Hire Air Freight Software Developers in Basingstoke. 

Cloud computing has become a game-changer for the transportation industry, bringing opportunities such as on-demand service scalability, solid data protection, and significant cost reduction. Global giants understand the value of cloud technologies and are actively planning to move their operations to the cloud. This is one more tech challenge pushing organisations to innovate.

Ramond Coleman, former Senior Manager at Capgemini, says that the cloud is making transportation services more accessible to shippers and carriers and develops a “sandbox” for technology providers who search for building cloud-based supply networks.

In 2020, Uber Freight partnered with Cloud Logistics to scale the capabilities of their shippers. Uber Freight integrated Cloud Logistics Transportation Management System through APIs to enable the options of instant quote viewing, tendering shipments, and more. The integration with cloud-based TMS allowed Uber Freight to respond to market changes much faster than before. Why can’t others do that?

Carriers of all sizes are striving to achieve better control over their fleets, driver locations, and hours they spend driving. Unlike cloud computing, which helps gather data remotely and transmit it in a clear format for tracking your fleet in real-time, edge computing picks, processes, and analyses data at the point nearest to data origin.

New distributed edge computing architecture is focused on dealing with the telematics data within the vehicle cab no matter whether or not it’s located within the network with availability to a data connection. Using edge technology, companies can be sure data is picked and stored precisely and will be synchronised as soon as network and service become available.

Weather tracking fleet locations via edge computing, or freight delivery scheduling via RPA, interconnection of all the shipment participants in a transaction is crucial. This is where AIoT comes in handy. AIoT is an intersection of AI and the Internet of Things that comprises people and devices working together in a single edge network for enhancing AI real-time decision-making for better performance.

Information gathered from the devices participating in freight delivery and processing due to AI technologies is helpful for:

• Preventive maintenance
• Lower costs of energy (due to analyses of optimal fleet operations)
• Infrastructure optimization
• Driver compliance identification
• Fuel management
• Equipment reliability auditing

Deloitte notes that eight startups raised $1.4 billion for self-driving vehicles initiatives in 2020. They developed a scenario planning model to describe how autonomous trucking will cover the freight market.

While speaking about autonomous trucks moving from A to B point, many specialists refer to the transfer hub model as increasing technical feasibility. This autonomous vehicle (AV) transportation is focused on the following concept: deploying drivers in congested urban environments (humans located in the hubs are responsible for local street driving to minimise risks). They drive AVs throughout the city area toward the hubs, and then driverless cars reach the next hub, following a special route.

The benefits of AVs are obvious: decreased human factor influence, minimised risks, reduced administrative running costs; the route will follow an optimum speed level, which eases ETA and ATA metrics balance.

All technologies in freight transportation are interconnected: AIoT catches device data, analyses it to turn into qualitative big data for decision making, and prescriptive shipping utilises big data analyses to predict route metrics variables. They all contribute to the digital transformation of the freight market to make companies stay competitive and profitable.