Predictive inventory management
AI links historical sales and seasonal patterns to the current stock position and triggers reorders automatically — 25–35% less dead stock, 40% fewer stock-outs.
OUR WORK/AI-WAREHOUSE
LOGISTICS · EXAMPLE CASEAI-Warehouse
An example case for a mid-sized distribution centre in garden tools: four AI applications that improve inventory, order fulfilment, purchasing and customer insight in a coherent way.
From reactive replenishment to predictable inventoryTHE CHALLENGE
Full shelves,
and still saying no.
A distribution centre in garden tools — around 150 employees, three warehouses, 2,500 items and over 500 retailers, garden centres and DIY stores as customers — manages its inventory in Excel alongside a legacy system. The result: 15 to 20 percent of the stock doesn’t sell, while popular items are regularly sold out. Three to four hundred thousand euros in capital is tied up in stock that doesn’t move.
Value also leaks away in the warehouse and on the sales side. Orders are picked and packed without prioritisation, with a lead time of two to three days and around five percent incorrect shipments. Purchasing reacts to shortages instead of planning ahead — the season is structurally misjudged. And there is no picture of buying patterns per customer, so upsell, cross-sell and customer retention are left on the table.
- 0115–20% dead stock; €300–400k tied up in inventory that doesn’t move
- 02Picking without prioritisation: 2–3 days lead time and ~5% incorrect shipments
- 03Seasonal demand misjudged — reactive purchasing instead of proactive
- 04No insight into buying patterns: upsell, cross-sell and retention left on the table
OUR APPROACH
One data stream,
four improvements.
- 01 · INSIGHT
Data brought together
Three years of transaction data from Excel and the legacy system brought together into one reliable base: items, orders, customers and seasonal patterns.
- 02 · FOCUS
Value first
Four applications chosen where the most money leaks away: inventory, order fulfilment, purchase planning and customer insight — each can be introduced separately.
- 03 · ADOPTION
In the daily work
Forecasts and recommendations land in the screens of buyers, warehouse staff and account managers — no separate system on top, but better work within the existing process.
THE SOLUTION
Four AI modules,
one coherent whole.
Each module solves one bottleneck, and together they reinforce each other: a better demand forecast feeds inventory control, faster orders and sharper customer insights.
Intelligent order routing
Orders prioritised by urgency, shipping location and pick efficiency, with computer vision checks at packing and consolidated transport routes.
Demand forecast & planning assistant
A machine learning model, trained on three years of transaction data, with automatic seasonal corrections and a weekly view of which products are about to take off.
Customer intelligence dashboard
Customers clustered by buying behaviour, with automated recommendations per customer and early signalling of churn risk.
THE RESULT
Less capital on the shelf,
more revenue per customer.
Inventory that adds up
25–35% less dead stock and 40% fewer stock-outs: the tied-up capital is released, while popular items stay available.
Orders out the door
Fulfilment becomes 50% faster and the error rate drops from ~5% to 0.5% — customers get the right product faster and more often.
Planning instead of reacting
85% more accurate inventory planning saves around €150k per year, and better customer retention delivers 12–15% revenue growth.
“Inventory follows demand — not the other way round.”
YOUR PROCESS NEXT?
Which process could be
better tomorrow?
In a first conversation, we map out your challenge and the most promising AI applications together.
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