Seasonality in the Supply Chain: How to Manage Inventory When Historical Data Is No Longer Enough?
Managing inventory under seasonal conditions is one of the biggest operational challenges. A faulty demand estimate ends either with an empty shelf (out-of-stock) and a loss of margin, or with capital frozen in goods that no one wants to buy after the deadline. An effective strategy requires understanding that seasonality is not a uniform phenomenon.
The Anatomy of Seasonality: Where Do Fluctuations Come From?
Seasonality refers to repetitive and predictable changes in demand over the course of a year. Its sources can be divided into three main categories:
- Meteorological: Arising directly from the seasons and weather conditions.
- Calendar-based: Linked to fixed dates (Christmas, Valentine's Day) or movable holidays (Easter).
- Incidental: Resulting from one-off events, such as major sporting events or concert tours, which generate local sales peaks.
Forecast Adaptability vs. Demand Retention
When managing seasonal goods, the key is to identify the so-called inflection points. As the season's peak approaches, historical data from previous years loses its relevance. Current meteorological readings come to the fore. Only a precise analysis of the short-term weather window makes it possible to avoid stockouts in the middle of peak demand.
Two types of products must be distinguished:
- Stable-cycle products: e.g. ski equipment. Demand here is relatively stable, as it arises from holidays planned well in advance.
- Interventional products: e.g. snow shovels or insect repellents. Their sales are extremely reactive and almost 100% correlated with sudden atmospheric events.
Product Segmentation and the 'Death Date'
Not all products respond to seasonality in the same way. We distinguish between year-round items, which experience only increased demand at certain times, and strictly seasonal products.
In the case of the latter, the key concept is the 'death date'. This is the moment at which a product loses its place on the shelf. Examples include Christmas-themed napkins or memorial candles. After a specific date, their sales value drops to nearly zero, and every unsold unit becomes a storage cost.
Strategic Inventory Management
Effective planning is based on three scenarios:
- Stockout risk: Real-time replenishment based on current sales dynamics.
- Pre-season build: Building stock in advance to serve the forecast peak.
- Liquidation and buying-in-advance: The end of the season is the time for aggressive stock clearance. On the other hand, for some businesses it is an opportunity for a buying-in-advance strategy — purchasing goods for the following year at clearance prices. This requires a precise calculation of the cost of frozen capital and storage space.
How Does Replee Technology Automate These Processes?
Managing hundreds of products with different seasonal characteristics manually carries a high risk of error. The Replee platform automates these processes through a three-stage approach:
- Identification and clustering: The system recognises the characteristics of individual products and assigns them to appropriate clusters (e.g. interventional vs. stable products).
- Differentiated predictive models: Replee does not rely on a single universal algorithm. It has a set of models that perform best for specific types of variability.
- Intelligent mapping: Each product class is automatically mapped to the appropriate predictive model. This allows the system to know when it should stop looking at last year's sales history and start prioritising current trends and weather data.
Summary
Seasonality is a natural element of trade which, when properly managed, becomes a competitive advantage. The key to success is moving from intuitive stock ordering to an analytical approach based on product segmentation and dynamic predictive models.