For many retailers, forecast accuracy has become a proxy for operational health. But in their pursuit of ever greater precision, are retailers fixating on solving the wrong problem, asks Farid Mohsen, VP of Strategic Accounts at invent.ai?
Retail’s forecasting fixation
As a bellwether of business efficiency, forecasting frequently appears in executive dashboards, quarterly reviews and performance conversations, with retailers believing that when forecasting accuracy levels improve, performance will follow. However, this theory rarely plays out, because better forecast accuracy rarely fixes retail performance in isolation; retail performance is not driven by forecasts – it’s driven by decisions.
While a forecast can look statistically strong on paper, the disconnect exists because accuracy is measured in isolation from execution. Even when a retailer sees precision improving across its inventory file, stockouts may persist at store level, distribution centres may continue to carry excess stock, and replenishment could still struggle to keep up with promotions. All of which lead to poor product availability, damaged customer experiences, markdown exposure and, ultimately, the risk of lost sales and margin.
By the same token, aggregated accuracy can frequently hide SKU-store distortions, where one location is consistently overstocked, while another remains with perpetual shelf gaps. So, while the forecasting average appears healthy, the reality of product availability and customer experience tells a very different story on the shop floor.
Retailers therefore need to reframe their thinking; forecasting alone does not move inventory, but the quality of decision-making around allocation, replenishment and buying does.
The silo problem behind the metrics
Part of the foundational issue is organisational rather than technical. Too often, retailers operate in functional silos – from merchandising to supply chain and stores to finance – each viewed through its own lens, and each governed by its own distinct set of KPIs. This means any performance dips trigger targeted adjustments within each silo, rather than a reassessment of overall operational performance.
Inventory management requires retailers to effectively make a financial bet with each adjustment, with every stock movement, allocation or purchase committing capital in expectation of a return, and always at the risk of opportunity cost. Yet most planning processes optimise for local metrics, rather than evaluating the profitability of the decision itself.
This means improving forecast accuracy in one silo rarely fixes the whole, because the underlying decision logic remains fragmented.
Why better forecasts don’t change results
Many retailers fall into the trap of investing heavily in refining models while leaving decision processes largely unchanged. The forecast becomes more precise, yet the operating logic surrounding it remains the same, leading to similar, unchanged outcomes. Across thousands of SKUs, multiple channels and hundreds of locations, small distortions compound into significant financial consequences.
The root of the problem is granularity; when forecasting is performed at aggregated levels, distortions disappear into the summary. Decision-level forecasting instead aligns projections with the point at which inventory is actually committed, often delivering operational improvements before headline accuracy shifts materially.
Activewear brand Alo Yoga, for example, reported higher availability after linking forecasting directly to allocation and replenishment decisioning, improving sales without increasing total stock. This demonstrates that execution changes outcomes more significantly than just focusing on better predictions.
Understanding uncertainty matters more than chasing precision
Traditional forecasting aims to produce a single best estimate, but this rarely squares the circle as retail demand is fluid, uncertain and constantly reshaped by small triggers, from weather and local context to promotions and channel shifts.
Rather than chasing a single number that can’t carry that complexity into a decision, retailers need to realign their efforts. Instead of asking “Is the forecast accurate?”, the question needs to shift to “Is this the right investment of capital?”. And that requires dynamic, intelligent systems that can connect predictions directly to action, allowing allocation, replenishment and buying decisions to continuously respond to demand signals and financial implications.
Multi-agentic decisioning allows retailers to deploy specialised AI agents to evaluate placement, movement and commitment in concert, rather than sequentially, applying the logic of the business – margin, risk and service – at the very point inventory decisions are made.
Measure outcomes, not predictions
The retail industry has spent decades trying to perfect prediction. But the next turning point will be embedding prediction into execution through AI, allowing retailers to evaluate and orchestrate every potential inventory move against its outcome on the total business and profitability.
Retailers no longer need more accurate forecasts – they need forecasts that actively drive unified decisions, replacing siloed optimisation with a single, economically aligned decision engine.
Contributor: Farid Mohsen
Farid Mohsen is a retail technology expert with deep sector expertise, having worked across IT, data and retail for almost 20 years. As VP at invent.ai, he helps global brands and leading retailers leverage AI to optimise inventory, pricing and assortment to grow sales and protect margin.
