4 Minutes Read By Dr. Hardy Kremer

Transforming Luxury Fashion Inventory Management with Generative AI

#Artificial Intelligence#Industry Trends#Innovation & Technology#Tech#E-commerce#Fashion & Sports

Artificial intelligence (AI) has become widely recognized as an efficiency and productivity booster across various industries, and luxury fashion is no exception. With the rise of e-commerce and omnichannel shopping behaviors, the need for organizations to establish a strong digital strategy has become paramount.

Dr. Hardy Kremer, Vice President of Data Science & Data Engineering at OMMAX, recently shared his insights on how Generative AI can be used to revolutionize inventory management for luxury fashion brands in a Vogue Business article:

How can generative AI help optimize inventory levels by improving demand forecasting, real-time monitoring, dynamic pricing, and any other methods?

  • Demand Forecasting: Applying AI-driven forecasting to supply chain management can reduce errors by up to 50 percent, resulting in a much more cost-efficient process.

    Luxury fashion faces typical challenges around inventory management,  including external market timing aspects such as fast-changing trends, seasonality, as well as competitor actions and reactions. These challenges are made more difficult by long-lead times. Not acting on these developments risks costly over or understocking. Another challenge is the ability to respond to real-time insights into the supply chain as well as negative customer reactions across the multitude of digital channels that demand attention. All these aspects share the underlying challenge of responding to global developments in real time.
  • Trend Analysis: Generative AI enables luxury fashion brands to analyze vast amounts of data from various sources, such as fashion shows, street styles, social media, and influencers. The newest Gen AI Large Language Models (LLM) can summarise, combine, reason, analyze, and evaluate unstructured text sources to fulfill specific needs. They can use tools (e.g., pricing platforms, calculators, google search) and answer analytical business questions for a given dataset. Given all my inventory data over all locations, by asking “Where am I at risk of over and understocking based on current sales numbers”, AI can transform the answers into specific formats and (API) interfaces. It can act as the perfect glue between humans, IT systems, and other AI technology, such as advanced demand forecasting technology or dynamic pricing algorithms.

    Gen AI allows brands to monitor digital feeds (web, social media) as well as their supply chain logs. It can rapidly structure and digest the data according to business needs, feeding it to consumers and consuming systems for immediate action.

    The biggest opportunity for Gen AI in inventory management is the ability to fulfill real-time needs and substitute manual human process steps (e.g., analysis of supply chain or inventory data) with intelligent automation and connecting systems, substantially speeding up the whole inventory management process.
  • Personalisation and Customisation: Generative AI models can be used to analyze customer data, including purchase history, preferences, and demographics, to offer personalized recommendations and customized products. Luxury fashion brands can leverage generative AI to understand individual customer preferences, create tailored shopping experiences, and optimize their inventory assortment accordingly. As well as improving customer satisfaction, this approach reduces the chances of excess inventory and markdowns.
  • Supply Chain Management: Generative AI can assist luxury fashion brands in optimizing their supply chain operations. By analyzing data related to suppliers, production capacities, lead times, and transportation logistics, generative AI models can help brands make data-driven decisions about sourcing, production planning, and inventory allocation. This enables luxury brands to streamline their supply chain processes, reduce costs, and improve overall operational efficiency.

Where are the limits (currently for generative AI) here, what underlying factors will it not be able to address?

  • Data Limitations: Generative AI heavily relies on data quality and quantity. If historical data is insufficient or biased, the generated forecasts and insights may also be less accurate or biased. Limited or poor-quality data can hinder the effectiveness of generative AI models.

    Gen AI’s most significant limitations namely bias and hallucinations are not especially relevant to inventory management. More relevant challenges are more from a company IP perspective (Do I want to send my internal data to an external cloud API) and in deploying the models to production. Inventory management is a complex domain and building corresponding highly scalable and reliable gen AI systems that interact with other IT systems needs substantial engineering competence.
  • Unpredictable Events: Generative AI models may struggle to accurately predict demand during unprecedented events, such as natural disasters, economic crises, or pandemics. These events can introduce significant disruptions and behavioral changes that may not be adequately captured by historical data. So, in future emergencies, while humans will likely rely on AI to help them make decisions, ultimately it still needs to be humans that make the big decisions.

      By Dr. Hardy Kremer

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