Improving Procure-to-Pay Discipline Through Data Analytics

Summary

In large organizations, Procure-to-Pay (P2P) process discipline is critical for:

• Budget control
• Spend forecasting accuracy
• Cash flow optimization
• Audit and compliance

However, in practice, operational teams often prioritize speed over process — creating Purchase Requisitions (PR) and Purchase Orders (PO) after invoices are received. This seemingly small behavior creates:

• Longer invoice processing cycles
• Lost early-payment discounts (e.g., Net 10 / Net 15)
• Weaker budget visibility and accrual accuracy
• Increased compliance and audit risk

This project started as a Marketing process investigation, and later expanded into a cross-department analytics framework covering: Marketing, Engineering, IT, Facilities, HR, Tech Services and other functions.

Using SAP ERP and invoice processing data, I built a Power BI analytics platform to:

• Quantify process violations
• Identify root causes by vendor, department, and requisitioner
• Track behavior trends over time
• Support management decisions and corrective actions
• Extend from process compliance into strategic spend analysis

Background

In a well-controlled procurement environment: PR and PO should be created before goods or services are received.

This ensures:

• Proper approval workflow
• Budget reservation
• Contract and pricing validation
• Predictable cash flow and accruals

But in fast-moving business functions (Marketing, Engineering, IT, Facilities), teams often engage vendors first, then “fix the paperwork later”. This leads to:

• Invoice exceptions
• Manual AP intervention
• Delayed payment
• Lost discounts
• Reactive rather than proactive spend management

Phase 1 — Marketing Pilot: Defining the Problem

The project started in Marketing, where we observed frequent cases of: Invoice received → PR created → PO created → Payment, instead of: PR → PO → Invoice → Payment.

Key Questions

• How big is the problem (volume & dollars)?
• Who is driving it (vendors / requisitioners)?
• Is it random or systematic?
• What is the business impact?

Data Sources & Modeling

Systems

• SAP ERP:PR info, PO info, Vendor info, Cost Center info
• Invoice Processing System: Invoice date, posting date, payment status, amount

Data Engineering

• Joined PR, PO, and invoice documents
• Created derived metrics:
• PR Created Before / After Invoice
• Time Lag (Invoice Date – PR Date)

Cleaned:

• Vendor master data
• Duplicate records
• Date inconsistencies

Phase 1 Dashboard — Marketing PR vs Invoice Timing

What it Shows

• Total invoices and total spend impacted
• Monthly trend of violations
• Top vendors involved
• Top requisitioners driving the issue
• Distribution by invoice size
• Average time lag

Insights

• The issue was systematic, not occasional
• A small group of vendors and users drove most violations
• The company was:
  • Losing early payment discounts
  • Increasing AP workload
  • Reducing forecast reliability

This dashboard became the management discussion anchor.

Phase 2 — Scaling to All Departments

After success in Marketing, the framework was extended company-wide to include: Engineering, IT, Facilities, HR, Tech Services, Sales & other functions

Cross-Department Dashboard

This version adds:

• Department-level comparisons
• Treemap of process violations by function and manager
• Vendor concentration across departments
• Monthly trends by department

What It Enables

• Identify which functions have structural process gaps
• Distinguish:
  • Training issues
  • Workload issues
  • Process design issues
• Move from:
  • “Finance is complaining about process” to:
  • “Here is the quantified, ranked, visualized business impact.”

Phase 3 — Deep Dive: Engineering Department

Engineering has:

• High spend volume
• Complex vendor ecosystem
• Both PO and Non-PO spend patterns

So we built two dedicated analytical views.

3A — Engineering PR vs Invoice Compliance Dashboard

This mirrors the Marketing analysis, but focuses on:Much larger scale; More vendors; More requisitioners; Larger financial exposure.

It answers:

• How compliant is Engineering vs other functions?
• Which teams or managers are driving violations?
• Which vendors are repeatedly bypassing procurement discipline?

3B — Engineering Non-PO (Finance Invoice) Spend Analysis

This is a different but strategically critical extension of the project.

Here the goal is not process timing, but: Understanding what changed in spend structure, YoY and YTD.

This dashboard shows:

• FY23 vs FY22 spending comparison
• YTD change bridge (increase/decrease by category)
• Spend mix by: Expense category & Vendor & Cost Center
• Invoice size distribution changes
• Trend by period

Questions It Answers

• Where did Engineering spend increase or decrease?
• Which categories are driving the change?
• Are we:
  • Consolidating vendors?
  • Fragmenting spend?
  • Shifting cost structure?
• Is spend growth aligned with strategy?

This turns the platform from a compliance tool into a strategic spend intelligence system.

Business Impact

This analytics framework is now used to:

• Support BU leadership discussions
• Drive:
  • Process discipline
  • Budget ownership
  • Spend transparency
• Enable:
  • Targeted corrective actions
  • Vendor strategy review
  • Policy and workflow redesign

Concrete benefits:

• Reduced invoice cycle time
• Better early-payment discount capture
• Improved forecast accuracy
• Stronger audit posture
• Higher-quality business partnering conversations

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