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Revolutionizing Logistics

Revolutionizing Logistics
How AI has transformed supply chain management from the 1960s to today
By Clinical Assistant Professor of Supply Chain Management Antonios Printezis
Technological advances, such as cloud computing and big data, are among the reasons behind the recent surge of interest in artificial intelligence (AI) technology. While AI has gained considerable media attention and recently captured the imagination of the masses through OpenAI, industries have used it extensively for years for supply chain automation and optimization.
digital illustration of male figure working on microchip
Supply chain automation can be traced back to the 1960s, with the development of the first computers for tasks such as inventory management and logistics planning.
digital illustration of female pointing to charts
In the 1970s and 1980s, the arrival of decision-support systems and optimization software allowed for more complex planning and optimization.
digital illustration of male on computer
In the 1990s and 2000s, the rise of the internet and e-commerce led to the creation of online marketplaces supported by real-time visibility and collaboration between suppliers, manufacturers, logistics, and customers.
digital illustration of male figure standing in front of AI graphics
In the 2010s, we saw the development of AI technologies, such as machine learning, natural language processing, and the Internet of Things, combined with existing technologies such as radio frequency identification and GPS. Together, they enabled more advanced supply chain visibility, prediction, and inventory optimization capabilities, including demand forecasting, risk management, and predictive maintenance.
During the 2010s, many large corporations became increasingly aware of AI’s potential benefits, leading to a rise in investment and adoption of the technology.
  • In 2016, Google DeepMind developed an AI system for Google’s data centers that could reduce energy consumption by 40%.
  • In 2017, JD.com—also known as Jingdong, a pioneering e-commerce platform with supply chain at its core—used drones to deliver packages to rural areas.
  • In 2018, Walmart used an AI-powered system to predict when products would sell out. Coca-Cola implemented an AI-powered supply chain optimization system that uses machine learning to predict demand and optimize production and inventory levels.
  • In 2019, Maersk partnered with IBM to develop an AI-powered shipping platform that tracks shipments in real time, predicts delays, and optimizes routing and scheduling to reduce transportation costs and improve delivery times.
  • In 2020, