How accurate is the forecast for seasonal products?

For seasonal products with at least 2 years of history, the forecast achieves 85–88 % accuracy. The model accounts for Christmas, Black Friday, Easter peaks, and other seasonal patterns automatically.

Does forecasting work for new products with no history?

For new products, AI draws on similar items (same category, price point, target segment) and external trends. Accuracy is lower (70–75 %) but improves with every week of sales data.

Will the system account for planned marketing campaigns?

Yes — you can enter planned promotions, discounts, and seasonal campaigns. AI adjusts the forecast based on the historical uplift from similar past campaigns.

AI Demand Forecasting: Order What You’ll Actually Sell

For e-commerce businesses, trading companies, and distributors, the right inventory level is critical. Too little = stockout, frustrated customers, lost revenue. Too much = tied-up capital, expiry waste, storage costs.

The Problem: Gut Feeling vs. Data

Most small and medium-sized businesses purchase stock “by feel” or based on the buyer’s experience. The result:

Bestsellers sell out exactly when customers are looking for them
Dead stock accounts for 15–25 % of inventory value
Expiry losses in food, cosmetics, and pharmaceuticals
Over-ordering before the season, liquidation sales afterwards

How Modulario AI Forecasts Demand

Input Data

AI processes:

Historical sales per item (minimum 3 months, ideally 2 years)
Seasonal indices (Christmas, Black Friday, Valentine’s Day, back-to-school)
Category trends (growing, stable, declining)
Current stock levels and orders in transit
Planned marketing activities and discounts
For connected e-shops: product page traffic and wishlist data

Output: Weekly Forecast per SKU

For each stock-keeping unit you receive:

Item: Arabica Coffee 250g (SKU-4821)
Forecast for next 4 weeks: 145 / 162 / 138 / 155 units
Recommended order: 520 units (7-day safety stock)
Preferred supplier: CaféDistrib Ltd.
Confidence: 89 %

Automatic Purchase Recommendations

Based on the forecast and current stock levels, the system generates purchase recommendations:

Items to order now (below reorder point)
Items to order within 7 days (approaching reorder point)
Items to reduce or not reorder (declining trend)

Results in Numbers

Metric	Before AI Forecasting	After AI Forecasting
Stockout rate	8–12 %	2–4 %
Dead stock (% of inventory value)	18–22 %	8–12 %
Inventory turnover	6x/year	9–11x/year
Buyer’s analysis time	15 h/month	3 h/month

Who Benefits Most from AI Demand Forecasting

E-commerce with 200+ SKUs and seasonal fluctuations
Wholesalers and distributors with large stock holdings
Food and beverage with expiry dates
Pharmacies and drugstores with strict expiry compliance
Manufacturers planning production based on customer orders

AI Demand Forecasting — Know What You'll Sell Next Month