03 September, 2026
Thai Airways Partners with CHAMP Cargosystems to Advance Its Cargo Business into the Digital Era with Cargospot neo
On 3 September 2026, Thai Airways International Public Company Limited (THAI) held a ceremony to celebrate its partnership with CHAMP Cargosystems on the implementation of Cargospot neo, an air cargo management system designed to enhance operational efficiency, strengthen revenue management capabilities, and improve the customer experience through digital technology. The event, held at Thai Airways’ Head Office, was attended by Mrs. Varangkana Luerojvong, THAI’s Chief Aviation Business Officer, and Mr. Kittiphong Sansomboon, THAI’s Chief Commercial Officer of Thai Airways, together with Mr. Manuel Galindo, Chief Executive Officer of CHAMP Cargosystems.
Thai Airways signed the agreement for the Cargospot neo project in July 2026. The system is designed to support end-to-end air cargo management, covering sales and cargo reservations, capacity and pricing management, operations, and shipment tracking. By connecting data and workflows across functions, the platform helps reduce siloed processes and enables faster decision-making based on integrated information throughout the cargo lifecycle. It also supports more effective cargo capacity management, creating opportunities to increase revenue per flight while reducing potential revenue loss from suboptimal capacity allocation.
Built on an API-native architecture with advanced automation capabilities, Cargospot neo provides greater end-to-end visibility across cargo operations, supports real-time shipment tracking, and enables sales, revenue management, and operations teams to access relevant information more efficiently. This helps improve data accuracy and strengthens collaboration across functions.
Mrs. Varangkana said: “For THAI Cargo, this project will enhance operational efficiency, accelerate decision-making, and strengthen coordination between our commercial teams and Cargo and Mail Commercial Services. Most importantly, the platform will enable us to deliver greater value, reliability, and an improved experience to our customers.”
Thai Airways has also selected Cargospot neo Revenue Management to further enhance revenue performance and optimize cargo capacity utilization. The solution combines demand and capacity forecasting with dynamic pricing and machine-learning-driven analytics to support decisions on pricing, capacity allocation, and cargo mix in line with demand at different points in time.
Thai Airways signed the agreement for the Cargospot neo project in July 2026. The system is designed to support end-to-end air cargo management, covering sales and cargo reservations, capacity and pricing management, operations, and shipment tracking. By connecting data and workflows across functions, the platform helps reduce siloed processes and enables faster decision-making based on integrated information throughout the cargo lifecycle. It also supports more effective cargo capacity management, creating opportunities to increase revenue per flight while reducing potential revenue loss from suboptimal capacity allocation.
Built on an API-native architecture with advanced automation capabilities, Cargospot neo provides greater end-to-end visibility across cargo operations, supports real-time shipment tracking, and enables sales, revenue management, and operations teams to access relevant information more efficiently. This helps improve data accuracy and strengthens collaboration across functions.
Mrs. Varangkana said: “For THAI Cargo, this project will enhance operational efficiency, accelerate decision-making, and strengthen coordination between our commercial teams and Cargo and Mail Commercial Services. Most importantly, the platform will enable us to deliver greater value, reliability, and an improved experience to our customers.”
Thai Airways has also selected Cargospot neo Revenue Management to further enhance revenue performance and optimize cargo capacity utilization. The solution combines demand and capacity forecasting with dynamic pricing and machine-learning-driven analytics to support decisions on pricing, capacity allocation, and cargo mix in line with demand at different points in time.
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