StockTrim handles seasonality and differing lead times at a per-product level, so you can have many SKUs each with their own seasonal pattern and supplier timing in the same account.
StockTrim analyses up to 24 months of sales history to automatically detect seasonal patterns (peaks, troughs, repeating monthly/annual effects) and builds a forecast that reflects those swings over the next 12–24 months. On the demand analysis screen, you can toggle whether seasonality is applied for an item, choose how much history to use (e.g., last 24 months vs last 6), and even set expected growth so seasonal peaks scale up or down in future years.
Each SKU is modeled independently, so products can have completely different seasonal profiles in the same catalogue. For any given item, you can either leave the forecast model on “auto” (AI chooses the best fit and seasonality) or manually set the model and seasonality flag if you know, for example, that one line is strongly winter-biased while another is steady all year.
Lead time is a core input in StockTrim and is configurable per item and even per supplier–item combination. When lead times differ, the system uses each product’s specific lead time to calculate its reorder point, required cover, and order quantities. If a lead time changes (e.g., 4 to 7 weeks), you can override it and the forecasts and order suggestions are recalculated in real time.
Because both seasonality and lead time are SKU-level settings, StockTrim can, for example, treat a highly seasonal summer SKU with a 90-day import lead time very differently from a steady, locally supplied spare part with a 7-day lead time, while still rolling everything into one unified order plan and order schedule. You can also adjust safety stock/service level so that seasonal peaks plus longer lead times are protected by higher buffers where needed, while low-risk items run leaner.
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Q1: How does StockTrim detect seasonal patterns for each SKU?
StockTrim analyses up to 24 months of historical sales data to identify recurring peaks, troughs, and patterns. It then uses this data to generate accurate forecasts that reflect seasonality for each product.
Q2: Can I customize the forecast for seasonal products?
Yes. You can either use StockTrim’s AI-driven “auto” mode or manually set seasonality and forecast parameters per SKU. This allows you to adjust for known seasonal trends or expected growth.
Q3: How does StockTrim handle products with different lead times?
Lead times are configurable per product and per supplier–item combination. StockTrim uses these specific lead times to calculate reorder points, cover, and order quantities, updating forecasts in real time if lead times change.
Q4: Can StockTrim manage products with very different seasonal profiles together?
Absolutely. Each SKU is modelled independently, allowing highly seasonal products and steady year-round items to coexist in one unified order plan without compromising forecast accuracy.
Q5: Does StockTrim consider safety stock for seasonal peaks?
Yes. You can adjust safety stock and service levels so that seasonal peaks and longer lead times are covered by higher buffers, while low-risk items can be managed with leaner stock levels.
Q6: What is the recommended sales history to use for accurate forecasts?
StockTrim allows you to choose from 6–24 months of historical sales. Longer histories give better insights for recurring seasonal trends, while shorter histories can be useful for newer products.