Transforming supply chain planning with SAP IBP

SAP Integrated Business Planning (IBP) is a fundamental tool that enables the implementation of an agentic AI framework. In line with EY's agentic AI principles, this article positions SAP IBP not only as a technological solution, but also as a strategic enabler of collaboration between humans and AI agents.

Liability and inventory reports, scenario planning, and dashboard and data integration are key use cases. By leveraging these capabilities, companies can move beyond Excel-based models and take a forward-looking approach. At a Fortune 50 semiconductor company, this transformation promoted cross-functional collaboration and enabled proactive liability and inventory management through detailed reporting. The path shows how SAP IBP can serve as the backbone of AI-driven decision-making in complex supply chains in the future.

What are the specific use cases? The original driver of this transformation at the Fortune 50 semiconductor company was the need to standardize processes across business units and accurately identify liabilities related to finished goods and components in the event of fluctuations in demand and product cancellations. For example, when a demand fluctuation occurs, demand for one product is shifted to another product or product line.

Liability shifts between suppliers and manufacturing sites, creating financial risks if not tracked accurately. Furthermore, the supply chain spans multiple global locations and thousands of SKUs, making the management of these liabilities complex at scale. To address this, static inventory reporting reflected demand across different weekly periods to ensure transparency and alignment across planning horizons.

Liability and inventory reports

Historically, liability analysis relied on manual processes and spreadsheet-based models. Although familiar, these approaches presented significant limitations due to large and complex data sets, including data silos, human error, and a lack of real-time visibility. For large, global organizations where rapid decision-making is critical, liability management requires a more robust and scalable solution to reduce costs and create consistent, timely opportunities for risk mitigation.

The introduction of SAP IBP for reporting marked a significant turning point, as all planning data is now stored in SAP IBP. By centralizing data and automating processes such as demand fluctuations and cancellations, teams gained access to simulation-based liability reports in real time. These reports improved transparency of potential risks and enabled faster, more informed decisions.

The transition involved mapping existing Excel models to SAP IBP, designing new planning templates, and integrating them into internal data platforms. This reduced manual effort and minimized inconsistencies between internal reporting and contract manufacturers' estimates—a challenge that is widespread in discrete manufacturing.

In addition to liability, inventory reporting has also been improved by calculating demand and dollar values across multiple planning horizons (lead time, 26 weeks, and 51 weeks), while alternative components are taken into account to mitigate risk in the event of bottlenecks. Currently, only „clean alternatives“ are supported, but future versions will map „non-clean alternatives“ with partial interchangeability, enabling more sophisticated substitution logic. SAP IBP also offsets negative net requirements by redistributing excess inventory from alternatives, reducing overall liability.

Identify planning bottlenecks

This enhanced reporting enables detailed analysis, allowing planning bottlenecks to be identified early on and excess inventory to be proactively avoided. An important success factor was the consistency with the figures provided by the contract manufacturers, which eliminated the need for manual reconciliation and built trust in the solution. SAP IBP generates inventory reports that can be easily distributed.

These capabilities lay the foundation for future AI enhancements and can be extended to other areas of manufacturing. The solution enables teams to take a proactive role in risk management while consolidating reporting in a single tool with comprehensive update cycles. Finance and planning users can leverage customized reports to highlight key metrics and exceptions, while historical databases created in SAP IBP will serve as the basis for future AI-powered trend analysis.

Scenario planning

Beyond static reporting, liability reporting can be extended to cover multiple scenarios. Demand fluctuations occur when demand for one product shifts to another—often due to changing customer requirements, supply bottlenecks, or product life cycle adjustments.

Managing such scenarios is complex, as it requires analyzing common and specific components, recalculating inventories, and assessing the overall impact on liability (component surplus in the event of a decline in demand) and expenditure (component requirements due to new demand).

SAP IBP reporting as the backbone for agentic AI in supply chain planning.

Analysis at scenario ID level

Planners initiate demand fluctuations or cancellations by using user-defined master data and then performing a copy operation to generate liability reports in a special planning template. A detailed analysis can be performed at the scenario ID level or summarized in a weekly template that consolidates total liability and effort. The benefits of this new reporting were significant: processes that previously took hours are now completed in just ten minutes.

The scenario ID concept also supports multiple demand fluctuations or cancellations within a single transaction. Planners can review a summary of total expenditures and liabilities across multiple transactions at the component level of the respective bill of materials.

Scenario planning is crucial for companies in volatile markets where demand and supply conditions change rapidly. By simulating a variety of scenarios, discrete manufacturing companies can evaluate the impact of different business decisions—such as introducing a new product, discontinuing an existing one, or responding to sudden supply disruptions. This capability strengthens contingency planning and optimizes component allocation.

