Manufacturers and retailers increasingly face the need to factor in external conditions when forecasting demand. Among these, weather stands out — it can drastically alter consumer preferences within a single day. Scorching heat drives sales of ice cream and cold beverages, while rain calms demand for charcoal and barbecue supplies. Weather fluctuations aren’t just background noise; they’re active players that can override even seasonal trends.

Sales data are typically stored in corporate databases and easy to retrieve. Weather conditions, however, are more challenging. For retail chains with stores spread across various regions, weather conditions can differ significantly. Therefore, it’s essential to consider geographical nuances in analysis.

This requires weather data tied not just to regions, but ideally to specific geo-points. Our team regularly uses the OpenWeatherMap service — a handy tool for retrieving both historical and forecasted weather data. Geolocation is embedded in the request parameters: all it takes is specifying the latitude and longitude of the desired point and the time period of interest.

The service’s flexible pricing and high availability make it especially attractive for regular use. We rely on weather indicators such as temperature and categorical variables (e.g., cloud cover, precipitation, wind, etc.).

Table 1. Structure for storing weather forecasts in the database

This table exemplifies storing weather data, including a numerical metric (temperature) and a categorical descriptor (weather type). These are combined with sales data to uncover hidden patterns, allowing more precise demand forecasting aligned with external factors.

To pinpoint products significantly influenced by weather, we use mathematical modeling. The approach assumes that demand depends on several factors: year, month, weekday, discount availability, and temperature.

We use linear regression as the model: Sales = A(year) + B(month) + C(weekday) + D(discount) + E(temperature) + F

Where A, B, C, D, and E are coefficients representing factor impacts, and F is the constant term. Statistical analysis helps assess the significance of temperature’s influence and its reliability.

This model is applied to each “product-store” pair to generate individual evaluations. A sample of results:

Table 2. Weather impact on sales and statistical significance

By filtering for statistical relevance, we identify products whose sales are significantly influenced by weather. However, irrelevant items may also emerge due to overlapping effects between temperature and other dominant factors.

Figure 1 illustrates how demand for ice cream varies with air temperature. The horizontal axis shows temperature (from –10 °C to +30 °C), while the vertical axis shows the number of units sold. The bar chart clearly visualizes how sales rise with increasing temperature.

Short-term forecast accuracy is especially vital for producers of perishable goods. A 7–14 day window is usually sufficient, provided the weather forecast fully covers the target sales period. It’s also crucial to keep data synchronized — updated sales figures, historical data, and weather forecasts should all align within a unified decision-making framework.

Possible approaches to incorporating weather influence:

1. Direct input model — The simplest and most intuitive method. The model ingests numeric and descriptive weather features (e.g., temperature, humidity, precipitation, cloud type) to assess their impact on sales.
2. Adjustment model — Weather data serve as correction coefficients to the primary sales forecast, implemented via regression analysis or a "weather-adjustment" module that tunes the final forecast based on deviations from typical weather conditions.

Thus, weather can be factored into forecasting either directly via climate-driven models or indirectly through correction coefficients. Both improve alignment between demand prediction and consumer behavior.

Analysis shows that weather — especially temperature — can substantially influence consumer behavior and demand for specific product categories. Combining weather data with linear models helps isolate products sensitive to temperature changes and quantify their dependence, backed by statistical significance.

Still, a strong correlation doesn’t always ensure relevance. Temperature effects may coincide with other hidden influences like seasonal promotions, holidays, or local events. Therefore, it’s vital to interpret results holistically, using both models and domain knowledge.

Integrating weather into demand forecasting empowers retailers to manage purchasing, logistics, and inventory more flexibly — particularly for geographically diverse chains. It’s a step toward more accurate, adaptive, and insightful retail strategy.