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Standard seasonality assumptions (quarterly cycle, summer/winter) miss the hidden seasonal patterns specific to individual SKUs, regions, or customer cohorts. A Seasonal Pattern Recognition Playbook reads multi-year demand history to detect non-obvious seasonality and recommend planning adjustments that match the real pattern.
The Challenge
Most planning treats seasonality as Q1-Q4. Real demand often shows 6-week or 10-week cycles that drive material variation within a quarter. Planning at the quarterly level smooths over the underlying pattern and produces stockouts and excess in alternating windows.
Corporate-level seasonality averages over regions with different patterns. A SKU that peaks in May at corporate level may peak in March in region A and July in region B. Without regional decomposition, regional inventory positions chase the wrong calendar.
Some seasonality is driven by recurring events (industry conferences, regulatory deadlines, school calendars) rather than weather. The team knows these patterns exist but rarely captures them as a planning input, so the planning system runs on calendar seasonality alone.
How eyko Solves It
A Seasonal Pattern Recognition Playbook reads multi-year demand history per SKU and region, joins it to event calendars and external signals, and detects recurring seasonality patterns at multiple time scales. It surfaces the SKUs and regions with materially different seasonality from corporate baseline, sizes the planning impact, and recommends seasonality coefficient updates for the forecasting and inventory planning workflows.
The Playbook analyzed 24 months of demand across 4,200 SKUs and 8 regions. 380 SKUs show seasonality patterns materially different from their assigned baseline. 84 SKUs have regional seasonality differences large enough to require region-specific planning. 28 SKUs have event-driven seasonality (industry conferences, regulatory deadlines) not currently captured in the forecasting model. Capturing these patterns projects a 14-point forecast accuracy lift on the affected SKUs.
| Metric | Current | Benchmark | Status |
|---|---|---|---|
| Primary indicator | Flagged | Target | Action needed |
| Secondary indicator | Monitoring | Within range | On track |
| Trend direction | Declining | Stable | Review required |
Seasonal Pattern Recognition detects recurring seasonality patterns per SKU and region at multiple time scales using multi-year demand history, event calendars, and external signals. The Playbook surfaces SKUs and regions with materially different seasonality from corporate baseline, sizes the planning impact, and recommends seasonality coefficient updates for forecasting and inventory planning workflows so the planning system runs on the real pattern rather than corporate assumption.
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FAQ
Everything you need to know about Seasonal Pattern Map.
Seasonal Pattern Recognition is an AI-driven detection of recurring seasonality patterns per SKU and region at multiple time scales using multi-year demand history, event calendars, and external signals. The Playbook surfaces SKUs and regions with materially different seasonality from corporate baseline, sizes the planning impact, and recommends seasonality coefficient updates for forecasting and inventory planning workflows.
The Playbook reads from your ERP or sales system (per-SKU demand history by region, customer cohort), planning system (current seasonality coefficients), event calendars (industry conferences, regulatory deadlines, school calendars), and external signals where applicable (weather data, retail calendars). At least 24 months of paired demand-and-context data anchors the pattern detection.
A quarterly seasonality index assumes one cycle per year aligned with calendar quarters. Seasonal Pattern Recognition detects multi-scale patterns including sub-quarterly cycles (6-week, 10-week), regional divergence from corporate baseline, and event-driven peaks. The two are complementary, but multi-scale detection is what captures the patterns quarterly indices smooth over.
Yes. For each SKU with materially different seasonality the Playbook recommends specific coefficient updates, region-specific calendars where regional divergence is large, and event-trigger additions to the planning system. Each recommendation projects forecast accuracy lift so leadership prioritizes the highest-leverage updates and validates them on backtests before locking them in.
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