Inventory forecasting is the art and science of predicting how much stock you'll need. Get it wrong, and you're either losing sales to stockouts or tying up cash in dead inventory. Here's how to get it right in 2026.
Understanding Demand Patterns
Before you can forecast, you need to understand the patterns in your sales data. Most products exhibit some combination of:
Trend: Long-term growth or decline in sales. A product might be gaining popularity or slowly being replaced by newer alternatives.
Seasonality: Predictable patterns that repeat annually, monthly, or weekly. Think swimwear in summer or gift items before holidays.
Cyclicality: Patterns tied to economic or industry cycles rather than calendar dates. These are harder to predict but important to account for.
Traditional Forecasting Methods
Before diving into AI-powered solutions, let's cover the fundamentals that still form the backbone of most forecasting systems.
Moving Average
The simplest approach: average your sales over the past N periods (weeks or months) to predict the next period. Works well for stable products without strong seasonality. The key is choosing the right N—too short and you'll overreact to noise, too long and you'll miss real changes in demand.
Exponential Smoothing
A step up from moving averages, exponential smoothing gives more weight to recent data while still considering historical patterns. The "triple exponential" variant (Holt-Winters) handles trend and seasonality automatically.
Safety Stock Calculations
No forecast is perfect. Safety stock is your buffer against uncertainty—extra inventory to cover demand variability and lead time variability. The formula involves your service level target (how often you want to be in stock), demand standard deviation, and lead time uncertainty.
AI-Powered Forecasting: What's Different in 2026
Machine learning has transformed inventory forecasting from a statistical exercise into a dynamic, multi-factor prediction system. Here's what modern AI forecasting considers:
External signals: Weather forecasts, economic indicators, competitor pricing, social media trends, and even news events can all influence demand. AI systems can incorporate these signals automatically.
Cross-product relationships: When one product sells well, related products often follow. AI can identify these patterns across your entire catalog.
Promotion effects: How do your sales change when you run a discount? AI learns the specific lift curves for your brand and products.
Implementing Forecasting in Your Operation
Starting with forecasting doesn't require a massive investment. Here's a phased approach:
Phase 1 - Data Foundation: Ensure you're capturing clean sales data, tracking promotions, and recording stockout events. You can't forecast what you don't measure.
Phase 2 - Basic Forecasting: Start with simple methods like moving averages for your top 20% of SKUs (which likely represent 80% of volume). Learn from the errors.
Phase 3 - Automated Reordering: Connect forecasts to purchase order generation. Set review thresholds to catch exceptions.
Phase 4 - AI Enhancement: Layer in machine learning for complex products, promotional planning, and new product launches.
Common Forecasting Pitfalls to Avoid
Even sophisticated systems fail when basic principles are ignored:
- Ignoring stockout data: If you were out of stock for a week, that week's sales don't reflect true demand. Adjust your historical data.
- Forecasting sporadically: Forecasting should be a continuous process, not a quarterly event. Weekly reviews for fast-moving items, monthly for slower ones.
- Not measuring forecast accuracy: Track your Mean Absolute Percentage Error (MAPE) and continuously improve.
- Over-relying on automation: Human judgment is still essential for new products, major market changes, and strategic decisions.
Forecast Smarter with Logentic
Logentic's inventory forecasting module uses AI to predict demand, recommend reorder points, and prevent stockouts before they happen.
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