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Machine learning to anticipate cold chain failures
Predictive models that warn in advance of possible thermal ruptures.
Using machine learning algorithms trained with historical data on temperature, humidity, and equipment performance, the system generates predictive alerts up to 2 hours in advance. Fleet managers receive notifications on the mobile app and can reschedule routes or activate backup equipment. Alert accuracy exceeds 92% in field tests conducted on routes of the Buenos Aires–Córdoba corridor.
Up to 2 hours before a possible thermal rupture.
Exceeds 92% on routes of the Buenos Aires–Córdoba corridor.
Alerts on mobile app to reschedule routes or activate backups.
Logistics companies that already trust Chil-D-Care
"The predictive alerts saved us three full loads in the last quarter. The model anticipated failures in refrigeration equipment that even our technicians didn't detect in time."
— Martín L., Logistics Director, Frigoríficos del Sur
"We implemented thermal monitoring in 120 vehicles. Losses from cold chain rupture dropped by 67% in six months. The dashboard is intuitive and alerts arrive before it's too late."
— Carolina G., Quality Manager, Distribuidora Alimentaria Rioplatense
"The automatic reports save us 10 hours of administrative work per week. Now we pass SENASA audits without last-minute preparation."
— Pablo R., Fleet Coordinator, Transportes del Litoral
Companies using Chil-D-Care in their fleets