Data is only as good as the next action it informs. With a forecast, it’s no different. Forecasts shape the everyday decisions that allocators make, yet the value of a forecast is often reduced to acronyms like MAPE, RMSE, or MAE — metrics that matter to data scientists but rarely reflect how allocators actually work.
While these measurements have technical value, retail teams benefit more when the conversation centers on how a forecast improves decision-making. Allocators operate at the level of individual units — specific sizes, colors, and stores. To support these decisions, a forecast must be granular, accurate, and reliable enough that the allocator trusts it. If it can’t help optimize allocations with greater precision and ultimately improve performance, it’s falling short of its potential.
Data that drives action
To understand the business impact of a forecast from an allocator’s perspective, it must be tied directly to the decisions it supports. At its core, allocation comes down to answering two questions:
- How many units will we sell in this store?
This requires information as granular as the level at which decisions are made. - How much safety stock is needed?
A forecast that feels unreliable forces allocators to hedge with additional buffer stock.
These questions point to a broader truth: accuracy matters, but accuracy metrics alone don’t translate directly into confident decisions. A forecast can be technically strong yet practically unhelpful if it lacks the specificity needed to support size-store level choices. A forecast’s value is ultimately measured by how well it supports action. If it does not provide clarity at the unit level, it is not serving its purpose.
3 hallmarks of an actionable allocations forecast
An effective allocation forecast does more than predict demand. It provides the signals that help retailers place the right product in the right place at the right time.
1. Granularity that matches your decisions
Allocators need forecasts that reflect the specificity of their choices. A regional forecast or even a style-level forecast cannot meaningfully inform decisions about whether to send twelve size-small blue shirts to store A and six to store B.
Examining forecast error at the size-store level reveals where misallocations occur — stockouts driven by under-allocation, or excess inventory driven by over-allocation. Granular insights help reduce reliance on safety stock and support more confident decisions.
2. Errors within acceptable limits
Perfect prediction is impossible, but understanding forecast error and its impact is essential. Traditional metrics such as wMAPE, MAE, and RMSE can help quantify performance, but they can also obscure issues that emerge at more granular levels.
A forecast may perform well on aggregate, yet mask significant inaccuracies at the SKU-store level. In practice, revenue is lost only when forecast error exceeds the cushion provided by safety stock. Understanding where and when that occurs is far more valuable than a single accuracy percentage.
3. Direct impact on key performance indicators
Forecasts matter because allocation errors have real costs. Even if the total quantity purchased is correct, misallocations at the store level lead to trapped stock, uneven sell-through, and forced markdowns. Stores may sell out of key sizes while inventory sits idle elsewhere. Shoppers who cannot find the size they want may not return, and loyalty is damaged in the process.
A granular and trustworthy forecast helps minimize these errors, protecting margins, improving availability, and supporting stronger sell-through.
So what is the value of a “better” allocations forecast?
Intuitively, a better forecast leads to better allocation outcomes. But quantifying improvement can be challenging. External research, such as McKinsey’s observed correlations, suggests that improving forecast accuracy by 10–20% can reduce inventory costs by as much as 5% and increase revenue by 2–3%.
The exact impact will vary depending on product mix, operational maturity, and the retailer’s existing forecasting approach. However, the direction is consistent: better forecasts support better financial outcomes.
Retailers also benefit in less visible ways. More reliable forecasts reduce the amount of manual rework allocators must do, improve confidence in system-generated recommendations, and help teams shift from reactive adjustments to strategic decision-making.
Forecasts must be engineered for action
Allocations are just one component of the larger merchandising and planning ecosystem. Retailers need a forecasting approach that supports a wide range of decisions across the lifecycle — from introducing new products, to planning assortments, to optimizing weekly allocations for sell-through and margin.
A single model cannot serve every need, especially when granularity requirements differ. Instead, forecasting approaches must be flexible, context-aware, and capable of delivering insights that match the level of decision-making.
The goal is simple: forecasts that truly inform action.
Want to improve allocation decisions with more accurate, actionable demand signals?
