You Walked the Floor, Now Walk The Data

Last issue, I made the case for getting out from behind the desk and walking your warehouse floor. You see things no report will tell you: dust on a pallet that has not moved in a year, an overstuffed bin the system swears is at par, or an empty location the ERP still counts as full.

The floor tells the truth.

But the floor walk has a ceiling. You can see one aisle at a time. You cannot see the pattern across 8,000 SKUs, seven branches, and 12 months of demand. You cannot hold every stockout, slow mover, substitution, transfer, and inventory adjustment in your head at once.

No one can.

That is where an AI assistant earns its place. Not to replace the walk. To extend it.

Do not get stuck comparing logos or arguing over which chatbot is best this month. The real difference is whether the tool has clean, approved data and clear business rules.

Your stocking logic. Your substitution rules. Your supplier programs. The way your best branch manager decides whether to transfer, return, liquidate, or hold inventory.

A good system should show where its answer came from, follow the rules you gave it, and know when to hand the question to a person. Your judgment is still the advantage. The tool helps apply it more consistently.

Start with dead and excess stock, because every distributor has both.

You already get the report: slow movers, aging inventory, and on-hand that outruns demand. The report tells you what happened. It rarely tells you what should happen next.

Put that report into a company-approved environment with the right inventory, sales, branch, and supplier data behind it. Then ask better questions:

• Which items have the most dollars tied up?
• Which may qualify for a vendor return under our existing terms?
• Which items are short at another branch?
• Which should be transferred, liquidated, or written down?

Then have it draft the vendor communication for the items that qualify.

Now you have more than a report. You have a ranked work list. The value is not the report. You already had the report. The value is turning it into a decision and putting the work in the right order while the inventory still has value.

The second use case lives at the counter.

A customer needs Type L copper. You are out. A twenty-year counter veteran knows the first response cannot simply be, “Type M will do.” The application matters. The specification matters. Local code may matter. Type L and Type M are not automatic substitutes.

A new hire may not know what questions to ask. So they guess, put the customer on hold, or go looking for the one person in the building who knows the answer.

That is where a properly built assistant helps. Give it approved product data, manufacturer documentation, substitution rules, and an escalation process. The new person asks in plain language. The system asks the right follow-up questions, shows approved alternatives, and points to the source behind the recommendation.

When the answer depends on code, engineering requirements, or a customer specification, it should not bluff. It should tell the counterperson to stop and escalate.

That protects the sale, the customer, and your company’s credibility.

The same principle applies to order status. Connected to approved order, supplier, and availability data, the system can pull together the status, the latest available date, and approved alternatives, then draft a response while the rep moves to the next customer.

The rep still owns the answer. They no longer need to spend 15 minutes digging through screens, emails, and calls to find it.

That is inventory data doing real work at the exact moment it matters.

Now for the use case that puts you right back on the floor.

You cannot count everything every day. Cycle counting has always been a question of where to spend limited count time. An AI assistant can help rank the work.

Count high-value items, fast movers, SKUs with repeated adjustments, unusual demand activity, and locations where the system balance does not line up with history.

The data tells you where to walk. The walk confirms what the data suspected. That is the two working as one.

None of this works on hope. Before you point an AI tool at inventory, three things have to be true.

1. Clean item data. If your item master is a mess, your on-hand is wrong, or your units of measure do not match, the tool will give you a polished answer built on bad information. Fix the data first. It is not glamorous work, but it matters most.
2. Written-down decisions. The rules in your best manager’s head have to live somewhere a tool can read: stocking logic, substitution rules, vendor return policies, and escalation when the answer is unclear. If it is not written down, it cannot be applied consistently. It walks out the door when that manager retires.
3. One real problem. Do not try to boil the ocean. Pick the inventory problem costing you the most money today and point the tool at that one first. Maybe it is dead stock, branch transfers, or cycle-count prioritization. Start there. Learn the pattern. Fix what is broken. Then widen the scope.

The distributors that try to do everything at once usually do nothing.

The floor walk and the AI assistant are the same instinct wearing different clothes. Both are about getting closer to the truth of what is in your building.

One uses your eyes. The other uses your data.

Walk the floor. Then walk the data.

The distributors that do both will find problems sooner, make better decisions faster, and know their inventory better than the ones that do neither.

By Will Quinn

Will Quinn is a thought leader in warehousing with decades of experience in warehouse management, distribution strategy, and transportation optimization. Reach out to him at will@thedistributionguy.com.