The SAP IBP solution automates the analysis of demand fluctuations in three key steps after the user-defined master data has been updated: it compares bills of materials, identifies common components, and calculates replacement quantities and costs; it recalculates demand and surpluses, identifies excess inventory, and estimates liabilities for common and unique parts; and finally, it summarizes weekly demand fluctuations in a single template to provide transparency on total liability and expenditure.

This automation reduces the risk of excess inventory, improves component utilization, and accelerates response to market changes. It also lays the foundation for future AI-driven optimizations, such as component standardization and the use of insights for simplified product design.

At the technical level, the scenario planning process in SAP IBP involves maintaining the scenario ID master data, executing the copy operator, transforming the core data, calculating exchange quantities at the component level, recalculating demand and supply, determining liability at the component level, analyzing liability at the scenario level, and finally reviewing the summary reports.

In future phases, risk mitigation strategies will be introduced, such as redistributing excess component inventories beyond static inventory adjustments. In addition, results exported to SharePoint will be used to support the further development of an AI-powered framework that enables predictive insights and further automation. Currently, each demand fluctuation is still processed individually, but in a future phase, all demand fluctuations and cancellations will be handled in a single run.

By automating previously manual tasks and providing clear, actionable reports, users were able to quickly adapt to the new process. Discrete manufacturing companies can use SAP IBP in a variety of ways for scenario planning, such as managing demand fluctuations for identical finished products at different locations, for different products at the same location, for different products across different locations, and for cancellations.

This flexibility makes it possible to analyze both similar and different products across multiple locations, providing a holistic view of potential impacts. Scenario planning supports the Fortune 50 semiconductor company's overall business strategy by enabling rapid, data-driven decision-making and risk mitigation.

The speed and flexibility of SAP IBP's scenario planning tools are particularly valuable in times of high uncertainty, such as global supply chain disruptions. By enabling rapid scenario analysis, the platform empowers decision-makers to act quickly, minimize negative impacts, and capitalize on new opportunities.

Dashboards and data integration

New dashboards and enhanced data integration capabilities provide actionable insights to both planners and finance teams by promoting collaboration and continuous improvement. By leveraging detailed liability and inventory reports across current and historical periods, companies can create a platform that enables the adoption of agentic AI.

SAP IBP serves as a valuable data source for future AI applications, as the system can act autonomously, make decisions, and interact with users in a dialog-based manner. By combining historical data with real-time analytics and scenario modeling, AI can drive proactive risk management and optimization. The dashboards developed in SAP IBP are highly customizable, allowing users to tailor views to their specific needs and preferences.

This flexibility ensures that operational planners can quickly identify patterns, outliers, and emerging risks. Data quality is ensured across integrated systems through thorough user testing and ongoing coordination with suppliers. For the Fortune 50 semiconductor company, future phases will include improved collaboration with suppliers and alignment of risk mitigation plans generated by SAP IBP with site-specific strategies to further improve coordination and data integrity.

Predictive analytics

SAP Integrated Business Planning is increasingly evolving from a pure planning tool to an intelligent analysis and decision-making system. The use of machine learning enables more accurate demand forecasts, earlier risk identification, and the derivation of concrete courses of action. IBP models continuously learn from new data and feedback—a key factor in improving forecast quality, relevance, and response speed.

The potential is enormous: AI helps companies anticipate disruptions, seize opportunities, and embed improvements in their processes for the long term. As the capabilities of these technologies grow, the combination of AI and IBP will enable even deeper automation and greater agility in the future.

The Fortune 50 semiconductor manufacturer demonstrates what transformative effects look like in practice. The company has expanded SAP IBP far beyond traditional planning processes—for example, to include inventory and liability reports, demand volatility analyses, and system-supported scenario planning. The platform created in this way serves as the foundation for agentic AI, which not only supports decisions but also increasingly prepares them independently.

The results: greater transparency, shorter decision-making processes, and proactive risk management. What is particularly noteworthy is that this form of reporting can be applied to any discrete manufacturing company. In light of complex and volatile global supply chains, the strategic use of data, analytics, and AI is becoming a decisive competitive advantage.

The approach described here shows that companies with clear objectives and the right tools can fundamentally modernize their supply chain planning. The next stages of development will focus on closer cooperation with suppliers, refined risk concepts, and optimized complaint processes.

The case study of the semiconductor company clearly shows that the intelligent expansion of SAP IBP solutions creates resilience and agility—and paves the way for AI-supported future scenarios. The integration of reporting logic, scenario planning, and dynamic dashboards provides near real-time insights that enable companies to make faster and more confident decisions. In the face of growing uncertainty, such frameworks are becoming key to preventing disruptions and embedding continuous improvement. This approach will become increasingly important across all industries.