Our study focuses on how real-world data can inform and enhance firms’ decisions around product design and assortment, which is critical in logistics, automotive, fast fashion and other industries. This article presents a data-driven analytics study on the challenges of new product design and product assortment. We first implement predictive analytics, utilizing a Multinomial Logit (MNL) model to estimate consumer preferences for both existing and newly designed products. Subsequently, we proceed with assortment optimization, including a deterministic model and a robust model. By applying our data-driven method in the case study based on the historical data of a fast fashion e-retailer, we find that the robust assortment model balances revenue and stability, while performing significantly better in the worst-case than the deterministic assortment model. This demonstrates that the robust assortment model, which accounts for parameter uncertainty, may be more suitable for real-world applications. Furthermore, the numerical results indicate that our data-driven new product design and robust assortment approaches can help the firm achieve a 31% expected revenue improvement. Interestingly, our robust assortment methods based on the MNL model outperform machine learning based assortment methods, despite the latter’s more accurate predictive abilities regarding consumer purchasing patterns. These results indicate that accurate predictions of consumer purchasing patterns alone are not sufficient to guarantee good assortment decisions. Firms are advised to adopt the simpler and more comprehensible MNL model as their predictive tool when making assortment decisions.

我们的研究重点是现实世界的数据如何为企业提供有关产品设计和分类的决策信息并增强其决策，这对于物流、汽车、快时尚和其他行业至关重要。本文提出了一项关于新产品设计和产品分类挑战的数据驱动分析研究。我们首先实施预测分析，利用多项 Logit (MNL) 模型来估计消费者对现有产品和新设计产品的偏好。随后，我们进行分类优化，包括确定性模型和鲁棒模型。通过在基于快时尚电子零售商历史数据的案例研究中应用我们的数据驱动方法，我们发现稳健的分类模型平衡了收入和稳定性，同时在最坏情况下的表现明显优于确定性分类模型。这表明考虑参数不确定性的稳健分类模型可能更适合实际应用。此外，数值结果表明，我们的数据驱动的新产品设计和强大的分类方法可以帮助公司实现 31% 的预期收入增长。有趣的是，我们基于 MNL 模型的稳健分类方法优于基于机器学习的分类方法，尽管后者对消费者购买模式的预测能力更准确。这些结果表明，仅对消费者购买模式进行准确预测并不足以保证良好的分类决策。建议企业在做出分类决策时采用更简单、更易于理解的 MNL 模型作为预测工具。