Does Omnichannel Demand Planning Make Sense for Mid-Tier Fashion Retailers?
Based on the results we’re seeing so far, absolutely. Here are the three key gains over traditional planning methods (derived from real-world experience).
As we continue transitioning toward 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 most likely never return to a pre-COVID sales mix dominated by in-store purchases, nor are we likely 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 favorite stores to browse through racks and try on items before purchase. Meanwhile, other customers—particularly millennials and Gen Z – avoid the local mall in favor of purchasing clothes online via websites and mobile apps.
If you’re a fashion retailer, you’re probably struggling to “get the number right” for both in-store and online sales (e.g., vendor orders, on-hand inventory, DC/store allocation) and trying figure out how to make shipping and BOPIS fulfillment more efficient, right? Out-of-stocks and/or delivery delays frustrate customers who will quickly turn to competitors for alternatives. And, as we’ve discussed in a previous blog post, excess or misallocated inventory can quickly eat away at your profits.
To tackle these problems, many fashion retailers we’re working with are rethinking their approach to planning and embracing the idea of omnichannel demand planning specifically – a seamless, holistic approach of understanding demand and balancing the allocation of inventory across both in-store and online channels.
Mastering this cross-channel optimization requires properly analyzing and processing all the available data – store sales, online sales, price and promotions, holidays and events, online traffic, customer demographics, external factors such as weather, among numerous other relevant variables. Yet, interpreting all these data sources and their effects on demand would take a human more time and effort than is reasonable. That’s why several retailers are leaning into AI/ML algorithms that have already demonstrated game-changing value to other retailers—enabling them to keep pace in an ever more competitive mid-tier fashion space.
What are some of the specific benefits an AI-powered solution can deliver for an omnichannel fashion retailer like yourself?
You can avoid the dreaded “rip and replace” project. AI-powered software solutions can easily be incorporated into existing ERP systems - such as SAP, JDA, and Oracle – to make them more intelligent. Once the AI application has ingested 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. If you’re interested in learning more about how we’ve already set this up successfully for one venerable 300-store fashion retailer in the Southeast U.S., let me know and we can share the details of the project. This augmentation approach was preferred for many reasons, including cost savings, time savings, and the reduction of retraining requirements for the larger team. It didn’t require as much process re-engineering as one might think.
You’ll be able to proactively manage inventory versus constantly trying to “catch up” to customers. Leading-edge AI/ML technology is playing an important 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 forecasts, you can better minimize the risks associated with fashion retail—while simultaneously improving profits.
You’ll finally feel like it’s easy to optimize pricing and maintain consistency in pricing. I know your customers’ omnichannel purchasing habits of late have complicated traditional pricing strategies. Traditionally used strict markdown rules are starting to become outdated, and you’re having to turn 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. And trying to sell those items at a higher price through the online channel is an interesting balancing act, I’m sure. Fortunately, the introduction of AI has effectively brought order to the chaos for many retailers we work with. Happy to share some specific examples if you’re interested, though I will say we’re particularly excited about the near-term opportunities for mid-tier fashion retailers to capitalize on the recent advancements in AI to better help build strong brand identities and serve loyal customer bases. During this period of changing demand patterns, mid-tier retailers are better positioned than their larger competitors to pivot to meet the changing needs of consumers and take full advantage of omnichannel preferences.
While we’re proud of our track record of success for clients across the retail spectrum, we think mid-tier fashion is an underserved segment when it comes to the next generation of AI-powered demand planning, inventory management and lifecycle pricing. For more information on our retail solutions, contact us.
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