Predictive Analytics for Sustainable Cold-Chain Energy Optimization in U.S. Pharmaceutical Logistics

Temperature-controlled logistics ensure the pharmaceutical supply chain in the United States maintains the quality and safety of the drugs. Cold-chain operations, however, require high energy usage and have a problem with sustainability because the cooling system is not efficient, and there is poor demand forecasting. This paper discusses predictive analytics, which can be used to achieve energy optimization in cold-chain logistics in pharmaceutical warehouses. It combines machine-based learning habits and real-time sensor reports to make a vision of temperature alterations, equipment loads, and paths. The study analyzes the data of the past energy utilization, volume of shipment, and climate ambient data in connection with conclusions on the factors that lead to waste and energy spikes. Regulations like regression analysis, random forests, and time-series prediction are used as predictive algorithms to interpolate the best cooling and transport planning direction. According to the results, there is a potential to cut energy consumption by 15- 25% without affecting drug stability. The environmental impact is also evaluated in the study with the reduction in carbon emissions and cost of operations being noted. The suggested model contributes to the sustainable logistics management and complies with the federal interests in lowering the energy intensity of the supply chains. Predictive analytics can therefore act as a viable solution to a more energy-conscious, stable, and sustainably friendly pharmaceutical logistics in the United States.