HIGH-VALUE FIT Highly innovation-active · ideal product, supplier and ERP fit

Wholesale & Distribution

Supplier catalogues, item masters, customer pricing and warehouse data quickly fall out of alignment.

USE CASE

Product catalogue at scale

AUD 100k–450k p.a.

TANGIBLE BENEFIT

Faster catalogue onboarding, fewer pack, attribute and channel errors, and more consistent customer content.

VALUE BASIS

Catalogue maintenance time, returns, order errors and channel-rejection costs reduced.

IF NOTHING CHANGES

Slow launches, incorrect pack sizes or prices and inconsistent content continue to create returns and disputes.

ILLUSTRATIVE BUSINESS CASE25,000 SKUs × 5 minutes annual maintenance saved × AUD 55/hour = approximately AUD 115k.

AI READINESS

From trusted evidence to a controlled capability.

Product identifiers, pack sizes, units, attributes, hierarchies, media, supplier facts and channel requirements are governed across ERP, WMS and commerce. Source authority and effective dates are explicit, with channel rejection, return and correction reasons linked to catalogue defects.

WHAT AI CAN DO

AI can extract and normalise supplier catalogue data, classify products, detect unit or attribute inconsistencies and generate channel-ready copy. It can prioritise enrichment and mapping work while governed product owners approve facts and release.

01Starting point

A supplier sends 2,000 new lines with inconsistent pack descriptions and abbreviations, requiring weeks of manual mapping before customers can order them.

02AI in action

Document and matching models propose product classes, pack conversions and attribute mappings with confidence and source evidence. Low-confidence unit conversions are held, while approved facts feed draft channel content.

03Measured outcome

Catalogue staff approve high-confidence mappings and resolve exceptions once. Measures include lines onboarded per day, attribute completeness, unit errors, channel rejection and order corrections.

HUMAN CONTROL

Product and commercial owners approve identifiers, units, claims, prices and publication; AI cannot infer unsupported specifications or release a catalogue automatically.

PROOF MEASURES

Catalogue onboarding throughput
Attribute and unit accuracy
Channel rejection and order correction

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USE CASE

Supplier and inventory truth

AUD 150k–700k p.a.

TANGIBLE BENEFIT

Fewer stockouts and rush orders, with lower excess inventory and clearer supplier availability.

VALUE BASIS

A 1–3% improvement in inventory deployment plus lower expedite and lost-sale cost.

IF NOTHING CHANGES

Excess and obsolete stock grows while missed sales and urgent freight continue despite high inventory.

ILLUSTRATIVE BUSINESS CASEAUD 8m inventory × 2% working-capital release = AUD 160k before service gains.

AI READINESS

From trusted evidence to a controlled capability.

Supplier, item, contract, purchase order, receipt, location, stock and availability data is linked with effective terms and governed units. Actual lead times and service outcomes are measurable, inventory events reconcile and forecast history includes promotions, shortages and known constraints.

WHAT AI CAN DO

AI can forecast demand and lead time, recommend replenishment or safety-stock review, detect inventory anomalies and identify supplier-performance deterioration. Scenario models can balance availability, working capital and expedite risk without placing orders autonomously.

01Starting point

A high-volume item repeatedly stocks out because the ERP lead time is outdated, while slower items from the same supplier accumulate excess inventory.

02AI in action

The model estimates item-level demand and observed supplier lead time, then recommends different reorder points and explains the service and inventory trade-offs. It flags a recent delivery pattern that reduced confidence.

03Measured outcome

Planning and procurement approve revised parameters for a controlled trial. Measures include stockout days, fill rate, forecast error, expedite cost, inventory turns and working capital.

HUMAN CONTROL

Planning and procurement owners approve orders, parameters and supplier actions; AI recommendations respect contractual, cash, capacity and service constraints.

PROOF MEASURES

Fill rate and stockout days
Demand and lead-time forecast accuracy
Inventory turns, expedite cost and working capital

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USE CASE

ERP migration and consolidation

AUD 120k–600k project value protected

TANGIBLE BENEFIT

Clean products, suppliers, customers, prices and balances enter the target ERP with controlled reconciliation.

VALUE BASIS

Typically 10–20% of programme effort otherwise consumed by avoidable data defects.

IF NOTHING CHANGES

Duplicate masters, wrong units, prices and opening balances disrupt cutover, orders and inventory.

ILLUSTRATIVE BUSINESS CASEAUD 2m ERP programme × 12% preventable data rework = AUD 240k protected.

AI READINESS

From trusted evidence to a controlled capability.

Products, suppliers, customers, prices, units, inventory, balances and open orders from each legacy business have profiled quality and approved target rules. Duplicate resolution, company-specific exceptions, retention and reconciliation are controlled across repeatable rehearsals.

WHAT AI CAN DO

AI can propose cross-company entity matches, cluster inconsistent attributes, identify obsolete records and generate candidate mapping and test evidence. It accelerates consolidation analysis while deterministic rules protect prices, quantities and financial balances.

01Starting point

Two acquired distributors use different product codes, customer duplicates and incompatible pack units, causing duplicate target masters and incorrect opening stock in rehearsal.

02AI in action

An AI-assisted workbench compares identifiers, descriptions, suppliers, units and transaction patterns, proposes match candidates and flags pack conversions requiring owner review. Approved mappings are replayed against controlled totals.

03Measured outcome

Business and finance owners resolve the candidates before the next rehearsal. Measures include target duplicates, mapping cycle time, inventory and balance variance, repeat defects and post-go-live order disruption.

HUMAN CONTROL

Business, finance and data owners approve all merges, conversions and cutover decisions; AI cannot change prices, balances or scope without controlled approval.

PROOF MEASURES

Duplicate target records
Inventory and balance reconciliation
Mapping effort and post-go-live disruption

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WHOLESALE & DISTRIBUTION

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