How fashion retailers are leveraging omnichannel demand planning

Written by: Nicholas Wegman, Senior Director of Artificial Intelligence, antuit.ai – a Zebra Technologies company.

As we continue transitioning towards a post-pandemic economy, mid-tier fashion stands out as one retail sector that is irrevocably changed. It’s clear now that fashion retail will never return to a pre-pandemic sales mix dominated by in-store purchases, nor are we ever going to see the levels of online sales that occurred during the height of the lockdowns.

There remains a considerable segment of consumers that will always prefer the tactile experience of visiting their favourite stores to browse through racks and try on items before purchase. While another consumer segment – particularly Millennials and Gen Z – would avoid malls in favour of purchasing clothes online via websites and mobile apps.

This results in an ever-changing omnichannel landscape that many fashion retailers are struggling to navigate to get the number right for both in-store and online sales. For example, vendor orders, on-hand inventory, distribution centre/store allocation, plus efficient shipping and buy online pick up in store (BOPIS) fulfilment. In addition to these essential capabilities, out-of-stock situations and/or delivery delays frustrate customers, who will quickly turn to competitors for alternatives. While excess or misallocated inventory can quickly eat away at the retailer’s profits.

To tackle these problems, successful fashion retailers are rethinking their approach and introducing the idea of omnichannel demand planning – a seamless, holistic approach to understanding demand and balancing the allocation of inventory across in-store and online channels.

Mastering this cross-channel optimisation requires proper analysis and processing of the available data – store sales, online sales, price and promotions, holidays and events, online traffic, customer demographics and external factors such as weather, among numerous other relevant variables.

Interpreting these data sources and their effects on demand would take a human more time and effort than is reasonable. By contrast, artificial intelligence/machine learning (AI/ML) algorithms have already demonstrated game-changing value to retailers enabling them to keep pace in the highly competitive mid-tier fashion space.

So, what are some specific benefits of an AI-powered solution for an omnichannel fashion retailer?

Augment existing solutions: AI-powered solutions can easily be used to make existing ERP systems such as SAP, JDA and Oracle more intelligent. Once the AI solution ingests all the relevant data, it will generate a unified demand signal that can be used as a single source of truth for omnichannel allocation and replenishment.

Proactive inventory management: Leading-edge AI/ML technology plays a crucial role in eliminating the costly guesswork involved in ordering and allocating fashion products across channels, as well as replenishing everyday staples. By making these decisions using AI/ ML-powered demand, retailers can minimise the risks associated with fashion retail while simultaneously improving profits.

Pricing optimisation & consistency: The omnichannel purchasing habits of customers have complicated traditional pricing strategies. Retailers that have traditionally used strict markdown rules are revaluating them as outdated. As an alternative, they are turning to data science that uses more granular data (real-time sales, online shopping patterns, on-hand inventory etc.) to determine optimal lifecycle pricing for every SKU while also balancing the ability to sell those items at a higher price through the online channel. In short, where omnichannel demand has significantly increased the complexity of pricing, the introduction of AI has effectively brought order to chaos. As such, mid-tier fashion is an underserved segment when it comes to the next generation of AI-powered demand planning, inventory management and lifecycle pricing – and that needs to change. It will be exciting to see the near-term opportunities for mid-tier fashion retailers to capitalise on the recent advancements in AI to build strong brand identities and serve loyal customer bases.