In inventory-based businesses, unusually large sales can happen unexpectedly, even if a business has accurate historical data and strong forecasts. But how businesses treat these sales can vastly impact forecasts, ordering, and warehousing. Typically, demand spikes are continuously integrated into future forecasting and planning without modification. This can be problematic, and throw off future forecasts for years to come. However, with the right supply chain planning tools, inventory planning leaders can use artificial intelligence (AI) to flag anomalies, either explain them (or exclude them), and ultimately improve service levels.
Is Historical Sales Data Alone Enough for Demand Forecasting?
Imagine this scenario: a few quarters ago, a regular customer placed an unusually large order for a SKU. As your business continued forecasting, it took that figure into account – unaltered and unexplained. Instead of leading to more accurate demand planning, that anomaly ends up skewing forecasts far too high, leading to over-ordering.
Historical sales data is important for demand forecasting and planning. But when you have unusual events (such as very large orders, seemingly out of nowhere), you can’t target on top of that figure for the next year without understanding the underlying factors.
If you are, you’re basically betting on lightning striking twice.
For example, a raw sales history alone doesn’t explain whether a spike came from:
- Customers pulling orders forward ahead of a tariff increase.
- A one-time promotion that won’t repeat.
- Supply shortages that temporarily shifted demand your way.
Yet in most organizations, that context gets lost. The spreadsheet just shows a peak, the ERP faithfully records it, and the forecast quietly assumes it’s repeatable.
This is where sales targets start to drift from reality – when historical anomalies aren’t isolated, they inflate the baseline. This leads to overstocking, crowded warehouses, and higher costs. Industry research shows that, while warehouse and shipping costs dropped slightly in 2025, they have been steadily rising over the years, and in many cases, are still higher than their pre-pandemic levels.
Instead, supply chain leaders are turning to next-gen inventory technology to prevent these inflations, explain anomalies, and include them only when relevant.
How AI Flags Demand Anomalies Before Targets Are Locked In
Most inventory management leaders make several mistakes when it comes to anomalies: they fail to isolate them and understand their source, and allow sales targets to drift from reality. But AI tools like StockIQ’s Unusual Sales feature screens data and allows you to tease out irregular events – before sales targets are set.
First, using pattern recognition and statistical modeling, StockIQ flags demand that deviates from expected behavior, including:
- Short-term spikes that don’t align with historical trends or seasonality.
- Sudden volume jumps followed by fast drop-offs.
- One-time customer behavior that doesn’t repeat.
Then, after the anomaly is detected, users can create an Event within StockIQ, that treats the sale as unusual, and allows you to optimally exclude/normalize it so it doesn’t inflate future forecasts.
The Human Layer: How Planners “Flatten the Peaks”
Artificial intelligence can be powerful in supply chain operations: research from McKinsey shows it can reduce inventory by up to 30%, logistics costs by up to 20%, and procurement spend by up to 15%. But AI is not a catch-all: it’s excellent at spotting what’s unusual. But when it comes to deciding and executing next steps, humans play an irreplaceable role.
Let’s say you notice there’s an unusually large order – AI flags it and allows you to exclude it from the forecast. But you first need to decide if that figure should be excluded at all. Why did the order occur? Is it part of a larger trend, or a one-off?
This is where the importance of human intervention and decision-making starts to become clear. Sales often has visibility into events that AI and demand planners alone don’t have, and might be able to provide insight into why that order happened. Then, your team can decide if it’s something that is likely to occur again, or not.
Humans have insights and context that AI might not intake, or understand. Was the spike driven by a trade tariff scare? Or a promotion that’s going to reoccur every year? Understanding why the spike happened determines whether it belongs in the future – or stays firmly in the past.
Next, planners normalize the history. Instead of letting one-time events artificially raise the baseline, they adjust or isolate those periods so the forecast reflects underlying demand patterns: true trends, real seasonality, and repeatable customer behavior. The peak doesn’t disappear – it just stops distorting everything that comes after it.
Importantly, this isn’t about sandbagging sales or suppressing ambition. Planners aren’t flattening peaks to make targets easier: they’re doing it to make them credible.
The Downstream Impact: Less Inventory, Less Congestion, Less Regret
When anomalies inflate sales targets, the damage might not show up immediately, and instead trickles downstream. On the other hand, when you’re catching, assessing, and addressing anomalies, you can cause a positive chain reaction:
- Forecast accuracy improves, reducing required safety stock.
- Purchase quantities align with true consumption, not hopeful targets.
- Inventory investment drops, freeing up working capital.
- Burn-down periods shorten, instead of dragging on for quarters.
By combining AI-driven anomaly detection with human judgment upstream, organizations can reduce inventory surprises, lower safety stock levels, and improve their service levels. When AI flags the anomalies and planners flatten the peaks, the forecast becomes a realistic starting point for growth.
StockIQ: AI Flags the Noise, Humans Decide What Counts
Sales anomalies and outliers are going to happen. It’s how you approach them that determines your business outcomes: whether your warehouse is overcrowded and excess stock is building up, or you’re keeping a lean amount that meets demand while maximizing profitability.
That’s where AI-driven tools like StockIQ make a difference. StockIQ is a supply chain planning suite that uses sophisticated technologies to help enhance the way you approach demand planning. With our AI-powered demand forecasts, features such as Unusual Sales, and granular controls, you can easily avoid inflated sales targets and drive away bloated inventory.
Contact us today or request a StockIQ demo to learn more.
Frequently Asked Questions About Normalizing Demand Forecasts
1. Why can outlier sales throw off a forecast?
Outlier sales (such as unusually large one-off orders) might represent demand that isn’t repeatable. If they’re treated like normal history, they’ll inflate the baseline. But it’s important to determine if an outlier sale is part of a larger trend, or an isolated event.
2. Is historical data enough for accurate demand forecasting?
Historical data is necessary, but it’s not sufficient on its own. History shows what happened, not why it happened. Without distinguishing between true demand trends and one-off events, forecasts inherit noise from the past.
3. How does AI flag anomalies?
AI analyzes demand patterns at a granular level and looks for behavior that doesn’t match normal trends or seasonality. This includes sudden spikes, short-lived surges, sharp reversals, or demand clustered around known uncertainty events. It also allows you to exclude certain events from your forecasts, so they don’t inflate them.
4. What role do humans play in normalizing demand forecasts?
Humans provide judgment, context, and accountability. Once AI flags anomalies, planners decide what should influence the future and what should remain a one-time exception