Weather in Retail: Risk or Opportunity?
ClimatePulse can help prevent a 10–15% reduction in yield losses by providing early weather alerts that enable farmers to adjust operations during extreme climate conditions.
Dairy farming and perishable goods industries in Bangladesh are highly sensitive to extreme weather events, particularly heat stress, which can reduce milk yield, disrupt fertility, and cause spoilage during transport and storage. For example, dairy farms have recorded a 24.4% drop in milk production during summer months due to elevated Temperature-Humidity Index (THI) levels. ClimatePulse helps producers manage these challenges by using historical weather data and forecasting tools to predict high-risk periods. This allows for timely adjustments in production, logistics, and inventory—ensuring continuity, quality, and reduced waste.
Example: ClimatePulse can provide historical heat-stress data to help companies like Milk Vita forecast reduced summer milk yields and adjust inventory and logistics in advance, preventing spoilage and losses. Similarly, companies like Igloo may use ClimatePulse’s heat-spike alerts to scale up ice production ahead of heatwaves, avoiding stockouts and achieving up to a 15% increase in sales during peak summer months.

Use Cases:

Climate-Aware Production Planning: Predictive weather data enables producers to adjust schedules and operations ahead of high-risk periods. In pilot programs, farms using AI-driven alerts reported a 10–15% reduction in milk yield losses during peak heat periods.

Animal Health & Fertility Monitoring: Forecasting extreme heat enables timely strategies to reduce climate stress on livestock. In Bangladesh, only 39.6% of dairy cows are lactating, far below the 60% profitability threshold, reflecting a ~34% productivity gap observed across climate-impacted regions. Heat stress in coastal areas is also associated with extended calving intervals exceeding 500 days.

Inventory & Demand Forecasting: Integrated weather-inventory models help businesses manage stock levels more efficiently during seasonal extremes. Retailers using weather-driven demand forecasts reduced fish spoilage from 8% to 5%, while processors avoided both overstocking and under-collection of milk.