Anomalies happen in inventory-based businesses. For example, a client might suddenly place an order that’s much larger than usual. But when these peaks are one-off events and they’re integrated into forecasts, they can skew projections for years to come. However, by using AI-powered tools (like StockIQ’s Unusual Sales feature) to identify and isolate these events, demand planners can align sales and operations processes – creating systems that either regularly normalize baselines, or explain outlier events that will likely repeat.
The Gap Between Anomalies & S&OP Processes
Most organizations see sales anomalies. They just might not catch them quickly enough – or know what to do with them.
Here’s what often happens: a demand spike shows up in a report and it gets mentioned in passing. But in the next S&OP meeting, folks move straight into forecasts, supply constraints, and budget rollouts. The anomaly gets often baked into the numbers without explanation, influencing future demand forecasts and inflating anticipated baselines.
In many S&OP processes, anomalies fail to influence decisions for a few predictable reasons. For example, they lack ownership, weren’t normalized, or simply weren’t remembered.
To generate highly accurate forecasts, reduce excess, and still meet demand, teams need to do two things well:
- Detect spikes and anomalies.
- Translate that data into a narrative, and either omit it, or intentionally include it.
This can be done by aligning unusual sales and S&OP processes, using tools powered by artificial intelligence (AI).
How AI-Identified Anomalies Become S&OP Agenda Items
AI-powered inventory management tools are exploding in popularity, with industry research showing that the market is expected to grow by the billions in the next few years. AI in inventory management is very useful for improving demand forecast accuracy, through pattern and anomaly detection. For example, StockIQ’s AI-powered Unusual Sales feature can flag and omit sales anomalies (such as a very large one-time order).
But how can these peaks become agenda items in S&OP?
1. AI flags what’s unusual
AI-powered demand forecasts are continuously scanning demand patterns by item, customer, and time period, immediately flagging sales that fall outside what’s typical. When this happens, human demand planners are able to quickly identify the unusual orders and decide what to do next.
2. Anomalies become a question
Sales anomalies can either be a one-time spike or part of a repeatable pattern. Leaders need to try to figure out why a jump happened – and if it will happen again.
To do so, talk to sales for context. See if they have insight into what drove the large order – such as a one-time promotion that won’t repeat, or a seasonal swing that will likely return next year.
3. If necessary, anomalies are excluded
Once discussed, unusual sales can be separated from “normal” demand signals. This allows planners to create a normalized baseline forecast that reflects repeatable demand, while also preventing in-house friction over numbers.
When AI-identified anomalies drive the S&OP agenda, meetings stop revolving around whose numbers are “right.” They focus on what demand is trying to tell the business, and how to purposefully respond.
What Planners Bring: Normalized Baselines, Risk, and Memory
Within this AI-Ready S&OP process, both planners and sales play a role in improving forecast accuracy. The first thing planners bring to the table is a clean baseline.
By isolating unusual sales from underlying demand, planners ensure the forecast reflects what is repeatable. This normalization step is critical because even a single spike can:
- Artificially inflate future forecasts.
- Drive unnecessary safety stock.
- Lead to crowded warehouses and higher associated costs/risks.
Planners also translate anomalies into risk the business can understand, and quantify the downstream impact. They answer questions such as:
- What happens to service levels if this demand doesn’t repeat?
- How much additional inventory is required if it does?
Perhaps the most underrated contribution planners make is organizational memory. By documenting anomalies as events – complete with notes, decisions, and outcomes – planners improve inventory management processes by ensuring that future forecasts don’t inherit unexplained spikes, team members understand previous behavior, and S&OP discussions are aligned.
What Sales Brings: Field Intelligence That AI Can’t See
AI and demand planners can tell you that demand moved. Sales is the team that usually knows why.
In an AI-ready S&OP, sales brings context that turns unexplained anomalies into informed decisions. For example, when an unusual sale is flagged, sales can often explain it immediately:
- A customer loaded ahead of a price increase or expected trade tariffs (which are impacting more than 80% of supply chains, according to McKinsey).
- A promotion landed better (or earlier) than expected.
- A competitor stocked out.
- A buyer made a one-time commitment tied to an event or contract.
To sales, these details are obvious. To inventory planners, they’re invisible unless surfaced intentionally.
Another area where sales shines: intent. They can answer questions such as:
- Is the promotion going to run again?
- Has the customer committed to ongoing volume?
- Was this a pull-forward that will create a valley later?
When sales brings field intelligence into the S&OP conversations, the result can be more accurate forecasts, alignment between supply and demand, and fewer inventory surprises.
StockIQ: Bridging S&OP, Inventory Analysis, and Unusual Sales
Most legacy S&OP processes simply weren’t built for AI-detected unusual sales. This leaves a gap between the data and what happens with it in your organization.
StockIQ closes that gap by connecting unusual sales detection, inventory analysis, and S&OP decision-making into a single, continuous workflow – before problems ever hit the warehouse. Because StockIQ operates upstream of purchasing and replenishment, it reinforces the core purpose of S&OP: aligning future actions, not explaining past surprises.
Contact us today or request a StockIQ demo to find out how StockIQ can help you not only detect and omit unusual sales, but connect the dots that S&OP depends on to keep your business optimized and profitable.
Frequently Asked Questions About Unusual Sales S&OP Processes
1. Does AI replace demand forecasts – or the people who build them?
No. AI doesn’t replace planners or sales input – it helps them quickly see data trends and patterns. For example, instead of missing an unusually large order, AI will highlight it, so humans can understand context and decide what actions to take.
2. Are unusual sales always “bad data” that should be removed?
Not all – and that’s what makes them tricky. Unusual sales can be:
- One-time events that should be normalized.
- Planned promotions that should be modeled.
- Early signals of real demand shifts.
Don’t just automatically exclude unusual sales. Intentionally decide which events deserve a future footprint in your forecasts.
3. How is this different from just adding notes to a forecast?
Notes without structure don’t scale, and can get lost in the shuffle. On the other hand, AI-identified anomalies:
- Are systematically detected.
- Are tied to specific items, customers, and time periods.
- Trigger discussion before plans are locked.
4. Won’t this slow down S&OP meetings/processes?
It will likely do the opposite. By surfacing anomalies early and narrowing discussion to what actually changed, S&OP meetings & processes can be rooted in clean data, and focused on what steps to take next.