The systems are running. The work is what happens around them.

Opening

Distribution operations run on operational software that's been in place for years - the ERP, and the added purchasing tools and sales platforms built around it. The investment has been made. The reports are being generated.

What rarely matches the investment is the operational work that turns what the systems produce into actual outcomes. The buyer opens the recommended purchase queue and processes some of it, sets some of it aside, and works around the parts the system doesn’t account for. The dashboard shows the dead stock report and the people in the operation know which SKUs on it are genuinely dead and which ones aren’t; the knowledge lives in the experienced operators rather than in anything written down.

That’s how distribution actually runs. Sophisticated software, used by experienced people who’ve learned which outputs to trust, which to verify, and which to set aside.

That model works right up until the experienced people get busy, retire, or move on.

What the work looks like

The interpretive work happens in specific seats. Whoever sits in them - your person or ours - is making decisions the platform can’t make on its own.

Three of those seats show up in almost every distribution operation we work with. They’re the clearest examples of the pattern, so they’re worth describing in detail. They’re not the boundary of what we do.

At the purchasing chair

A buyer sitting in front of a recommended purchase queue is making decisions the system can’t make. The work is figuring out which lines reflect actual demand, which reflect data the system didn’t have full context on, which need to be modified, and which need to wait.

The work happens explicitly. Research before acting. Documentation of where the system’s view of demand diverges from the operational reality. When the queue recommends a purchase that doesn’t match what’s been concluded, the purchase doesn’t get made.

A normal interaction looks like this: “You didn’t click the queue on that line.” “Yeah, the number’s wrong. I’m not buying for about ten days. Current estimate is closer to forty units.” That conversation, repeated through the week, is what prevents the inventory that would otherwise be sitting unmoved in ninety days.

When the inventory has aged

That’s the work at the front of the cycle. The other half is the inventory that’s already on the shelf.

Most operations bring us in after the inventory has accumulated. Identifying it is trivial; any analyst can produce a list of SKUs that haven’t moved in ninety days. The list itself doesn’t change anything.

What changes the list is someone making the calls. Why was this bought? Is it allocated? Can it go back to the vendor? Is there an RMA path? Can it move to another branch? That work doesn’t happen on its own. Most operations don’t have anyone whose job it is to do it.

The work gets done by someone making those calls, working directly with purchasing agents and branch managers as operational partners. The objective is movement before write-off.

Product data

The symptoms are familiar. Quotes pull outdated specs. Submittals go out with attribute data that no longer matches what the manufacturer is publishing. Customer-facing catalogs show information someone has to apologize for later.

Distribution businesses run on product data, and that data is inconsistent at the source, manually maintained in most organizations, and rarely clean enough to power the workflows depending on it. The platforms can hold the data. They can’t fix it. We build and operate the workflows that bring it under control: connecting manufacturer feeds, normalizing attributes, surfacing drift before it affects customer-facing output. The work is foundational and rarely visible until it isn’t done.

Elsewhere in the operation

The pattern repeats across the rest of the business. Marketplace compliance work that nobody owns until Amazon suspends a listing. Sales team tooling that turns a CRM into something the outside reps will actually open. AP and AR workflows that have been held together by one person’s spreadsheet for six years. Dispatch decisions that the TMS doesn’t have the context to make. Field issues that span three systems and live in nobody’s job description.

The roles look different. The work has the same shape: a call the system isn't equipped to make, made by someone with the context and the tooling to make it well. If a wholesaler has felt the pain, we've probably sat in that seat.

How it gets supported

The work is hard to do well without tooling built for it. The recommended purchase queue, the aged-inventory list, the product data feed - these are outputs the platform produces and then hands off. Whoever picks them up has to do what the platform can’t.

We build the tooling that makes that work tractable. Purpose-built workflow layers that sit alongside the ERP and whatever's been added around it - designed around the actual sequence of decisions the person doing the work is making, shipped in weeks, and built to connect: the buyer’s tooling talks to the aged-inventory workflow, which talks to the product data layer, because they were designed that way from the start.

Where AI is part of that tooling, we use it deliberately and tell clients what we’re using. The distribution business has no shortage of vendors pitching AI right now. Our posture is deliberate, not promotional - the boundaries are real, and the model isn’t somewhere else learning from your operation.

That methodology has a name internally - RAPID - and the logic is deliberate. One person, one seat, one workflow at a time. Tooling that gets adopted because it was built for the way that person actually works. Adoption that compounds because each tool connects to what came before and what comes next.

Who does the work

Sometimes that’s your person, now equipped to do work they didn’t have time for before. Sometimes the role is open - the senior buyer retired, the product data role was never filled, the aged-inventory work has no owner - and we place a trained specialist, already fluent in the tooling, ready to do the work on day one.

Same methodology either way. The tooling is the constant. The staffing answer depends on what the operation actually needs.

What stays behind

The knowledge that used to live in the experienced operator’s head ends up encoded in the tooling and the workflows around it. When the next person picks it up - yours or ours, this year or in five - the institutional memory is still there.