The Artificial/Human Intelligence Balance
New technologies push the boundaries of speed, efficiency, and connectivity. The move to the cloud and the rise of edge computing has led to an emphasis on computer learning. By moving processing further from a centralized system, companies must rely on nodes to correctly judge and manage information, which has renewed interest in artificial intelligence (AI). However, the human element is still a vital component in any successful operation. Transportation management systems (TMS) represent the cutting edge of technology, while continuing to place value on skills humans bring to the table.
Smart Technology in TMS
TMS solutions are a prime example of technology bettering an industry. By importing clients, vendors, companies, and data into a system, business is managed easily. Role-specific access to joined dashboards allows high connectivity between every member of the supply chain. Document imaging and integrated accounting provide smooth transactions. Predictive analytics provide valuable insights into large-scale logistics. These elements all use some degree of automation in conjunction with cutting-edge software solutions.
The rise of the internet of things (IoT) is increasingly important in TMS. Smart shipping containers, for example, represent an increase in valuable data flow. In many industries, the IoT has spawned a push toward processing data at outlying nodes – the “things” in the IoT. Known as edge computing, this requires objects in the IoT to possess learning capabilities. Without these abilities, people would need to intervene continuously. The speed gained in edge computing would be lost in the need for management. This draws attention to an already growing move toward artificial intelligence.
The Rise of Artificial Intelligence
Though not always obvious, AI technology is already present in daily life. Facebook’s facial recognition programs are constantly learning. Google’s smart search algorithms predict users’ needs. Traffic signals in some areas use predictive analytics to anticipate congestion. Each is an example of computer learning – and AI.
As the IoT expands and edge computing moves closer to the mainstream, AI is going to become more prevalent across industries. This inspires trepidation in some people, who fear a slew of jobs will be lost to the machine. However, others suggest that companies are not reaching for AI technology but IA – augmented intelligence. This principle suggests a middle ground in which computer learning exists to serve humans performing jobs, not replace them.
The Vital Human Element in TMS
Predictive analytics are a useful part of TMS solutions. However, an experienced human is required to make raw data into a new strategy. Though computer learning is growing by leaps and bounds, pattern recognition is still its foundation. The machine can only use rules people have programed into it. The elasticity of the human intellect is required to accomplish complex problem solving.
These needs are not industry specific. A machine may be able to cut wood, but it cannot feel the grain, decide which piece would be best for a tabletop, and craft a unique piece of furniture. From manual labor to sophisticated smarter cities’ planning, machine learning cannot entirely replace the human brain. Of course, machines are also unable to provide comfort, excellent customer service, or situational humor – they are incapable of being human.
TMS stands as a technological solution that balances the human element with the machine. Providing technology that enhances the user experience and enough automation to ease day-to-day business, it does not strive to remove people from the chain. The supply chain would fall apart without humans reviewing data, forming strategies, making business connections, and solving problems on the fly. People are still the vital ingredient in making transportation management successful.