Opinion: The First Allocation Dilemma - A High-Stakes Bet in Retail

There’s a moment of truth in retail supply chains that doesn’t get the attention it deserves: the first allocation. That pivotal decision about how much of a newly launched product to send to each store—and how much to keep in reserve at the distribution center—often determines whether the product will succeed or fail, not because of its appeal or pricing, but because it simply didn’t end up in the right place at the right time.

The High Cost of Getting It Wrong

Too much inventory in the wrong store leads to markdowns, cluttered shelves, and lost margin. Too little, especially in a high-performing location, leads to stockouts, missed sales, and frustrated customers who may not give the product (or brand) a second chance. Worse still, initial performance skews downstream demand forecasting, affecting replenishment cycles and promotional planning.

Retailers face this challenge without the luxury of historical data—because by definition, this is a new product. There are no prior sales figures, no seasonality curves, no clean signals. Just risk.

What We Do Have: Analogues and Local Context

While history for the item is unavailable, there is often history for similar items—same category, similar price point, same brand family. Retailers also know how each store performs across categories. This forms the starting point: a triangulation of similar product performance and store-specific demand dynamics.

But even this isn’t straightforward. Two stores may both do well with yoga pants, but one might serve fitness-focused urban professionals, while the other caters to suburban moms looking for athleisure. The same product may move fast in both—but for different reasons, and in different volumes. You can’t simply transpose historical velocity.

Enter AI: Probabilistic Forecasting, Not Crystal Ball Gazing

This is where machine learning earns its keep. Instead of one-size-fits-all rules or gut instinct, AI models can estimate demand probability distributions for new products using proxy data—similar items, historical lift during launches, local demographic indicators, and store-level basket analysis.

AI doesn't predict a single number—it models a range of possible outcomes with confidence intervals. That allows supply chain planners to shift from rigid allocation rules to risk-aware distribution: weighting allocation more heavily toward stores with high upside potential and high sell-through reliability, while using central warehouses as buffers to rapidly redirect based on real-time signals.

This agility is especially critical during a product’s launch window, where the velocity of early sales can signal momentum—or the need to pivot quickly. AI doesn’t remove uncertainty. It helps retailers act under uncertainty intelligently.

Pini Usha, CEO Buffers.ai

The Way Forward: From Guesswork to Framework

First allocation is not just a logistical task—it’s a strategic move. Treating it as such means investing in better data infrastructure, tighter feedback loops between sales and planning, and AI systems that are not black boxes but transparent, explainable collaborators.

It also means shifting the culture—from hero-planners who “just know” their stores, to data-guided teams that learn faster, iterate smarter, and optimize holistically. Retailers that embrace this mindset will see first allocation not as a gamble, but as a lever for growth. From my experience, when you get the right product to the right store in week one, everything else gets easier.

The views expressed in this column are those of the author, Pini Usha CEO of Buffers.ai. Retailers are encouraged to evaluate their specific circumstances and consult with supply chain experts before implementing any AI-based solutions.