You Can Have Too Much Inventory and Still Not Have What You Need

American Eagle just discounted excess stock while still not having what shoppers wanted.
Manufacturers hit the same wall with raw materials and components every day.

The hardest inventory problems aren’t shortages. They’re mismatches: too much inventory, and still not enough of what you actually need.

American Eagle provided a useful example this week. The retailer reported inventory costs up 14% year over year, partly because of tariffs, and it kept discounting older merchandise while rebalancing stock across brands as consumer preferences shifted. The problem wasn’t that the company needed more inventory – it was that some of what it had no longer matched what customers wanted.

Manufacturers experience the same problem every day. The warehouse can be full, working capital can be increasing, and the balance sheet can show a healthy inventory value but the production floor is still waiting on one component.

Picture a Friday walk around a typical plant floor: three pallets of a component that hasn’t moved in eight weeks, sitting a few aisles from an empty bin for the part two operators are waiting on right now. Same building, same shift, opposite problems.

That’s why inventory quantity alone tells you remarkably little. The better question is whether the business has the right inventory, in the right place, at the right time, for what it needs right now. This is what we call Operational Truth, and it shows up across TransLution customers whether the product is cheese, electrical fittings or furniture.

Inventory has two opposite failure modes

Manufacturing leaders spend a lot of time worrying about shortages, understandably – missing material can stop a line, delay a shipment or force expensive expediting.

Excess inventory creates a different kind of risk. It consumes cash, takes up warehouse space, and can quietly become obsolete before anyone notices.

Manufacturers have to manage two opposite risks simultaneously: running out; and having too much. Both come back to the same foundation – Operational Truth.

A&A Electrical, part of the Dewhurst group, shows what happens when a business closes that gap. The company supplies lift and electrical products to customers who rely on quick quotations, accurate stock availability and next-day delivery. By bringing enterprise-scale visibility into a business that didn’t need an enterprise-scale budget to get there, A&A cut warehouse costs by up to £200,000 a year. It’s a visibility story.

Start with the Big Five

We use what we call the Big Five of Operational Truth.

1. What do you have? Not what the spreadsheet says, what actually exists.
2. Where is it? Receiving, storage, allocated to a job, out on the floor, in quality, finished, or waiting for shipment.
3. Who is using it? Which production order, which customer, which machine, which shift.
4. Is it where Syspro thought it would be? When physical reality and Syspro start to diverge, purchasing and planning decisions get made on assumptions instead of facts.

We wrote about this exact gap in You Can’t Cut What You Can’t See: during a disruption, the floor keeps moving while the system freezes, and the longer that gap stays open, the more the business is planning against a memory of the warehouse rather than the warehouse itself.

5. What is it costing you? Not just the purchase price — the cost of holding it, moving it, expediting it, or writing it off after demand has moved on.

That fifth question turns inventory from an operations discussion into a business one.

Corinthian Furniture is a good case study for the first four of the Big Five. As the business grew, its existing inventory system couldn’t keep pace. Rather than a single sweeping fix, Corinthian implemented TransLution in phases over two years, adding RFID-driven work-in-progress tracking along the way. The result was better warehouse control and stronger traceability, built at a pace the business could absorb.

The inventory number is not the insight

Imagine two plants each carrying $10 million of inventory. On a financial statement they look identical but operationally they may be completely different.

Plant A has high inventory accuracy, predictable consumption, reliable suppliers and material aligned with scheduled demand. Plant B has excess slow-moving stock, critical shortages, inaccurate locations and emergency purchases most weeks. Both report inventory of $10 million.

One plant has inventory. The other has uncertainty.

Days to Constraint, and its opposite

In my last article I introduced Days to Constraint: instead of reporting Raw Material X at 8,000 units, ask how many days remain before that material limits production, given current stock, expected receipts and consumption rate.

That connects inventory to time, and time gives management a decision window. If a material becomes critical in 18 days and an alternate supplier needs 12 days, management still has options. If it becomes critical tomorrow, most of those options have disappeared.

