SAP IBP Time-Series Supply Optimizer: A Pre-Build Example

Mandar Padwal · 3 July 2026 · 3 min read

How the time-series supply optimizer in SAP IBP weighs holding, late-delivery and non-delivery costs, shown with a seasonal air-conditioner example where building stock early wins.

Should a plant build stock in winter for a summer peak, or produce only when the orders come in? A rule-based heuristic cannot answer that, because it does not compare costs. The time-series supply optimizer in SAP IBP does: it builds the supply plan with the lowest total cost that still respects capacity, lead times and material availability.

Heuristic vs optimizer

A heuristic follows fixed planning rules. The optimizer treats the whole network as one cost model and chooses among production, procurement, transport and inventory options. It answers questions such as:

• Produce in plant A or plant B?

• Make in-house or buy?

• Transfer stock from another warehouse?

• Build inventory now or later?

• Which customer gets scarce stock in a shortage?

If you know SAP APO, this is the cost-based planning of the SNP optimizer, now in SAP IBP.

What the optimizer weighs

• Costs: production, procurement, transport, inventory holding, and penalties for late delivery and non-delivery

• Constraints: production and resource capacity, material availability, lead times and, where modelled, storage, shelf life and transport limits

Customer priority comes through the penalties: a higher non-delivery cost for a key customer makes the optimizer serve that customer first.

Example: an air-conditioner manufacturer

• Capacity: 2,500 units per month, all year

• Demand January to March: 500 to 800 units per month

• Demand April to June: 4,000 to 4,500 units per month

From April to June every month is short by 1,500 to 2,000 units, while January to March leave 1,700 to 2,000 units of spare capacity each month. The cost rates in the model:

• Inventory holding: $5 per unit per month

• Late delivery: $80 per unit per day

• Non-delivery: $1,500 per unit

Why pre-building wins

• Holding 5,600 pre-built units for a month: 5,600 × $5 = $28,000

• Delivering a 1,500-unit shortfall late: 1,500 × $80 = $120,000 for every day of delay

• Not delivering those 1,500 units at all: 1,500 × $1,500 = $2,250,000

Holding stock is far cheaper than either penalty, so the optimizer uses the spare winter capacity to build inventory for the peak. The result: demand fully met, capacity fully used and total cost at its lowest. Change the rates and the answer can change too; with a high holding cost or a low penalty, waiting can become the cheaper plan.

When a pre-build makes sense

Typical triggers are seasonal peaks (air conditioners before summer, soft drinks before festivals, medicines before flu season), planned maintenance shutdowns, promotions and supplier disruptions. The optimizer proposes a pre-build only when:

• spare capacity exists before the peak,

• capacity during the peak cannot meet demand,

• holding cost is lower than the late or non-delivery penalties, and

• all constraints still hold.

Key takeaways

• A heuristic follows rules; the optimizer compares costs across the whole network.

• Cost rates and penalties drive every decision, so agree them with the business before go-live.

• Customer priority is expressed through non-delivery penalties.

• In the example, $28,000 a month of holding cost avoids $120,000 a day in late-delivery penalties.

Interested in SAP supply chain planning? Browse our SAP courses to see where to begin.