Why Inventory Cash Problems Compound Every Season
Insights from Mary Wiegand, Founder & CEO of Boon — a demand planning and inventory management consultancy specializing in fashion and apparel brands.
Most founders who have an inventory problem don't think they have a process problem. They think they had a bad season.
The product didn't land the way they expected. The trend shifted. A competitor undercut them on price. External factors conspired against an otherwise solid bet.
Sometimes that's true. But more often, when you look at the pattern across multiple seasons — not just the most recent one — a different picture emerges. The problem isn't a single bad call. It's a structure that makes bad calls likely every time, because the process never creates a moment to make a better one.
The most common version of that structure is this: you're only looking at your numbers a few times a year. You place the buy, pay the invoice, and move on. By the time sell-through data is telling you something real — that coconut is outselling lime heading into peak summer, that tanks are flying while lightweight sweaters sit — it's too late to act on it. The inventory is placed. The only lever left is trying to negotiate next season's buy to right-size what's already gone sideways.
And then next season arrives, and the same thing happens again.
Why This Pattern Is Hard to See From the Inside
When you're inside a founder-led brand, the planning calendar feels full. There's a buy decision. There's a launch. There's a promotional push. There's an end-of-season review. It doesn't feel like you're only looking at the numbers a few times — it feels like the numbers are always somewhere in the conversation.
But looking at the numbers and making decisions from the numbers are different things.
Most brands track revenue. They watch their top-line performance. They know whether they're up or down versus last year, versus plan, versus last quarter. That's not the same as looking at sell-through by style, by size, by channel, by week — and asking what the data is telling you about where to put more inventory and where to pull back.
The distinction matters because revenue can look fine while an inventory problem is quietly building underneath it. You can be hitting your dollar target while running out of stock in your best sizes and accumulating excess in your worst-performing styles. You can be up year-over-year while carrying more dead inventory than you realize, because the markdowns haven't hit yet.
By the time the markdowns hit — by the time the end-of-season clearance event makes the problem visible — the decision that caused it is six to twelve months in the past. And the next season's buy is already in motion.
What "Buying on Hope" Actually Looks Like
Hope-based planning isn't reckless. It's the natural result of a process that doesn't create space for anything else.
It looks like this: the buying decision is made based on last season's sell-through and a gut-feel adjustment for how the current year is trending. "This year feels stronger" shapes the buy quantity. "This colorway feels right" shapes the color allocation. "Customers have been asking for this" shapes which new styles make the line.
None of these inputs are wrong on their own. Founder instinct is a real and valuable input into a planning process. The problem is when it's the only input — when there's no systematic review of the data, no scenario modeling, no explicit moment where the team asks "what would have to be true for this buy to work, and what happens if it isn't?"
The process never slows the decision down long enough to interrogate it. The buy goes out. The season runs. And whatever the outcome, the learning doesn't get built back into the next decision in a structured way.
This is buying on hope. Not because the founder isn't thoughtful — they are. But because the structure of the process produces hope-based decisions by default.
The Compounding Problem
One season of hope-based buying is recoverable. Markdowns clear the excess. Cash tightens for a quarter. The business absorbs it and moves on.
The compounding problem is what happens when the structure never changes.
Every season of overbuying in the wrong styles or wrong sizes ties up cash that could have funded better decisions next season. Every clearance event trains your customer to wait for the sale. Every markdown cycle compresses your realized margin a little further. Every season where cash is tight going into the buy forces you to be more conservative than you'd otherwise be — not because the market doesn't support a bigger bet, but because last season's inventory problem used up the capital.
Over three or four seasons, the cumulative effect is significant. Not a single dramatic event. A steady erosion of margin and buying power that's hard to attribute to any one decision because it was never one decision — it was the absence of a process that would have made better decisions consistently.
What the Fix Actually Looks Like
The fix isn't more intuition or better product selection. It's a process that creates structured moments to review the data and act on it while acting is still possible.
For brands with seasonal buys, this means building a review cadence that works within the selling calendar — not around it. The specific cadence depends on your lead time and buy structure, but the principle is the same: you need to be looking at sell-through data at regular intervals during the season, not just at the end of it.
What a working in-season review includes:
A consistent read on sell-through by style, by size, by color — not just total revenue. Which specific items are outperforming, and which are lagging? The aggregate number hides the story. The detail is where the decision lives.
A clear view of your current inventory position against your expected sell-through curve. If you're three weeks into a twelve-week selling season and a key style is already at 40% sell-through, that's a signal. If another style is at 8%, that's a different signal. Both of them require a response — either adjusting your remaining promotional calendar, shifting allocation if you have multiple channels, or accelerating markdown timing before the inventory ages further.
A defined set of levers you can actually pull. This is where a lot of brands stop short: they do the review but haven't pre-defined what actions are available in response to what signals. When a style is underperforming, do you have a promotional mechanism to move it? When a style is outperforming and you have replenishment flexibility, do you have a process for acting on that quickly? The review is only valuable if it connects to action.
What this looks like for brands with longer planning horizons:
For apparel brands with nine to twelve month lead times, most of what's happening in the current season can't be changed. The buy is placed. But the current season's data is the primary input for next season's buy — and the discipline of capturing it systematically, at regular intervals rather than as a post-mortem, is what makes the next buy materially better.
A monthly sell-through review during the selling season, feeding directly into a documented set of buy adjustments for next season, is the structural change that breaks the cycle. Not because it gives you a perfect forecast — it doesn't. But because it replaces "this year feels stronger" with "here's what actually sold, in what quantities, through which channels, at what margin, compared to what we planned."
That's not a fortune teller. It's just a process that builds learning into the calendar instead of leaving it to accumulate informally — or not at all.
The Question That Surfaces the Problem
There's a simple diagnostic that reveals whether a brand is operating with a data-driven process or a hope-based one.
Ask a founder: after last season, what specifically did you learn about how your customer buys — by size, by style, by color, by channel — and how did that change the next season's buy decisions?
Brands with a working process can answer this question in detail. They can tell you which styles overperformed and why, which sizes consistently underperform their buy allocation, which channel has better full-price sell-through, and how all of that shifted the current season's buy.
Brands without a working process tend to give a general answer. "We learned that our customer really responds to color." "We learned that the core styles hold up." These aren't wrong, but they're not actionable. They don't produce a specific change to a specific number in the buy plan.
The gap between those two answers is the gap the process is meant to close.
Starting Points
If your current process looks more like the second answer than the first, three things close most of the gap:
Set a sell-through review date at the start of every season. Not at the end — during it. Four weeks in, eight weeks in, and two weeks before the end of the active selling period. Put it on the calendar before the season starts. The brands that do this consistently make meaningfully better decisions than the ones that review retroactively.
Track sell-through at the style and size level, not just total revenue. This requires pulling a report most brands aren't pulling regularly. It takes thirty minutes. It tells you more about where your inventory is working and where it isn't than any top-line revenue number.
Document the learning before next season's buy. After the season closes, write down — in a format that actually feeds into the buy room — what the data said. Which styles beat plan and by how much. Which sizes you consistently over or underbought. What you'd change if you were placing the buy again today. That document is the bridge between this season's outcome and next season's decision.
None of this is complicated. The compounding cash drain it prevents is not.
About the Contributor
Mary Wiegand is the Founder & CEO of Boon, a demand planning and inventory management consultancy that works with fashion, apparel, and lifestyle brands. Mary specializes in helping founder-led brands build the planning infrastructure to make smarter buy decisions — before the PO goes out.
If your inventory decisions feel like they're made on instinct more than data, and you're not sure what to do about it, start here.
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