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Buy Planning2026-06-26

A seasonal buy plan, signed off in one review

A 14-store apparel buyer had to cut next season's open-to-buy by 12% — and built a sharper plan on two years of live sell-through instead of intuition.

Outcome

−12% OTB absorbed

Network

14 stores · lifestyle apparel

Measured outcomes

−12%

OTB absorbed, no lost bestseller volume

2d → 3h

To refresh sell-through analysis

70/30

Solya recommendations / buyer adjustments

What's wired up

Systems connected

MERCH

Merchandising system

2 yrs sell-through · SKU×store×week

ECOM

E-commerce platform

Online sales

SUP

Supplier data

MOQs · margin floor

Before · After

Before

A smaller buy, on stale data

After two seasons of weak full-price sell-through, leadership asked buyers to cut next season's open-to-buy by 12%. The data to build a sharper plan existed, but monthly reports took two days to assemble and arrived three weeks stale.

After

One plan, validated in a single review

Solya unified two years of sell-through at SKU × store × week, then proposed a buy plan inside the new budget, the supplier MOQs and the team's margin floor. The team committed a hybrid plan three weeks early — and the network director signed it off in one pass.

The challenge

It was spring 2025. After two seasons of disappointing full-price sell-through, leadership asked buyers to cut next season's open-to-buy by 12%. A third miss would force a rethink of the network's expansion plans.

The team had ten weeks to build a smaller, sharper buy. Every category, supplier and store tier was now open to challenge, and the old playbook — senior intuition plus last year ±X — wouldn't survive that scrutiny.

The data to do better existed, but it never reached the buying table. Sell-through reports were pulled monthly, aggregated, and took two days to build. By the time anyone analysed them, three weeks had passed and the conclusions were half-stale.

A jacket selling out in one store could sit untouched in another for weeks. The spreadsheet only saw "jackets sold OK across the network." Decisions ran on data that was technically available but operationally invisible.

What we changed

Solya unified two years of sell-through at SKU × store × week granularity across every store, channel and category, all through its data layer. The team could finally see real performance of every product, in every store, against every week.

On top of that signal, Solya's intelligence layer built a recommended buy plan per category, supplier and month. It respected the new −12% budget, the supplier MOQs and the team's margin floor — and every line traced back to the data behind it.

How decisions get made

Each recommendation came with its reasoning attached: the sell-through curves, the store-level performance, the lookalike SKUs from prior seasons. The team didn't hand the buy to a model. They used it to challenge their own assumptions, line by line.

They committed to a hybrid plan — roughly 70% Solya recommendations and 30% buyer adjustments — with every decision and its rationale logged for the next review.

Where it lands

The committed plan flowed back through the orchestration layer into the merchandising system and the buyers' working view, three weeks ahead of the usual deadline. There were no meetings about which spreadsheet was right.

For the first season in three, the network director didn't ask the team to redo their numbers.

What changed

  • −12% of open-to-buy absorbed without losing any bestseller volume
  • Sell-through analysis refreshed in 3 hours instead of 2 days
  • A 70/30 split of Solya recommendations to buyer adjustments, every line traceable to data

Related: see how network-aware allocation carries the same buy into each store, or how continuous replenishment keeps it on track through the season.

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