The story I’m about to tell is one I know you’ve lived through. How? Because I’ve been there, too.
A product that looked like a solid bet during line planning somehow ends the season with both stockouts and markdowns. Merchants retrace their logic and planners pull reports to diagnose where things diverged as allocators scramble to manage the fallout.
It’s rarely the result of a bad idea or a bad call. More often, it's the slow drift that happens as a good idea moves through a process held together by spreadsheets, outdated data, and handoffs. A process that loses a little clarity at every stage.
This is the anatomy of one bad outcome — a fictional story built from very real patterns — and how that story could play out when assortment planning is unified and intelligent.
Act I: The merchant’s plan
Let’s start with a quilted vest — a style that quietly overperformed last year. It sold well during transitional weeks, held its price, and surprised teams with its momentum. So as our merchant builds the new season’s line plan, including the vest feels like a smart, defensible choice. Last year’s success becomes the anchor: if it did this well before, it deserves another bet.
That instinct makes its way into the line plan. But as the merchant begins shaping the assortment strategy, tooling constraints kick in. Historical performance is buried in rollups that hide nuance. Attribute-level insights are difficult to extract. And the ability to understand how different stores responded to the vest — or how they might respond this year — is limited.
The merchant moves forward anyway. The strategy is thoughtful. The product deserves its spot. But the clarity behind the decision doesn’t survive the spreadsheet it’s recorded in.
The first handoff approaches.
Act II: Quantification drift
When our planner receives the merchant’s vest strategy, they inherit its blind spots — and face new ones of their own.
Last season’s store clusters remain stubbornly locked in. APS logic pushes oversimplified depth calculations that smooth over meaningful differences. Size decisions come from what could best be described as an optimistic curve, carried forward because there’s no better way to predict how this vest will behave across sizes this season. Forecasts look backward rather than forward, obscuring evolving demand patterns. And there’s no way to easily model alternate strategies before committing.
Aligning the vest strategy to financial targets requires manual reconciliation, and every adjustment introduces the possibility of even more drift. By the time breadth, depth, and size assumptions are finalized, the assortment has shifted from the merchant’s original vision — not through error, but through static inputs and partial visibility.
The relay moves forward.
Act III: In-season consequences
When the vest finally reaches stores and online channels, gaps appear quickly.
In some clusters, it sells faster than expected, especially where last year’s performance was driven by specific attributes the spreadsheet had blurred. Depth isn’t sufficient, and upside is left on the table. In other clusters, where customer behavior differed more sharply than the planning data suggested, units move slowly. Markdown exposure builds early.
Size curves begin to break in familiar — and predictable — ways. Teams see quickly what they wish they’d known earlier. Everyone begins diagnosing where the plan went sideways.
The answer is nowhere and everywhere. Each stage introduced a small distortion the tools couldn’t catch.
The idea was solid. The execution never stood a chance.
The assortment story, rewritten by Anaplan
Now imagine that same vest — the same instincts, the same creative vision — carried through an intelligent process built to validate bets with actionable specifics and enable careful handoffs.
With Anaplan’s Assortment Planning application, merchants build their line plans and strategies directly inside a unified environment. Historical performance is visible at the detail that matters, not hidden in aggregated totals. Breadth and depth strategies are informed by clear insight into where the vest performed, which stores over-indexed, and the nuances that influenced last year’s lift.
As planners take the baton, the drift that once felt inevitable starts to disappear. Predictive forecasting offers a forward-leaning read on demand using existing trends, rather than relying solely on last year. Scenario modeling lets teams pressure-test different depth and distribution decisions before committing. Financial guardrails stay visible throughout, so adjustments no longer create unintended misalignment.
By the time the assortment is finalized, the plan reflects merchant intent, quantified demand, and targets pulled in from the MFP — not a compromise forced by disconnected tools.
And when the vest reaches stores, it performs the way it was designed to: strong where demand is real, appropriately controlled where it isn’t, and aligned to the broader financial strategy.
The story doesn't end in surprise. It ends in success.
A better way to relay
Assortment outcomes don’t fall apart because people lack judgment. They fall apart because each leg of the process operates with incomplete information.
A spreadsheet hides detail. A cluster oversimplifies behavior. A forecast misses signal. A size curve reiterates history that may not repeat itself. And each of these small gaps compound as spreadsheets, decks, and scribbled notes pass from hand to hand.
Anaplan doesn’t eliminate the relay — it strengthens it. It gives merchants, planners, and allocators a shared foundation: one strategy, one plan, one source of truth. The handoffs stop leaking information. The intent carries through. And the assortment that arrives on the floor is the one the team actually meant to build.
That’s how a quilted vest goes from a cautionary tale to a commercial success.
