Automotive & Mobility
As of 2026-08-16
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
- Understand the core concepts behind automotive & mobility
- Apply Automotive in a typical SAP analytics engagement
- Recognize the 3-5 common mistakes and how to avoid them
- Position this skill in your personal brand and rate conversation
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
- Understand the core concepts behind automotive & mobility
- Apply Automotive in a typical SAP analytics engagement
- Explain the core architecture and decision points for Automotive & Mobility
- Apply a repeatable implementation pattern in a 15-minute lab format
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.