The Advancing Role of Artificial Intelligence in the Logistics Sector

After the widespread disruption of COVID-19 and other impacts, supply chains are big news. It’s no surprise: every product you have in your possession has passed through a supply chain. Like most industries in today’s world, this sector is being impacted by the advancement of technologies such as artificial intelligence (AI).

How AI is successfully being applied in supply chain management

Established organizations are already embracing and utilizing AI and machine learning (ML) to improve their supply chains in a number of ways.

Automated warehouses

Large corporations that have a need for fast and complex logistics solutions, such as Amazon, are investing their own resources to promote innovation in AI and robotics in order to facilitate automated warehousing. Amazon has been investing heavily in warehouse automation for a decade, acquiring companies, hiring top researchers, and funding challenges to find innovative solutions. However, the robots it has deployed so far display limited intelligence and can perform only basic tasks, demonstrating how far even a company with such immense resources still has to go

Another case in point is Ocado, an online-only supermarket in the UK. Their highly automated warehouses are optimizing logistics so effectively that other grocery chains are buying their AI-driven warehousing technology. Ocado’s automated factory employs 3,000 robots controlled by an AI traffic controller. The robots move on a grid containing stacked containers of merchandise, ordered by frequency of use to expedite the picking process. Multiple robots work together to lift crates until the right one is found, whereupon one robot delivers the items to a shelf to be packed by a human. Ocado says its goal is to automate the process completely, from receiving orders to delivery.

Route optimization

Global shipping and logistics company UPS employs a system called On-Road Integrated Optimization and Navigation (ORION), which it says originally reduced driver miles by an average of eight per day. The company recently upgraded the system with dynamic routing, which evaluates changing conditions constantly and feeds optimized route information to drivers, saving a further two-to-four miles on average. Crucially, UPS estimates that every daily driver mile saved can add up to $50 million in savings per year.

Predictive maintenance

Utilizing past and present data, ML algorithms can make predictions and recommendations about vehicle maintenance, increasing fleet lifespan and reducing downtime. Technology company Uptake combines AI and ML to analyze data from Internet of Things devices, GPS, and vehicle logs to predict mechanical failures for vehicles including trucks, planes, and railcars


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