Comment: Artificial Intelligence can solve challenge of revolutionising freight

Abhinav Chaudhary, CEO, look at the extent of inefficiency within the logistics industry, and the degree to which it be reduced
Abhinav Chaudhary, CEO,
Abhinav Chaudhary, CEO,


Over the last 5 decades, there has been an exponential rise in the number of shipping vessels, aircraft and road vehicles across the globe. In fact, as this is being read, there are roughly 6000+ ships at sea, 20,000+ commercial cargo aircraft with over a 100,000 flights and over 70 million commercial road vehicles on a daily basis.

With such massive arrangements in the logistics industry, the same question will continue to come up - is the global freight setup between brokers, shippers, carriers, ports, etc. operating at optimal efficiency? What is the extent of inefficiency within the system, and to what degree can it be reduced?

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To tackle the industry optimum issues, offshoring and outsourcing continue to shift with the regulatory and political climates globally. So here’s the question - is it humanly possible to resolve the scale and magnitude of counter-productivity in freight through innovative approaches? Is AI merely a concept or is it finally being put to practice in order to answer challenges faced by the logistics sector?


Some of the easy but pertinent issues in freight are often inquiries such as “Can you take a booking for a truck/container?”, “Where is my shipment?” or “What are the rates?”

NLP Agents have provided immense power to seamlessly resolve the issues of Data Extraction & Machine Translation - processes that are currently handled by simple notifications, IVRS or manual coordination. However, NLP utilizes customized, round-the-clock and accurate data to humanize communications and eliminating visibility issues once and for all, in a manner that is scalable and without any challenges.


The physical movement of good and information processing statuses are updated in the current freight environment through the means of ERP logs, mobile app status buttons and RFID tags. Computer vision allows us to leverage various possible action and object detection scenarios that enable status updates based on actual action events.

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The basic compliance of freight handling is something that is a fairly easy derivative of computer vision capabilities. For instance, the controlling of pilferage actions, DG cargo compliance irregularities, missing cargo and many more. It will no longer be a post-mortem solution to actions but rather the pro-active control of these situations.


The human understanding of purchase sensitivity, online quotation tools, intelligent systems with differential pricing, offline/online bidding tools and so forth, have put across the increased requirement of human resources. In fact, the technologies to-date have resulted in a rise of the shipping rates rather than omission.

Layer mechanisms in Machine Learning is relatively easy to grasp by any individual with an average digital quotient. However, ML approaches shipping rates by predictive analysis, pattern recognition, modular factual factors - all of which are statistical algorithmic approaches rather than an AI approach.

The k-nearest neighbour algorithm is one of the approaches to make ML cluster up various shippers and carriers on the basis of their rates and rankings, specific to lanes or freight expense categories with time mapping across regions. With absolutely no dearth of data as well as super computational speeds that are available, ensuring that the best suited arrangement for the move is very achievable today through the replication of human brains in pricing managers.

On the whole, multidimensional approaches should be taken to augment human actions and decision making in freight, instead of replacing it. It is time that executives in the logistics industry visit drawing board to evaluate whether they will need to implement AI strategies for the years to come and in what manner.

The technology of AI is fundamentally changing the way packages actually move from around the world- through the use of predictive analysis to autonomous vehicles, robotics and voice-assisted programs. As we delve into an era of digitization, the need for AI in the logistics and freight industry will become inevitable.

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