This is exactly the dynamic we described in Predictability = Visibility + Control: The Lancewood Case Study. Before automating production data capture, one of South Africa’s largest cheese and dairy producers needed three to five days to update its planning spreadsheet from paper records. With live data flowing in automatically, the same update takes three hours. Shrinking the gap between something changing on the floor and management knowing about it is what turns a planning exercise into an actual decision window.

There’s another side to this. A product family slows, demand shifts and production plans have changed, but purchasing hasn’t caught up and inventory keeps arriving. Days on hand climbs from 30 to 45 to 70. Nothing has failed and there’s no shortage alert. Production hasn’t stopped but cash is quietly accumulating on the shelf.

Call it the days-to-excess question: how quickly current inventory is moving toward a level that no longer makes economic sense against expected demand, weighed against open purchase orders, shelf life and obsolescence risk. It catches inventory turning into a problem before it becomes a write-off.

The real target is a range, not a number

Put the two together. On one side too little inventory creates production risk. However, too much creates working-capital risk. The goal is not maximum inventory, and it isn’t minimum inventory, it’s keeping stock inside the range where the factory runs reliably without tying up cash it doesn’t need to.

Why this matters more when demand changes

The American Eagle example makes another point: inventory that made sense three months ago can be the wrong inventory today. Manufacturers see the same thing when a customer changes a forecast, an order slips, a supplier changes MOQ, or demand shifts from one SKU family to another. Total demand can stay flat, but underneath it, Product A is up 25% and Product B is down 20%. The company looks stable, but the factory is living through a real mix change.

That’s why inventory predictability can’t lean on historical averages alone; it needs visibility into what’s changing right now.

Predictability = Visibility + Control

That’s the equation we keep coming back to: predictability isn’t about forecasting demand perfectly — it’s the combination of seeing what’s actually happening and acting on it before it gets expensive.

Visibility means you know what you have, where it is, how fast it’s moving, and where demand is shifting. Control means adjusting purchasing, resequencing production, reallocating material or expediting the one thing that actually matters, while still protecting the customer commitment.

Customers will change their minds. Suppliers will miss deliveries. Tariffs will move input costs. None of that is fully controllable. How quickly a business sees the change, and how quickly it responds, is.

Excess inventory is often uncertainty expressed in dollars

When manufacturers don’t trust their inventory or supplier reliability, they protect themselves by carrying more — more safety stock, more buffer stock, more emergency inventory. That’s rational behavior when uncertainty is high.

But it also means poor visibility can quietly turn into a working-capital problem. Better visibility can let a business protect production with less inventory — the same conversation A&A Electrical had on its way to £200,000 in annual savings. A CFO conversation, not just a warehouse one.

The most dangerous inventory may not be the most expensive

A manufacturer might hold $2 million of one raw material and $20,000 of another. The $20,000 component could matter more, if it is sole-sourced and stops $5 million of finished-goods production the day it runs out.

Inventory should be ranked by criticality and velocity as much as by value. That is what turns a number into intelligence.

From Inventory Reporting to Inventory Decisions

A traditional report might read: Component A, 12,400 units; Component B, 46,000 units; Component C, 8,200 units. The report is accurate, but it doesn’t tell management what to do.

A decision-ready view looks different. Component A: production constraint in 9 days, next receipt in 13, three customer orders affected, 48-hour action window. Component B: days on hand up from 34 to 72, expected consumption down 23%, open purchase orders adding another 18 days of supply — time to review replenishment. Component C: healthy, relative to current demand and supplier reliability.

Same inventory. Completely different intelligence. One tells you what exists while the other tells you where to act.

The goal is the right inventory, in the right place, aligned with what production and customers actually need. That takes more than a count; it takes connecting physical reality to demand, time, cost and consequence.

So: does your team know which materials are heading toward a shortage, which toward excess, and what either one will actually cost?

Your floor. Your facts. Right now.

If your team can’t quickly answer that, TransLution can help. Take the TransLution 5 Question Audit and find where better Operational Truth could improve inventory confidence, working capital and production predictability.