Fashion retailers require accurate demand forecasts for the next season,
almost a year in advance, for demand management and supply chain planning
purposes. Accurate forecasts are important to ensure retailers' profitability
and to reduce environmental damage caused by disposal of unsold inventory. It
is challenging because most products are new in a season and have short life
cycles, huge sales variations and long lead-times. In this paper, we present a
novel product age based forecast model, where product age refers to the number
of weeks since its launch, and show that it outperforms existing models. We
demonstrate the robust performance of the approach through real world use case
of a multinational fashion retailer having over 300 stores, 35k items and
around 40 categories. The main contributions of this work include unique and
significant feature engineering for product attribute values, accurate demand
forecast 6-12 months in advance and extending our approach to recommend product
launch time for the next season. We use our fashion assortment optimization
model to produce list and quantity of items to be listed in a store for the
next season that maximizes total revenue and satisfies business constraints. We
found a revenue uplift of 41% from our framework in comparison to the
retailer's plan. We also compare our forecast results with the current methods
and show that it outperforms existing models. Our framework leads to better
ordering, inventory planning, assortment planning and overall increase in
profit for the retailer's supply chain.

本文介绍了一种基于产品年龄的预测模型，对于时尚零售商的需求预测、供应链规划和库存管理都具有重要价值，通过实际案例验证了其在 6-12 个月内准确预测销售额的能力，并可以为下一季度的产品推出时间提供建议，此外还提供了产品组合优化模型，最大化收益并满足业务约束条件，相较于现有方法具有更好的预测效果和可操作性。