Automated vendor spend dashboard using embedded analytics for procurement

We implemented an automated vendor spend dashboard in S/4HANA 1809 that transformed our procurement analytics from manual Excel-based reporting to real-time embedded analytics. The project eliminated 40+ hours of monthly manual effort and provided procurement managers with instant visibility into vendor performance and spending patterns.

Our challenge was consolidating data from purchase orders, goods receipts, invoices, and payments into meaningful vendor spend metrics. Previously, analysts exported data from multiple transactions, merged it in Excel, and distributed static reports that were outdated within days.

The solution leveraged CDS view aggregation to create a unified vendor spend data model, Fiori dashboard automation to deliver self-service analytics, and real-time vendor spend calculations that update as transactions are posted. I’ll share the technical implementation approach and business outcomes.

This sounds like exactly what we need. Can you share details about your CDS view architecture? Did you build a single comprehensive view or layer multiple views? We’re struggling with performance when trying to aggregate across purchase orders and invoices in a single query.

Did you integrate vendor master data attributes into your dashboard? We want to show spend analysis by vendor classification, payment terms, geographic region, and strategic vs tactical vendors. I’m wondering if CDS views can efficiently join procurement transaction data with vendor master data enrichment without performance issues.

I’ll walk through our complete implementation covering CDS view aggregation, Fiori dashboard automation, and real-time vendor spend calculation approach.

CDS View Aggregation Architecture: We implemented a three-layer CDS view structure rather than a single monolithic view, which was critical for performance:

Layer 1 - Basic CDS Views (Data Extraction):

  • Z_PO_HEADER_BASIC: Purchase order header data with vendor, dates, total value
  • Z_INVOICE_BASIC: Invoice documents with vendor, amounts, payment terms
  • Z_GR_BASIC: Goods receipt data with quantities and values
  • Z_PAYMENT_BASIC: Payment documents with cleared amounts and dates

Each basic view reads from a single primary table with minimal joins, ensuring fast data access.

Layer 2 - Calculation Views (Business Logic):

  • Z_VENDOR_PO_SUMMARY: Aggregates PO data by vendor, fiscal period, purchasing organization
  • Z_VENDOR_INVOICE_SUMMARY: Aggregates invoice data with payment status indicators
  • Z_VENDOR_PAYMENT_SUMMARY: Calculates payment metrics including average days to pay

These calculation views include business logic like currency conversion, fiscal period derivation, and status categorization.

Layer 3 - Consumption View (Dashboard Data Source):

  • Z_VENDOR_SPEND_CUBE: Combines all summaries with vendor master enrichment, exposed as analytical query with proper annotations (@Analytics.dataCategory: #CUBE)

This layered approach keeps each view focused and maintainable while achieving good performance through HANA optimization.

Fiori Dashboard Automation: We used a hybrid approach combining standard SAP Fiori apps with custom tiles:

Standard Apps (Configured):

  • Analyze Procurement Spend (F1726): Configured for top-level vendor spend overview
  • Display Purchase Order Items (F0117): Embedded for drill-through to PO details

Custom Tiles (Developed):

  • Vendor Spend Trends: Custom analytical list page showing spend trends by vendor with sparklines
  • Payment Performance: Custom tile displaying average payment days vs terms
  • Savings Opportunities: Tile highlighting vendors where early payment discounts aren’t being captured

The custom tiles were built as SAPUI5 applications consuming our Z_VENDOR_SPEND_CUBE view through OData services. We chose custom development for these because standard apps didn’t provide the specific KPIs and visualizations procurement managers requested.

Key Automation Features:

  • Scheduled email distribution of dashboard snapshots (weekly summary to executives)
  • Alert notifications when vendor spend exceeds thresholds or payment terms are violated
  • Automatic variance highlighting when month-over-month spend changes exceed 20%

Real-Time Vendor Spend Calculations: The real-time aspect works because CDS views query transactional tables directly rather than relying on batch extracts. Here’s how we handle common scenarios:

Timing Reconciliation:

  • Invoice before GR: Dashboard shows “Unmatched Invoice” category with separate KPI tile
  • PO changes after partial delivery: CDS view uses latest PO data with delivered quantity filters
  • Payment timing: Separate “In Process” vs “Completed” payment status based on clearing document dates

Data Quality Controls:

  • Daily reconciliation job compares CDS view aggregations against financial postings
  • Exception report highlights discrepancies for investigation
  • User documentation explains timing differences between real-time dashboard and official period-end reports

Metrics Included: Our dashboard tracks comprehensive vendor performance:

  • Total spend by vendor (current month, YTD, prior year comparison)
  • Average payment days vs contractual terms
  • Early payment discount capture rate
  • On-time delivery percentage (from goods receipt dates vs PO requested dates)
  • Invoice accuracy (first-time match rate without exceptions)
  • Spend concentration (top 10 vendors as % of total spend)
  • Maverick spend (POs without contracts)
  • Vendor risk indicators (single-source dependencies)

Manual Effort Reduction Breakdown: The 40+ hours monthly savings came from:

  • Data extraction from multiple transactions (ME2N, FBL1N, F110): 12 hours eliminated
  • Excel consolidation and reconciliation: 15 hours eliminated
  • Report formatting and distribution: 8 hours eliminated
  • Ad-hoc analysis requests (now self-service): 10 hours eliminated

Business Outcomes: Beyond time savings, we achieved:

  • 15% increase in early payment discount capture ($180K annual savings)
  • 3-day reduction in average payment cycle through better visibility
  • Improved vendor negotiations using data-driven spend analysis
  • Faster response to vendor inquiries with real-time payment status

Implementation Lessons:

  1. Vendor Master Data Integration: Yes, we extensively integrated vendor master attributes. The key is doing this in Layer 2 calculation views rather than the basic views, using left outer joins to handle vendors without complete master data. Performance has been acceptable with our 8,000 active vendors.

  2. User Adoption: The dashboard succeeded because we involved procurement managers in design from the beginning. They defined the metrics that matter, and we delivered exactly those KPIs rather than building what we thought they needed.

  3. Governance: We established clear data ownership with procurement responsible for vendor master data quality and finance responsible for transactional data accuracy. The dashboard’s data quality alerts help both teams maintain standards.

The combination of well-structured CDS view aggregation, thoughtful Fiori dashboard automation, and reliable real-time vendor spend calculations created a solution that’s now considered essential to our procurement operations.