Optimizing Cold Food Supply Chains for Enhanced Food Availability Under Climate Variability.

Area of Science:

• Agricultural Economics
• Operations Research
• Climate Science

Background:

• Produce supply chains are vital for delivering fresh food efficiently and affordably.
• Hub-and-spoke models optimize cold food supply chains, but traditional methods ignore climate variability's impact on crop yields.
• Climate change, including CO2 rise and extreme weather, significantly affects crop productivity and availability, leading to increased costs for distributors.

Purpose of the Study:

• To develop a stochastic hub-and-spoke network optimization model that minimizes transportation expenses.
• To explicitly incorporate climate variability's effects on crop yields into supply chain route optimization.
• To enhance decision-making for climate-resilient produce supply chains.

Main Methods:

• Developed a stochastic hub-and-spoke network optimization model.
• Utilized climate models and real soil data for California to create various weather scenarios.
• Applied the model to a cold food supply chain (CFSC) for strawberries, a key California crop.

Main Results:

• Increased rainfall scenarios led to higher yields, enabling cost reductions through sourcing from nearby farms.
• Reduced rainfall scenarios resulted in lower yields, necessitating sourcing from distant locations and increasing transportation costs.
• Supply chain configurations are sensitive to the choice of climate models and require regular updates.

Conclusions:

• The stochastic model effectively minimizes transportation costs by optimizing routes based on climate-influenced yields.
• The research provides a tool for planning climate-resilient supply chains, improving preparedness for climate-related disruptions.
• Regularly updating climate scenario inputs is crucial for robust and adaptive supply chain planning.