Pre-order optimisation:
Ordering with data instead of gut feeling
How a two-stage model (LightGBM + LSTM) uses trend signals, trade-fair orders and weather data to improve pre-order accuracy by 42% and then cuts the forecast error by around another 40% after 2 in-season weeks.
This module is part of a six-part model calculation about a fictional mid-sized fashion company (1,800 SKUs, 18 markets). All figures, datasets, model metrics and euro amounts are entirely fictional: they illustrate the methodology and the kind of effect achievable. They are not results of real clients and not a performance promise. What values are achievable on your real data is established only in a pilot project.
01 The problem: ordering blind, 6 months before delivery
Why 35% of jeans end up in the sale
A fashion company with 1,800 SKUs (jeans, t-shirts, shirts, hoodies) must place its pre-orders at production sites 6-8 months before delivery. At that point there are no weather data and no sell-through figures for the coming season, only samples, buyers' intuition and prior-year comparisons.
The result: 35% of merchandise ends up sold at markdown prices (average discount: 42%). At the same time, the stockout rate stands at 8% (published: ~8% revenue loss from out-of-stock, IHL), and re-production is not an option with 8-12 week lead times. Between overstock and stockout lies the sweet spot that only data can hit.
02 Data foundation: 4 seasons, 1,800 SKUs, 18 markets
What your merchandise management system already knows about your customers
Buying teams must order summer stock in January for August delivery, without knowing whether the summer will be warm, whether wide-leg jeans will continue to boom or whether a TikTok trend in March will turn everything upside down. Classic pre-ordering is based on prior year ±10%, seasoned with buyers' opinion. That is no longer sufficient.
03 Exploratory analysis: where the pre-order goes wrong
The patterns your buying team recognises but cannot quantify
Wide-leg jeans have a pre-order deviation of 48%, so nearly every other unit is planned incorrectly. The reason: trend-driven items have no stable prior-year baseline. Basic t-shirts, by contrast, deviate by only 12%, because the prior-year logic works there. The model must therefore work category-specifically, not with a one-size-fits-all method.
04 Feature engineering: what makes the pre-order better
External signals that already exist 6 months before season start
The key: the model operates in two stages. Stage 1 (pre-order, 6 months out) uses the 12 long-lead signals. Stage 2 (in-season adjustment, from week 2) corrects the pre-order based on the first real sell-through data, recommending redistributions between markets, flash re-orders or early markdowns.
05 Model: two-stage demand prediction
Pre-order recommendation + in-season correction in a single system
The pre-order alone (Stage 1) is already 42% more accurate than the current buying method. But the real leverage comes from Stage 2: after just 2 weeks of sell-through data, the LSTM corrects the forecast to 11.2% WAPE, which enables timely redistribution. A bestseller in Munich that is underperforming in Hamburg? The system detects it in week 2 and reallocates 200 units. The actual improvement depends on data quality and adoption rate within the buying team. The model calculation assumes: first-year results typically land at the lower end of the range, with the effect increasing in subsequent years as the data foundation matures and the model is fine-tuned. The 31.8% baseline and the in-season value are assumptions of the model calculation; the relative improvement sits within the published band of 20-50% (McKinsey).
06 Business impact: the pre-order calculation
What better pre-orders mean in euros
| Category | Units/year | Overstock current | Overstock with model | Savings | Lever |
|---|---|---|---|---|---|
| Jeans Wide Leg (trend) | 320,000 | 48% | 38% | €0.95M | Trend signals via social |
| T-Shirt Graphic | 256,000 | 42% | 33% | €0.60M | Pinterest save rate |
| Hoodie | 384,000 | 31% | 24% | €0.55M | Long-range weather forecast |
| Jeans Slim Fit (NOS) | 704,000 | 18% | 14% | €0.90M | Carry-over stability |
| T-Shirt Basic (NOS) | 576,000 | 12% | 9% | €0.45M | In-season redistribution |
Top 5 categories = €3.45M of €3.84M; the rest is spread across the remaining categories. Savings per category include both the overstock and the stockout share.
The result shown of €3.84M is a model calculation: 224,000 avoided markdown units × €12 net + 96,000 recovered stockout units × €48 contribution margin × 25%. The assumption: in the first year, typically around half of the potential is achievable, with the effect increasing in subsequent years. The greatest leverage lies in trend categories (wide leg, graphic tees): planning accuracy is worst there today, and external signals deliver the most added value.
07 Next steps for your brand
From analysis to implementation
Run your last 4 seasons through the model. Would the model have delivered a more accurate pre-order? Backtesting with real figures.
Next season: pre-order for 100 focus SKUs with model recommendation vs. 100 SKUs without. A/B comparison at season end.
From week 2: live comparison of pre-order vs. sell-through. Automated recommendations for redistributions, flash re-orders and early markdowns.