Advanced Course on Supply Planning with SAP IBP

Explore a simple two-warehouse, two-customer SAP IBP use case, building product, warehouses, and customers, defining customer sourcing ratios, and validating allocations with the SNP operator.

Explore dynamically shifting the customer sourcing ratio week by week using the transportation sourcing indicator in the customer source master data, with explicit weekly warehouse allocations and heuristic validation.

Add two plants in the location master, map them to products and warehouses with transport ratios, and run heuristics to show supply without production or stock.

Add production elements to the SAP IBP use case by configuring the production source header for two plants and defining output coefficient and production ratio, then verify receipts against demand.

Add a co-packer to a plant in SAP IBP, set a 60/40 production split, configure location data and transport ratios, and verify demand coverage via reports.

Analyze use case six by building the bill of materials, mapping components to the finished product, and running heuristics to balance plant capacity and production resources.

Explore use case seven, detailing how a subcomponent is modeled for component one with five units required. Configure master data, locations, sourcing, and production sources to reflect assembly and reports.

Oversee manual adjustments in a supply plan by overriding transportation or production volumes, and assess resulting stockouts and shortfalls across warehouse and plants to inform inventory decisions.

Show how minimum and maximum lot sizes and rounding values, including multiples of 500, shape shipment quantities, stock projections, and lead-time driven pre-build in SAP IBP.

Demonstrates receipts balancing in SAP IBP by reallocating from two warehouses to meet a customer's full demand, using the receipts balancing policy.

Explore location sources of supply in SAP IBP, showing how receipts balancing with lot-size constraints and quotas reshapes allocations to meet 100 units, considering lot size parameters.

Demonstrates production sources of supply by balancing receipts across three plants: adjust plant one to 60 units and allocate 20 units each from plants two and three, eliminating warehouse stock.

Explore use case four external sources of supply, balancing receipts from three plants and a supplier under 25% quotas and minimum lot sizes, demonstrating how received balancing prevents warehouse overstock.

Explore how IBP optimization weighs constraints and costs to generate feasible, cost-efficient supply plans, delivering optimized replenishment, production, distribution, and inventory recommendations.

Explores a use case of supply planning optimization in SAP IBP, showing how the optimizer from two plants meets a 1000-unit customer demand with zero transportation costs to maximize profit.

Use case 2 demonstrates how a transportation cost of 100 causes the optimizer to refrain from shipping from the warehouse to the customer.

Apply a non-delivery cost penalty higher than transport costs from plants to the warehouse. Observe the optimizer deliver to the customer with shared supply from both plants.

Explore how the optimizer balances transportation costs and finite plant capacities in SAP IBP use case 4, selecting production from the cheaper plant when capacity allows.

Illustrates how high plant cost and limited capacity limit production to 200 units, halting shipments. Only 200 of the 1000 unit demand is satisfied.

Increase plant capacity to ten, run the optimizer, and meet customer demand by routing production to plant two to minimize transport costs from client one to the warehouse.

The optimizer prioritizes transportation cost over production cost, despite equal capacity, causing all production to shift to the lower-transport plant when non-delivery penalties are active for a single week.

Explore how equalizing transportation costs between plants leads SAP IBP to favor the plant with lower production cost, shifting entire production from plant two to plant one.

Set a maximum late delivery of three weeks and watch the optimizer schedule shipments within that window, adjusting penalties and removing costs to influence week-by-week timing.

Weekly transportation costs drive the timing of production and shipments; with equal costs, demand is met in week one, but lower costs later shift production to week three.

The lecture shows how production and transportation costs influence weekly planning, causing preponed production in weeks with no production cost and shipments when transport costs are low to meet demand.

Evaluate optimization of shipment and production timing across weeks by comparing production costs between two plants and transportation costs, showing plant one chosen over plant two and week three shipment.