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Artificial Intelligence Applications in the Logistics Industry

Artificial Intelligence (AI) is often thought of as ‘self-aware’ technology with the ability to think and act autonomously.

However, within the commercial context, AI is currently accepted as a term to convey a machine capable of performing tasks that would formerly require human intelligence (such as visual perception, speech recognition, decision-making, and language translation).

A fundamental component of AI is machine learning, a term which refers to the ability of a computer to identify patterns in streams of inputs and learn by association. Through this process, a computer can ‘learn’ to distinguish a dog from a cat by filtering a data bank of thousands of categorised images and responding to human corrections to build an association between the data; Big Data is the ‘fuel’ for AI.

As these technologies progress and mature, they will be increasingly bedded within a mutually supportive ecosystem which operates and improves physical and virtual networks, such as supply chains.

3 applications of AI in logistics

1. Last-mile delivery

One area of logistics which will be increasingly influenced by AI is the operation of last-mile delivery systems.

Overall, customers are becoming more demanding. Any company offering a flexible range of delivery options faces an increasingly difficult task in coordinating last-mile flows and this is where the application of AI can dramatically improve delivery services.

Besides optimizing the distribution of shipments, AI can also submit alternatives by crunching customer data; for example, proposing that a customer pick up their consignment from a designated access point, based on geo-location data showing that it will be located on their route home as they commute from work.

By analysing consumer behaviours and location data provided by mobile devices, it is likely that AI will enable companies to become increasingly capable of customizing delivery options for individual customers.

2. Autonomous vehicles

Arguably the most visible manifestation of AI within the e-commerce supply chain is autonomous vehicles.

Although significant progress has been made, the likelihood of full autonomy – or ‘driverless trucks’ – is still some way off.

Current programming techniques employed in AI are unable to provide a computer with the ability to infer potential actions in an unfamiliar situation. Without access to any data relating to a similar situation, none of today’s autonomous systems are able to determine how to respond to extremely low probability events.

Moreover, conceiving of such events in order to simulate an AI response is challenging in and of itself. As such, the physical safety of autonomous vehicles is incredibly difficult to determine because it is essentially impossible to test exhaustively.

As the last-mile delivery of products constitutes the only visible segment of the supply chain for individual consumers, ‘drones’ have also captured the popular imagination and account for a sizeable proportion of the news coverage relating to AI.

3. Warehouse automation

Warehouse automation has already been significantly impacted by AI. The distribution of products in an Amazon warehouse, for example, is not predetermined by category but uses an organic shelving system where products are arranged by the company’s Warehouse Management System (WMS) using algorithms to optimize placement based on picking routes.

Amazon’s 2012 acquisition of Kiva systems allowed it to optimize fulfilment further, by deploying robots to streamline the picking process; bringing the shelves of goods to the human picker, rather than vice versa. This has enhanced the speed of picking operations within the company’s facilities.

Additional companies have exploited machine learning to ‘train’ autonomous guided vehicles (AGVs) to operate in a mixed warehouse setting, alongside humans.


AI is set to significantly increase the efficiency of major organizations.

However, fears over the unintended consequences of AI experimentation are likely to become a recurring topic of debate over the coming years in the media and for politicians.

Within supply chains, more effective allocation of assets in response to demand peaks and troughs will reduce costs.

In addition, the prospects for using AI in a creative manner will allow organizations to solve problems in different ways.

As AI applications become more advanced, this will eventually create a self-correcting supply chain which is adaptable and responsive to changing circumstances. Combined with strategic analysis, this could result in an evolving system that is able to recreate itself to support different requirements.