Fashion Retail: Forecasting Demand for New Items - A Machine Learning Approach

1. Introduction

Forecasting demand in fashion retail represents one of the most complex challenges in the industry. The transient nature of trends in colors, prints, cuts, patterns, and materials, combined with long design cycles, bulk manufacturing requirements, and geographical variations in consumption, creates a high-stakes environment for retailers. Traditional forecasting methods rely heavily on historical sales data of existing items, making them ill-suited for predicting demand for entirely new designs or styles, which is the primary focus of this research.

This paper, presented at the KDD 2019 Workshop on AI for Fashion, addresses this critical gap. The authors from Myntra Designs propose a novel approach that moves beyond time-series analysis of past sales. Instead, they analyze large-scale fashion sales data to infer which specific product attributes (e.g., neckline, sleeve type, fabric) and merchandising factors (e.g., price point, brand) drive consumer demand. They then build generalized machine learning models capable of forecasting demand for new items based solely on these attributes, before any sales history exists.

2. Problem Statement & Challenges

The core problem is the "cold-start" scenario in fashion forecasting: predicting demand for a new item with zero historical sales data. Conventional techniques fail because:

• Non-linear Interactions: Multiple design parameters (color, pattern, cut) interact in complex, non-linear ways to define an item's appeal, making simple extrapolation impossible.
• Reliance on Intuition: Current industry practice often relies on merchandisers' subjective intuition, leading to high variability, inability to account for cross-product effects (substitution, cannibalization), and significant forecast errors.
• Business & Environmental Cost: Inaccurate forecasts result in lost sales opportunities, massive unsold inventory (working capital loss), and environmental damage from overproduction and waste.

The need is for a data-driven, generalizable model that translates item attributes into a reliable demand forecast for a planning horizon of 6-8 months.

3. Methodology & Technical Approach

The authors' methodology pivots from modeling time-series to modeling the semantic space of fashion attributes.

3.1 Data & Attribute Representation

The model is built on a large dataset of historical fashion items, each described by a rich set of categorical and numerical attributes. Key to their approach is the creation of attribute embeddings. Similar to word embeddings in NLP (like Word2Vec), categorical attributes (e.g., "crew neck," "floral print") are transformed into dense, continuous vector representations. This allows the model to learn nuanced relationships and similarities between attributes (e.g., that "v-neck" and "scoop neck" are more similar to each other than to "turtle neck").

3.2 Model Architectures

The paper experiments with multiple neural architectures and traditional ML methods:

• Tree-Based Models (XGBoost, Random Forest): Used as robust baselines, capable of handling tabular data with mixed feature types.
• Feed-Forward Neural Networks (FFNN): Standard multi-layer perceptrons that take concatenated attribute embeddings and numerical features as input.
• Long Short-Term Memory Networks (LSTM): Employed not for temporal sales sequences, but potentially to model sequences of attributes or to capture dependencies in the feature processing pipeline. The paper explores their utility in this non-sequential context.

The core architecture involves an embedding layer for each categorical attribute, whose outputs are combined (e.g., concatenated or pooled) and fed into the subsequent neural network layers for final demand prediction.

3.3 Loss Functions

Choosing the right objective is critical for business impact. The authors experiment beyond standard Mean Squared Error (MSE). They consider asymmetric loss functions that penalize overstocking (predicting too high) and understocking (predicting too low) differently, aligning the model's optimization goal with the actual cost structure of retail inventory management. A simplified form could be:

$L(y, \hat{y}) = \begin{cases} c_{over} \cdot (\hat{y} - y) & \text{if } \hat{y} > y \\ c_{under} \cdot (y - \hat{y}) & \text{if } \hat{y} \leq y \end{cases}$

where $c_{over}$ and $c_{under}$ are the respective costs of over-forecasting and under-forecasting.

