How Aurora Supply Turned ERP Chaos into a Self‑Driving Ops Engine
Introduction
About the company
Aurora Supply is a mid‑market B2B distributor that ships technical equipment to factories and data centers across 14 countries. Their operations team manages thousands of SKUs, long lead times, and strict service‑level commitments.
Their core business model
They buy from a global network of manufacturers, hold inventory in regional hubs, and sell to enterprise customers on annual contracts. Margin depends on forecasting demand correctly and keeping fill‑rates high without overstocking.
Their target audience
Plant managers, procurement leads, and finance teams at industrial and technology firms that expect reliable deliveries, clear pricing, and accurate invoices.
Industry
The Problem
The organization had invested in an ERP system, but most critical processes still relied on offline spreadsheets, ad-hoc scripts, and manual coordination. Planners exported data to Excel to forecast. Finance teams re-entered invoice details. Sales managers kept shadow pipeline trackers. Data was duplicated, out of sync, and hard to trust.
Three pain points came up in every conversation:
- Forecasts were consistently off, creating both stockouts and dead stock across regions.
- Month-end closing was slow because exceptions, disputes, and credit notes had to be reconciled manually.
- No one had a real-time view of operations - reports were always as of last week.
The cost of manual coordination was visible across every function:
$1.2M
a year lost to expedited shipping, emergency purchase orders, and penalties for missed SLAs.
$1.2M
a year lost to expedited shipping, emergency purchase orders, and penalties for missed SLAs.
$1.2M
a year lost to expedited shipping, emergency purchase orders, and penalties for missed SLAs.
The operations lead summed it up: "The ERP has the data, but our people spend more time searching for it than acting on it."
The Solution
Avannte deployed an AI layer over the existing ERP that watches transactions, surfaces exceptions, guides actions, and reduces manual coordination across planning, finance, and supply chain. The system acts as an always-on operations copilot - triaging issues, pulling context from multiple systems, and routing structured recommendations to the right people with approval workflows and audit trails.
Key capabilities delivered: automated exception detection and routing across AP, AR, and inventory; AI-assisted demand sensing that combined ERP history with external signals; a unified operations dashboard replacing spreadsheet-based reporting; and role-based copilots for planners, finance analysts, and supply chain coordinators.
At the core of the solution is an agentic AI layer with three capabilities working together: understanding demand, protecting data quality, and closing the books faster. A Demand Intelligence agent consumes years of order history, seasonality, promotions, and open quotes to generate weekly forecasts per SKU and region, then pushes recommended adjustments straight into the planning screens. A second agent patrols new orders as they are created, checking pricing, terms, and mandatory fields in real time so bad data never enters the pipeline. A third agent continuously reconciles invoices, shipments, and payments, flagging mismatches early and assembling all related records into a single, review‑ready view.
The Impact
Reduced manual coordination
Operations
Teams shifted from searching for information to acting on structured recommendations, significantly reducing time spent on manual data gathering and reconciliation.
Faster month-end close
Finance
Exception detection and automated routing shortened the reconciliation cycle, with fewer manual touchpoints and cleaner audit trails.
Improved forecast reliability
Planning
AI-assisted demand sensing replaced spreadsheet-based forecasting, reducing both stockout and dead stock events across regions.
