TRACKS 1,200+ POs IN REAL TIME PROCUREMENT SQL POWER BI

Purchase Order Analytics Hub

SQL extraction + Power BI model turning raw ERP PO data into a unified hub showing spend analytics, PO cycle times, approval bottlenecks, supplier concentration, invoice variance, and a real time exceptions register for overdue, over budget, and unapproved orders.

Project Overview

⚑ Problem

PO Data Trapped in ERP

Purchase order data exists in the ERP but surfaces only as raw transaction exports 40 column flat files that require hours of pivot table work to generate a spend report. No visibility into cycle times, approval bottlenecks, supplier concentration risk, or POs past due date until Finance raises a query at month end.

⚙ Solution

SQL + Power BI Analytics Hub

A scheduled SQL query extracts, joins, and aggregates PO data into a star schema (PO fact + supplier/category/buyer dimensions). Power BI layers on top with a spend treemap, cycle time analysis, approval bottleneck waterfall, invoice variance flags, and a live exceptions register refreshed daily before the 8am procurement stand up.

✓ Findings

€340K Invoice Variance Detected

62 POs flagged with anomalies across 1,240 tracked. €340K in invoice to PO variance identified 68% attributable to a single supplier category (Raw Materials). Average PO cycle time 14.2 days vs a 10 day target; approval bottleneck concentrated in two buyers. 3 way match automation reduced invoice exceptions by 41%.

Live Exceptions Register

Spend Treemap

PO Spend by Category Proportional View (€K)

Dataset Explorer

Spend & Cycle Time Analysis

PO Spend by Category (€K) Actuals vs Budget

PO Cycle Time by Buyer (Days)

Invoice Variance PO Value vs Invoice Value (€K)

PO Status Distribution

Monthly PO Volume & Spend Trend

Supplier Concentration Top 10 by Spend (€K)

PO Register

PO ID	SUPPLIER	CATEGORY	BUYER	PO DATE	REQ DATE	RCPT DATE	PO VALUE (€)	INV VALUE (€)	VARIANCE (€)	VAR %	STATUS	APPR DAYS	CYCLE DAYS	EXCEPTION

6 Month Spend Forecast

Monthly PO Spend vs Procurement Budget 6 Month Forward View

Company Advice

💡 Recommendations for Procurement Operations Teams

3 way match automation (PO → Goods Receipt → Invoice) reduces invoice processing cost by 60 80% and virtually eliminates payment disputes. Without it, the AP team manually reconciles mismatches that a simple SQL join would catch automatically.
The PO cycle time chart will show you where approvals bottleneck. Every day in approval is a day of supplier lead time consumed a buyer with a 6 day average approval time is effectively making the supply chain 6 days shorter for their category.
Supplier concentration risk is invisible until it isn't. If your top 3 suppliers account for more than 50% of category spend, you have leverage concentration but also single source fragility. Model a dual source scenario before an event forces you to.
Invoice variance above ±3% at PO level is a red flag for either specification drift (the goods delivered don't match the PO), pricing errors, or fraud. Flag these for mandatory review at any variance above €1,000.
POs raised after delivery (retrospective POs) are the clearest indicator of a maverick spend problem. The analytics hub makes them visible; the fix is contract compliance training and a PO first purchasing policy enforced in the ERP.
Run a monthly PO ageing report: any open PO older than 90 days with no receipt should be reviewed for cancellation or re negotiation. Aged open POs inflate committed spend figures and distort budget visibility for Finance.

Key Takeaway: The PO is the first control point in the procure to pay cycle. Every downstream problem wrong invoice, missed delivery, budget overrun is traceable to a failure at PO creation or approval. Getting the data right at source, and surfacing it daily, is the foundation of procurement governance.