allow acceptable over-length or deviation during confirmation.
Supports automatic feasible confirmation instead of rejection when exact length stock is unavailable.
Helps optimize material utilization, reduce scrap, and minimize manual planner intervention.

Delivered Technical Objects

Planning Procedure: 7 – activates length-oriented planning logic in PP/DS.
Heuristic: SAP_LEN_001N – standard heuristic for:

Length-based planning
Cut optimization scenarios
Tolerance-based confirmations
Allocation of variable-dimension stock

Configuration steps

SPRO → Production Planning → PP/DS → Define Planning Procedures & Heuristics

8.2 Characteristics-Based Availability Check

Integration Capabilities

ATP triggers PP/DS-based evaluation to validate:

Configuration characteristics – ensures selected variant or options are manufacturable.
Production feasibility – checks capacity, resources, and material availability in detailed scheduling.
Optional supply creation – system can automatically generate planned orders or procurement proposals if supply is missing.

Delivered Technical Objects

Planning Procedure: 6 – enables PP/DS-supported supply check during ATP confirmation.
Heuristic: SAP_PP_SBC_R – used for:

Supply-based confirmation logic
Automatic planned order creation
Feasibility-oriented production scheduling

Fiori apps applicable:

Check Product Availability
Schedule Production
Manage Planned Orders

Conclusion:

I would say, SAP S/4HANA 2025 aATP release delivers meaningful technical enhancements that improve the robustness and intelligence of the order confirmation process across planning, allocation, and execution layers:

Automation → Automated STO creation and refined sourcing logic improve supply selection, reduce manual intervention, and ensure consistent confirmation behavior during ATP checks.
Control → Enhancements to Interactive BOP and overprotection resolution provide finer control over confirmation redistribution, enabling planners to rebalance supply based on priority, rules, and real-time availability.
Flexibility → Extended allocation-period handling and enriched substitution attributes allow more granular product/location substitution and time-phased protection management aligned with business constraints.
Manufacturing integration → PP-based ATP improvements strengthen the linkage between production supply (planned/process orders, receipts, capacity-relevant dates) and confirmation logic, resulting in more accurate commit dates.

From a system and process perspective, these capabilities help organizations with complex, multi-node fulfillment networks achieve:

More deterministic and reliable confirmations through improved rule evaluation and supply matching
Increased real-time supply transparency across allocations, protection objects, and production receipts
Higher planning and logistics efficiency via reduced manual BOP runs and exception handling
Improved service levels and customer promise accuracy driven by tighter integration between ATP, allocation, and manufacturing data

Overall, the 2025 release continues the evolution of aATP toward a proactive, exception-driven confirmation framework, enabling supply chain teams to move from reactive troubleshooting to controlled, automated order promising.

If you want to configuration steps, you will get the detail step by step configuration of SAP S/4HANA 2025 aATP on SAP help - https://help.sap.com/docs/SAP_S4HANA_ON-PREMISE/f132c385e0234fe68ae9ff35b2da178c/a004ec57a7b5bc12e10000000a4450e5.html

Also you can read summary of changes in SAP S/4HANA 2025 aATP written by Sujeet Acharya (SAP Product Lead) - https://community.sap.com/t5/enterprise-resource-planning-blog-posts-by-sap/release-highlights-of-advanced-available-to-promise-aatp-in-sap-cloud-erp/ba-p/14240423

Automated Invoice and Credit Memo Processing Using ERS in SAP S/4HANA Public Cloud

2026-02-26T08:37:53.774000+01:00

Automated Invoice and Credit Memo Processing Using ERS in SAP S/4HANA Public Cloud

Introduction

In a standard procurement process, materials are procured from a supplier through purchase order creation, goods receipt posting, invoice processing, and vendor payment. When Evaluated Receipt Settlement (ERS) is activated, the system automatically creates the supplier invoice based on the goods receipt, eliminating the need for manual invoice posting.

During one of the SAP S/4HANA Public Cloud implementations, a requirement came up to handle supplier return scenarios using ERS. In cases where materials were found defective after receipt and payment, the business expected the system to automatically generate a credit memo once the returned goods were posted. This blog explains the end-to-end process that was configured and tested in SAP S/4HANA Public Cloud to achieve this automation.

Step 1: Maintain Business Partner Master Data

Use the Manage Business Partner Master Data app.

Ensure the following fields are enabled for the Supplier in the Purchasing tab:

Automatic Evaluated Receipt Settlement
Evaluated Receipt Settlement
Goods Receipt-Based Invoice Verification

These settings allow the system to generate invoices and credit memos automatically without manual invoice posting.

Step 1: Image 1

Step 2: Schedule ERS Background Job

Use the app: Schedule Supplier Invoice Jobs – Advanced

Step 2: Image 1

Select the job template: Evaluated Receipt Settlement

This job automatically:

Creates Supplier Invoices for GR
Creates Credit Memos for Returns

I have created scheduled background jobs for ERS using this app.

