Deep Learning × Fashion

AI in fashion: Your collection knows more
than your buying team suspects

6 modules. 6 problems every fashion brand knows. 6 data-driven solutions that turn your existing ERP, webshop and PLM data into concrete euro figures. No new system, no new hardware.

The 6 modules

Each module solves a concrete fashion problem

Click on a module to view the complete interactive notebook with code, visualisations and business impact.

Module 01
Pre-order optimisation
A two-stage model (LightGBM + LSTM) improves pre-order accuracy by 42% and corrects again after 2 in-season weeks, reducing overstock from 35% to 28%.
€3,84M
Savings / year
LightGBMLSTM
Module 02
Trend radar
CLIP analysis of runway images + social-media velocity + Google Trends detects fashion trends 6-9 months ahead of the mass market, which secures the first-mover advantage.
€0,72M
First-mover advantage / year
CLIPSocial Listening
Module 03
Size & fit prediction
Collaborative filtering on purchase history and return patterns reduces the jeans return rate from 38% to 31%, with a checkout widget and no hardware required.
€1,61M
Savings / year
Collaborative FilterRecommender
Module 04
Returns analysis
NLP analysis of 210,000 free-text comments uncovers the 5 true root causes behind "doesn't fit". The quick wins can be implemented straight away.
€0,81M
Savings / year
NLPTopic Modelling
Module 05
Collection planning
A cannibalisation network identifies 400 SKUs as candidates for review in the assortment round, with only 3% revenue loss but up to 18% less overstock.
€0,78M
Savings / year
Graph-AnalyseOptimization
Module 06
Markdown optimisation
A reinforcement-learning agent computes SKU-specific price paths instead of blanket discounts: +400-800 basis points on markdown margin (McKinsey), end-of-season leftover stock 21% → 15% (assumption).
€1,17M
Savings / year
RL / PPOPricing
Interactive

The Fashion Simulator

S01-S04
Four simulations, all assumptions disclosed
Pre-order quantity, season contribution margin as a distribution, returns savings and markdown scenarios: set the sliders to the scale of your company. Every calculation path sits beneath its simulation.
Open all four simulations →
Our approach

No theory - results in weeks

We work with the data you already have. No vendor lock-in, no cloud mandate, no hidden costs.

📊
Your data, your systems
We connect what already exists in your systems: webshop, ERP, PLM, CRM, return comments. No new system required.
⚡
Results before perfection
We deliver a pilot dashboard with real numbers from your data. No 6-month concept - you see the ROI before you invest.
👗
Industry knowledge + data science
We understand pre-order, returns and trend cycles. Our models solve real fashion problems, not academic exercises.

Ready to put your data to work?

Schedule a data workshop →
Evidence & sources

Where the industry figures come from

The levers of the model calculation are aligned with published studies and case reports. Every third-party figure named in the text is documented here with its source.

  • McKinsey: AI forecasts reduce errors by 20-50%; markdown optimisation improves markdown margin by 400-800 basis points.
  • BoF State of Fashion 2025: full-price sell-through down to ~50%.
  • IHL: ~8% revenue loss from out-of-stock.
  • Fit Analytics (Mammut): -20% return rate through size guidance.
  • True Fit/M&Co: -10% among active users.
  • University of Bamberg returns research: ~€19.51 process cost per return; German fashion returns ~50%.
  • Prime AI: value loss of 20-40% of merchandise value.
  • Heuritech (vendor claim): trend forecasts up to 24 months ahead, 90%+. We deliberately assume 6-9 months and 67%.
  • Pareto norm: ~20% of SKUs carry ~80% of revenue.
  • Inditex FY2024: gross margin ~58% (context: initial markup).

The figures of other companies serve as plausibility anchors, but they are not transferred to the model company. All EUR amounts are model calculations based on the assumptions stated in the disclaimer. Retrieval date of the sources: 2026-08-14.

Note on the context of this portfolio

The following six modules use a fictitious mid-sized fashion company (jeans, T-shirts, hoodies, shirts) to illustrate how data science and AI can be applied across the entire value chain, from pre-order to trend identification to markdown optimisation.

All figures, datasets and results are entirely fictitious. They serve solely to illustrate the methodology and the type of impact achievable. No promises are made.

In a real project, the domain expertise of your company is the decisive factor: how does your pre-order process work? What data is available in your PLM, ERP and webshop? Where are the biggest losses, in overstock, returns or missed trends? On this basis we develop models tailored to your reality.

This portfolio shows what questions data science can answer in the fashion industry. The concrete answers emerge only with your data.