ai logistics

Artificial Intelligence (AI) Applications in Transportation and Logistics: Separating The Wheat from the Chaff

JJ Singh

These days AI is very much in the news and ballyhooed as a technology that will revolutionize business and humankind. The recent firing and return of Sam Altman, Founder and CEO, ChatGPT is worthy of a Hollywood drama serial!

That said, AI will have a major impact on the Transportation and logistics business. According to McKinsey, the successful implementation of AI has already helped early adopter businesses improve logistics costs by 15%, inventory levels by 35%, and service levels by 65%.

Application of AI in business is not new. As an example, for over 50 years, refinery process control engineers have used AI models to optimize and operate in an automated manner to meet high safety severity operations standards in petroleum refineries using mainframe computing and technical coding languages. What is new today is that AI infrastructure and tools are now simplified enough to be available affordably to businesses of all sizes, just like the PC, internet, iPhone, and cloud computing before it. Importantly, optimal application of AI in businesses will accelerate the erosion of strategic advantages derived from “business size” versus small and medium size.

As a recent article in Forbes noted, the human remains at the center of the transportation and logistics process. While AI can provide valuable assistance and support in the freight industry, human expertise and oversight remain crucial." AI is ‘Augmented’ Intelligence when it comes to transportation and logistics, as AI helps humans be more efficient, handle more volume, and ensure quality outcomes. At the end of the day, the person will like their job better, which helps companies with talent attraction and retention. Companies should apply AI to assist transportation and Logistics personnel in their daily operations.

EKA ‘s People-First platform solves real life problems by delivering state-of-the-art AI infrastructure capabilities fully integrated into the platform. This enables its customers – carriers, brokers, and shippers – to optimize customer business workflow automation, decision support, risk management and predictive solutions. A few of the real-life problems that AI can solve are listed below:

Workflow Automation

Transportation and logistics businesses rely heavily on documents to convey information. Think of all the PDFs, email forms and contracts that you interact with on daily basis e.g., load tenders, vendor invoices, bills of lading, etc. There's an enormous amount of data in these files. The problem with this type of data is that it is unstructured or dark data. Dark data is information that businesses collect, process and store during regular activities but generally fail to use for other purposes.

In other words, businesses are sitting on a document goldmine full of data that could be used to automate processes or gather analytics if it could be extracted into a machine-readable format. AML technology has progressed rapidly in the last few years, and it's now possible to use it to read documents of all types, parse the content and extract valuable information from many different document types. This helps remove much of the data entry toil and can reduce document processing time. Not to mention the fact that it enhances the accuracy of data you work with.

Warehouse Automation

A real-life example of AI application in the supply chain sector, is the integration of AI and robotics that has led to significant advancements in warehouse automation. AI-powered robots can efficiently sort, pick, pack and organize inventory, speeding up the order fulfillment process in ~50% of warehouses.

Decision Support

Analysis of historical and current data – carrier/truck availability, driver HOS availability, revenue/costs, carrier/driver service rating, etc., – in real-time can provide “virtual assistant support” to help brokers and dispatchers to make optimal load to carrier and driver/truck/trailer match decisions.

Dynamic pricing is real-time pricing, where the price of a product responds to changes in demand, supply, competition price, and subsidiary product prices. Pricing software mostly uses machine learning algorithms to analyze customers’ historical data in real-time so that it can respond to demand fluctuations faster by adjusting prices.

AI models help businesses to analyze existing routing and track route optimization. Route optimization uses shortest-path algorithms discipline to identify the most efficient route for logistics trucks. Therefore, the business will be able to reduce shipping costs and speed up the shipping process.

Risk Management

Damaged products can lead to unsatisfied customers and churn. Computer vision technology enables businesses to identify damages and ensure quality control in transportation and warehouse operations. Logistics managers can determine the size and type of damage and take action to reduce further damage.

AI models can be developed to extract sufficient insights from fleet accident, reserve, loss run and historical driver behavior incidents data to take meaningful operational actions to proactively reduce magnitude of auto liability and physical damage loss ratio on a trip-miles basis.


One of the most significant contributions of AI in logistics is its powerful application in predictive analytics fueled by explosive growth in data. Combined with an exponentially increasing computing power, larger models capable of doing more complex tasks can be created.

By analyzing historical data and real-time information, AI-powered systems can anticipate demand patterns, inventory fluctuations, and potential disruptions. This enables optimization of inventory levels, minimizes stockouts and streamlines supply chain operations. Predictive analytics will continue to evolve into prescriptive analytics and will ultimately lead to the automation of larger parts of workflows.

Predictive maintenance is predicting potential machine failures in the factory by analyzing real-time data collected from IoT sensors in machines. Machine learning-powered analytics tools enhance predictive analytics and identify patterns in sensor data so that technicians can act before the failure occurs.


The human remains at the center of the transportation and logistics process. Although AI can provide valuable assistance and support in the freight industry, human expertise and oversight remain crucial. AI as ‘Augmented’ Intelligence when it comes to transportation and logistics helps humans be more efficient, handle more volume, do their jobs faster, and have better quality outcomes. At the end of the day, the person will like their job better, which helps companies with talent attraction and retention that are vital. Companies should apply AI to assist transportation and Logistics personnel in their daily operations.

Despite the breakthrough represented by AI, technology alone cannot fix today’s supply chain problems. Digital transformation requires three critical ingredients: the right digital talent, adjusted business models and processes, and the right mix of technology. Also, it requires the ability of its platform provider to be acutely aware and understanding of the key customer operational pain points to be solved, and that comes from listening to their customers. Finally, companies need to change their behaviors from looking in the rear-view mirror to what has happened and start looking forward to using real-time and predictive insights. They need to trust the data that allows them these insights on which they can make instantaneous decisions and then execute them. Only then will real change happen, and only then can digital transformation be achieved.

EKA Platform delivers AI technology infrastructure coupled with deep domain knowledge and operating experience, and an “uncanny ability” to make complex, simple. Equally importantly, its work ethos powers it to fluidly work with customers to configure and solve their key management and operational pain points.

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JJ Singh, CEO, EKA Solutions, Inc.

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