Step 2: Image 2

Step 2: Image 3

Step 2: Image 4

Step 2: Image 5

Step 3: Create Standard Purchase Order (ERS Enabled)

Create a standard PO.

Ensure:

ERS checkbox is automatically enabled in the Invoice tab.

Step 3: Image 1

Post Goods Receipt (GRN).

Once the scheduled ERS job runs: System automatically generates the Supplier Invoice document. The below image shows the PO history reflecting the automatically created invoice document.

Step 3: Image 2

In the Schedule Supplier Invoice Jobs – Advanced app, the below image shows the scheduled job that automatically generated the supplier invoice based on the ERS configuration.

Step 3: Image 3

Step 3: Image 4

Note: In SAP S/4HANA Cloud (Public Edition), the classic GUI transaction MRRL used for Evaluated Receipt Settlement is not available. Instead, ERS processing is executed through the Schedule Supplier Invoice Jobs - Advanced app.

Step 4: Vendor Payment

Based on the automatically generated invoice:

Vendor payment is processed and cleared using standard payment process.

Return Process Using ERS

Step 5: Create Return Purchase Order

When defective material needs to be returned:

Create PO with Return Indicator
Ensure ERS is enabled in the PO

Step 5: Image 1

Step 6: Post Return Goods Receipt

Post Goods Receipt for the Return PO.

Once the scheduled ERS job runs:

System automatically generates a Credit Memo
No manual invoice posting required
Liability is adjusted automatically

Step 6: Image 1

In the Schedule Supplier Invoice Jobs – Advanced app, the below image shows the scheduled job that automatically generated the credit memo for the supplier return process based on the ERS configuration.

Step 6: Image 2

Step 6: Image 3

Conclusion

In my experience, ERS in SAP S/4HANA Public Cloud works smoothly not only for regular procurement but also for supplier return scenarios, provided the configuration is done correctly. Once the supplier master is properly maintained and the background job is scheduled, the system automatically takes care of invoice generation for goods receipts and credit memo creation for returns.

The key is to ensure that ERS is consistently enabled in both the standard and return purchase orders. When everything is aligned, the process runs without manual intervention, reducing effort for the finance team and avoiding posting errors. This makes the overall P2P cycle more controlled and efficient.

SAP Community - SAP Supply Chain Management

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The Future of Supply Chain Management | Part 6 of 6

Resolve Overprotection

Resolve Overprotection

What is Overprotection?

Other critical Situations

Identify Overprotection and other critical Situations

Alerting for Overprotection

App Resolve Overprotection

Mitigate Overprotection

“Overprotection” not Addressed by the App

Summary

Video: Resolve Overprotection

Links

Release Navigator for Supply Chain Management - What's New in 2602

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Why automate testing in SAP Digital Manufacturing?

🧰 What’s in your toolkit? (You already own it!)

From manual to automated: How it works

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Further resources

What's next?

Characteristics-Based Planning: Shelf Life Meets Compliance

The Problem: When Systems Can't See What Matters

What's Changed:

Accelerate Your Role in SAP Supply Chain Transformation: Join Cloud Acceleration Partner Sessions

Welcome to our Supply Chain Management Learning Group!

WELCOME!

Upcoming: SAP Solutions for PLM Info Days 2026 (Virtual Event)

Let’s Spread the Word: SAP Solutions for PLM Info Days 2026

New Certification Available: SAP Certified Associate – SAP S/4HANA Cloud Private Edition, Production

New Certification Available: SAP Certified Associate – SAP S/4HANA Cloud Private Edition, Production

What This Certification Confirms

Recommended Learning Journey

Staying Certified

Get Started

Public Cloud Release 2602 Enhancement: Goods Movement Posting from Manage Reservation Items App

What's New in SAP S/4HANA 2025 - Advance Available to Promise (aATP) Business Uses and Configuration

1. Product Availability Check (PAC) Enhancements in SAP S/4HANA 2025

1.1 Flexible Reservation & Locking Strategies

Business scenario example:

Configuration steps:

Fiori apps applicable:

1.2 Enhanced Scope of Check Logic

1.3 Horizon for Late Supply

1.4 Automatic Quantity Rounding

2. Backorder Processing (BOP) Enhancements in SAP S/4HANA 2025

2.1 Availability Change Log (ACL)

2.2 Event-Driven Availability Updates (RAP Integration)

2.3 Interactive Backorder Processing Improvements

3. Alternative-Based Confirmation (ABC) Enhancements in SAP S/4HANA 2025

3.1 Storage Location Constraint

3.2 Storage Location Constraint New Rating Attributes

3.3 Maximum Partial Deliveries Control

3.4 Enhanced Logging & BAdI

4. Third-Party Order Processing Integration in SAP S/4HANA 2025

4.1 Availability-Controlled Purchasing

4.2 Backorder Follow-Up Strategies

4.3 Unified Confirmation Visibility

4.4 Business Process Scheduling Integration

5. Automatic STO Creation (Plant Consolidation) in SAP S/4HANA 2025

5.1 How it works in SAP S/4HANA 2025