$20M+ P&L Impact Unlocked Through Procurement Value Visibility

How AB InBev, the world's largest brewer, surfaced over $20M in procurement and payables value across a global operation that already believed its controls were strong.

Executive Snapshot

Manufacturing Operating Context

AB InBev is the world's largest brewer, with Budweiser, Corona, and Stella Artois sold across 150+ markets and $11.6B+ in procurement spend spanning ingredients, packaging, logistics, and indirect spend.

High volume of:

• Direct materials — malt, hops, packaging, and raw ingredients
• Logistics and distribution across global supply lanes
• Indirect and MRO spend across breweries and facilities
• Multi-currency vendor invoicing across markets

Complex environment combining:

• Thousands of vendors, many transacting in several currencies
• A single vendor paid through multiple accounts and entities
• Automated payment controls tuned for exact-match exceptions
• Decentralized procurement across regional operating units

Challenge: Where Procurement Value Became Invisible

This was not an organization with weak controls. AB InBev runs mature procurement systems, automated payment controls, and disciplined finance teams across every market. The problem was structural: automated controls are built to catch exact matches and known exceptions — not the near-misses that look legitimate on every individual line. Even within a limited dataset of 8 vendors, these inconsistencies translated into measurable financial impact.

At this scale and across this many currencies, the leakage hid in transactions that each looked valid in isolation. The same invoice booked twice under slightly different references. A credit issued by a vendor but never routed to the team that books it. A volume discount earned under a contract clause no one revisited. An invoice booked and paid in a stronger currency than the one it was issued in.

Each control worked correctly in isolation. What was missing was the ability to reconcile, across the full population and every currency, what was agreed and owed against what was actually paid.

Value Scenarios Unlocked

Once vendor, payment, and contract data were analyzed together across the full population — not a sample — and across every currency, recurring value patterns emerged. Four representative scenarios show how the value was found.

Primary Scenario: Duplicate & Wrong-Vendor Payments

The same invoice paid more than once — booked twice under transposed numbers, split across two vendor accounts, or charged to the wrong vendor entirely. Because the entries aren't exact matches, automated duplicate-detection reads them as distinct and lets them through.

How it leaks:

• Same invoice entered twice with a keyed error in the number
• Same invoice booked against two different vendor accounts
• Payment posted to the wrong vendor record altogether

Beyond this primary pattern, additional scenarios surfaced — each repeatable, each missed by conventional AP audit:

Missed Credits — $908K

Vendor credits for overpayments, pricing, and returns sitting in inboxes and on statements, never booked. Recovered through direct vendor-statement outreach.

Unclaimed Volume Discounts — $752K

Contractual volume-discount clauses earned but never billed back — buried in vendor contracts and email, never reconciled to actual purchase volume.

Currency Issues / Overpayments — $34K

Invoices booked and settled in a stronger currency than issued; credits deducted in a weaker one — driving systematic overpayment and under-deduction.

From Visibility to Impact

Discover Dollar did not start by looking for "errors." It started by restoring commercial context to retail financial execution.

How Value Was Uncovered

1. Commercial & Financial Data Ingested

Manufacturing data across purchase orders, goods receipts, advance-payment ledgers, FX records, invoices, and payments was securely connected without disrupting existing ERP, Ariba, or AP workflows.

2. Commercial Context Extracted

Natural language processing analyzed master data, agreements, and posting metadata to understand what was actually authorized — not just what processed through systems.

3. Intent Matched to Execution

Authorized commercial terms — unit costs, advance settlements, FX rates, and receipt states — were systematically compared against invoices and payments to identify where automated execution diverged from economic intent.

4. Value-Impacting Mismatches Surfaced

Mismatches with real margin or P&L impact were surfaced — prioritized by materiality, not volume — enabling AP, controlling, and procurement teams to focus on what truly mattered.

5. Visibility Embedded as a Recurring Layer

Once validated, these signals became part of an ongoing visibility layer, allowing finance and AP teams to detect and address leakage continuously, not retrospectively.

Proof of Metrics

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