4. Experimental Results & Analysis

The paper demonstrates robust performance of the proposed attribute-based models. Key findings likely include (inferred from the abstract):

• Superiority to Baselines: The neural models with attribute embeddings significantly outperform simple historical extrapolation models and potentially traditional ML models on the task of new-item forecasting.
• Generalization Power: The models show an ability to generalize to unseen combinations of attributes, validating the core hypothesis that demand is driven by decomposable attributes.
• Architecture Comparison: The results provide a comparative analysis of FFNNs vs. LSTMs in this setting, likely concluding that while LSTMs are powerful, simpler FFNNs might be sufficient and more efficient for this specific attribute-to-demand mapping problem.
• Loss Function Impact: Models trained with business-aware asymmetric loss functions lead to forecasts that minimize actual inventory costs, not just prediction error.

Chart Description (Inferred): A bar chart would likely show comparison metrics (e.g., Mean Absolute Percentage Error - MAPE, or a custom cost-based metric) for different models: a naive baseline (e.g., average demand for similar categories), tree-based models (XGBoost), FFNN, and LSTM. The neural network models with embeddings would show the lowest error. A second chart might illustrate how forecast error changes with the asymmetry parameter in the custom loss function, showing a clear minimum at a business-optimal setting.

5. Case Study: Framework Application

Scenario: A fast-fashion retailer needs to forecast demand for a new women's summer dress planned for next season.

Step 1 - Attribute Definition: The product team defines its attributes: {Category: Dress, Sub-category: Midi, Neckline: V-neck, Sleeve: Short, Pattern: Floral, Color: Pastel Blue, Material: Cotton, Price Tier: Mid-Range, Brand: In-House Label}.

Step 2 - Feature Vectorization: Each categorical attribute (Neckline, Pattern, etc.) is passed through its pre-trained embedding layer, converting "V-neck" and "Floral" into dense vectors (e.g., [0.2, -0.5, 0.8...]). Numerical features like price are normalized.

Step 3 - Model Inference: All attribute vectors and numerical features are concatenated into a single input vector. This vector is fed into the trained FFNN model.

Step 4 - Demand Prediction: The model outputs a continuous value representing the predicted total units sold in the first season. This forecast is used for production planning and inventory allocation.

Insight: The model might internally recognize that the combination of "Floral," "Pastel Blue," and "Midi" length has been highly successful in the "Mid-Range" price tier during summer, leading to a high-confidence, high-volume forecast.

6. Future Applications & Directions

The approach outlined opens several promising avenues:

• Generative Design & Forecasting Loop: Integrating this predictive model with generative AI (like GANs or Diffusion Models, akin to those used in image synthesis from text) could create a closed-loop system. Designers could input trend mood boards, a generator (inspired by models like CycleGAN for style transfer) produces new attribute combinations, and the forecaster evaluates their commercial potential, enabling AI-assisted design of high-demand items.
• Dynamic Pricing Integration: The model could be extended to a demand function $D(attributes, price)$, allowing for optimal initial pricing and markdown strategies for new items.
• Cross-Domain Adaptation: The core methodology of attribute embedding for cold-start prediction is transferable to other retail verticals with rich product attributes, such as electronics, furniture, or cosmetics.
• Explainable AI (XAI): Future work could focus on interpreting the embedding spaces and model decisions, answering why a certain attribute combination is predicted to be successful, providing valuable feedback to merchandisers.
• Real-Time Trend Incorporation: Augmenting static attributes with real-time signals from social media (e.g., Instagram, Pinterest) or search trends could make the forecasts more responsive to emerging fads.

7. References

1. Singh, P. K., Gupta, Y., Jha, N., & Rajan, A. (2019). Fashion Retail: Forecasting Demand for New Items. In Proceedings of the KDD 2019 Workshop on AI for Fashion.
2. Ferreira, K. J., Lee, B. H. A., & Simchi-Levi, D. (2015). Analytics for an Online Retailer: Demand Forecasting and Price Optimization. Manufacturing & Service Operations Management, 18(1), 69–88.
3. Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space. arXiv preprint arXiv:1301.3781.
4. Zhu, J.-Y., Park, T., Isola, P., & Efros, A. A. (2017). Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks. In Proceedings of the IEEE International Conference on Computer Vision (ICCV). (CycleGAN paper referenced for generative design concept).
5. Academictorrents.com & arXiv.org - as representative open-access academic databases for related work in ML and forecasting.

8. Analyst's Perspective