Introducing Demand-Driven Buffer Level Management (1Y2)

1. Navigate to SAP Best Practices for SAP S/4HANA Cloud, public edition for Enterprise Management.
2. Select a country localization from the Version dropdown list.
3. If necessary, select the Login icon in the upper left corner to log in.
4. Go to the Accelerators section and, under General Documents, select the link SAP Best Practices content library.
5. Select the relevant scope option checkboxes to filter information and locate scope item test scripts and process flows.

Note

Ignore the BPMN2 version of process flows.

• Demand-driven replenishment (DD) helps to plan and manage supply chains efficiently based on customer demand, rather than through traditional MRP procedures. It forms the basis for a reliable material flow by defining buffers at strategically important points and adjusting these buffers regularly.
• Products at all Bill-of-Material (BOM) levels are classified based on average demand, lead time, BOM usage, and variability to identify whether they are relevant for demand-driven replenishment. A well-balanced buffer level is proposed for DD-relevant products to ensure that they are sufficiently stocked to meet average demand, but in low enough quantities to prevent excessive storage costs or losses due to expiry. An app helps planners to manage safety stock, reorder point, and maximum stock through the buffer level proposals. To support the calculation of buffer levels, predictive Material and Resource Planning (pMRP) can be deployed.

Buffer Positioning:

• Classify products based on their goods issue value, usage across BOMs, and variation in actual demand to help identify whether they are relevant for Demand-Driven Replenishment
• Display and change master data records relevant to Demand-Driven Replenishment for several products simultaneously
• Optionally, use different scenarios, such as inbound protection, to position buffers efficiently within your supply chain

Buffer Sizing:

• Classify products relevant to Demand-Driven Replenishment based on their decoupled lead time, and help define inputs for their buffer settings
• Review and maintain buffer profiles for variability and lead-time factors depending on product classification
• Generate buffer (stock) level proposals for products based on their average daily usage, decoupled lead time, buffer profiles, and several other factors
• Manage safety stock, reorder point, and maximum stock using buffer level proposals
• Use predictive Material and Resource Planning (pMRP) for average daily use calculations (optional)
• Optionally, use machine learning capabilities for the prediction of lead times

• Avoid the bull-whip effect in production by providing decoupling points with strategic stock positions
• Enable immediate production of individualized products using adaptive component buffers
• Ensure highest service levels at lowest possible inventory along all BOM levels
• Improve material requirements planning based on high-quality data that is updated regularly and is used as input, such as for stock levels
• Use predictive Material and Resource Planning (pMRP) or Machine Learning (ML) for reliable input to buffer level calculations (optional)
• Support users in identifying issues with current stock levels and in prioritizing the issues as a starting point for taking the right action