Automotive & Mobility
As of 2026-10-10
Automotive and mobility analytics on SAP is a bill-of-materials problem before it is a BI problem: a premium vehicle carries 30,000+ part numbers, and a query that explodes that BOM live will time out — the fix is a nightly-materialised BOM layer in Datasphere, the exact pattern that took one Tier-1 supplier's warranty dashboard from 45-90 seconds to under 3. The module also covers the shift most SAP shops are least ready for: connected-vehicle subscriptions recognised under IFRS 15 through SAP RAR, tracked by VIN rather than by sales order. A consultant who can join BOM, quality, and RAR data on a validated VIN — and who keeps warranty and recall cost apart — sits at the top of the industrial-analytics rate band; one who can't spends the engagement rebuilding trust in numbers finance has already stopped believing.
What you will learn
- Work through a realistic scenario: A German Tier-1 automotive supplier (€4B revenue, 800 active part numbers) needs a warranty-cost analytics platform its category managers can query daily.
- Recognize and avoid the anti-pattern: Exploding the BOM inside a live analytic query — Query cost grows exponentially with BOM depth.
- Apply the module's core decision: Where to run BOM explosion — choose Pre-materialise the exploded BOM nightly in a Datasphere staging layer and join analytics queries to that layer only.
- Track mastery with the KPI: Warranty dashboard query latency.
Module overview
Automotive and mobility analytics on SAP is shaped by two structural facts that distinguish the industry from all others: the complexity of the bill of materials (a premium vehicle has 30,000+ unique part numbers, each with a multi-level production BOM and a service BOM) and the shift from one-time product sales to recurring connected-vehicle service revenue. Both require SAP analytics capabilities that go well beyond a standard BI deployment.
The BOM-driven analytics challenge. Vehicle programme analytics — which platform variants are meeting cost targets, which part families are generating warranty claims — requires exploding multi-level BOMs (transactions CS11 and CS12 in SAP MM/PP) against actual production orders and quality notifications. The performance killer here is the BOM explosion at query time: a naive CDS view that calls SAP's delivered BOM function module for every vehicle in the query will timeout on a run of even modest size. The correct approach is to pre-materialise the exploded BOM levels in a staging table (updated nightly from production) and join that to the analytical query. SAP Datasphere's Data Flow and Intelligent Lookup objects are well-suited to building and maintaining this materialised layer.
Prerequisites
- Intermediate hands-on experience on SAP analytics projects
- Review core concepts first: C087, C083, C046
Outcomes
- Work through a realistic scenario: A German Tier-1 automotive supplier (€4B revenue, 800 active part numbers) needs a warranty-cost analytics platform its category managers can query daily.
- Recognize and avoid the anti-pattern: Exploding the BOM inside a live analytic query — Query cost grows exponentially with BOM depth.
- Apply the module's core decision: Where to run BOM explosion — choose Pre-materialise the exploded BOM nightly in a Datasphere staging layer and join analytics queries to that layer only.
- Track mastery with the KPI: Warranty dashboard query latency.
Full module available to members. The full module adds: the decision framework · the end-to-end scenario walkthrough · the KPI scorecard · the anti-patterns · the code blocks · the knowledge check · the diagrams.