AI for Warehouse and Dispatch Teams

The floor runs on systems, the office runs on email

A distribution business shipping 600 orders a day usually has a warehouse management system that knows exactly where every pallet is. The problems start around it. Orders arrive as emailed PDFs from trade customers. Carriers send delay notifications in their own formats. Customers phone to ask where their delivery is. A driver texts a photo of a damaged pallet. The dispatch supervisor keeps a spreadsheet of exceptions because the WMS does not have a good place for them.

That layer of emails, documents, calls and photos is where AI for warehouse and dispatch teams is practical today. The core logic, what is in stock, what to pick, which van it goes on, belongs in systems built for it.

Capturing orders that arrive as documents

Trade customers who email purchase orders are often a warehouse's best customers and its biggest admin burden. Someone reads each PDF and keys it into the order system, matching the customer's product codes to yours.

AI extraction handles this well: read the document, pull out customer, delivery address, lines, quantities and requested dates, and map the customer's descriptions to your SKUs using past orders. Stock checks and pricing are then done by your normal systems. We describe the full flow in our post on email-to-order automation.

• Confidence shown per line, with uncertain SKU matches sent to a person
• New or unusual delivery addresses flagged rather than accepted
• Duplicate order detection, because customers do resend the same PO
• Order created as a draft until checks pass

Carrier updates and delivery exceptions

Carriers communicate through a mix of portals, API feeds and emails. Delay notices, failed delivery attempts, address queries and damage reports all need someone to read them and act.

The daily result is an exception list that is complete and current, rather than whatever the supervisor had time to add to the spreadsheet.

Where is my order

'Where is my order' calls and emails can take up a large share of a customer service team's time in distribution businesses. Almost all of them can be answered from data you already hold: order status in the WMS, the consignment number and the carrier's tracking feed.

An assistant on email, chat or the phone that looks up the real status and answers in plain language removes most of those contacts. It must answer only from live data. If the tracking feed is down, it says it cannot check right now and passes the query to a person, rather than guessing a delivery date.

The daily operations summary

Supervisors spend the first half hour of each shift working out what went wrong yesterday. A morning summary that reads the WMS exceptions, carrier notices, customer complaints and shift notes, then lists the issues with likely causes, gives that half hour back.

1. Orders not despatched on their due date, with the reason recorded
2. Carrier failures by carrier, so patterns show up
3. Picking errors reported by customers, grouped by product or zone
4. Anything unusual in the shift notes, such as equipment or staffing issues

All counts come from the systems. The model only writes the explanation and groups the free-text notes.

Keep the summary short enough to read on a phone in the yard. If it runs to three screens, supervisors will stop opening it by the second week, and the one line that mattered, a carrier failing the same postcode every day, gets missed.

What AI should not run in a warehouse

Stock allocation, slotting, pick path optimisation and vehicle routing are optimisation problems. Mature software and operations research methods handle them well and predictably. A language model is the wrong tool, and a warehouse where a model decides allocation is a warehouse with unexplained mis-picks.

Demand forecasting can use machine learning, but that is a separate project needing clean history. Our guide to AI for logistics and supply chain covers where forecasting and optimisation fit.

And be realistic about the floor. Pickers wearing gloves in a cold store do not want to talk to a chatbot. Useful AI in a warehouse is mostly invisible to the people on the floor.

When it is worth doing

If most of your orders already arrive through EDI or an ecommerce integration, and carriers feed tracking via API, there is less paperwork to handle and the case is weaker. The case is strongest for B2B distributors, 3PLs and wholesalers with many trade customers ordering by email and several carriers.

At SpiderHunts, we usually start with a two-week sample of every inbound email and document the office handles, sorted by type. The count by type tells you where the hours are. The build itself runs through our automation service and connects to your WMS and carrier accounts rather than replacing them.

Frequently asked questions