Training a machine to automate spot pricing of logistics services in a large-scale network

A computer readable storage medium storing a program of instructions executable by a machine to perform a method of training a machine to automate spot pricing of logistics services, the method comprising:

• receiving a plurality of original-destination routes in a network for transporting cargo;
• clustering the plurality of original-destination routes into a plurality of clusters based on similarities of the original-destination routes;
• clustering the plurality of clusters into a plurality of subgroups based on customer related factors,
• wherein based on a machine learning algorithm, a machine learning model is trained to perform the clustering and the clustering is performed by the trained machine learning model;
• determining influencing factors associated with each of the subgroups;
• based on the influencing factors, generating a price elasticity curve for each of the subgroups;
• based on the price elasticity curve and current network traffic, determining cargo transportation price associated with each of the subgroups,
• wherein the influencing factors comprise at least rate, chargeable weight (CWT), density, weight, volume, leadtime, number of pieces, freighter route, number of stops, customer industry, product code, special handling flag, seasonality indices indicating CWT demand relative to average CWT, revenue demand relative to average revenue and orders demand relative to average orders, shipper capacity, average price quoted to customer in market, average price quoted to customer overall, customer percent (%) revenue through spot market, and customer % CWT through spot market;
• and
• dynamically balancing load in air traffic network in real-time via providing the cargo transportation price.

A system of training a machine to automate spot pricing of logistics services comprising:

• at least one hardware processor;
• a display device coupled to the hardware processor;
• the hardware processor operable to receive a plurality of original-destination routes in a network for transporting cargo;
• the hardware processor further operable to learn to train itself to cluster the plurality of original-destination routes into a plurality of clusters based on similarities of the original-destination routes;
• the hardware processor further operable to learn to train itself to cluster the plurality of clusters into a plurality of subgroups based on customer related factors;
• the hardware processor further operable to determine influencing factors associated with each of the subgroups, and based on the influencing factors, generate a price elasticity curve for each of the subgroups;
• the hardware processor further operable to, based on the price elasticity curve and current network traffic, determine cargo transportation price associated with each of the subgroups;
• the hardware processor further operable to visually display the subgroups in a graphical user interface on the display device,
• wherein the influencing factors comprise at least rate, chargeable weight (CWT), density, weight, volume, leadtime, number of pieces, freighter route, number of stops, customer industry, product code, special handling flag, seasonality indices indicating CWT demand relative to average CWT, revenue demand relative to average revenue and orders demand relative to average orders, shipper capacity, average price quoted to customer in market, average price quoted to customer overall, customer percent (%) revenue through spot market, and customer % CWT through spot market;
• and
• the hardware processor further operable to control dynamically balancing of load in air traffic network in real-time via providing the cargo transportation price.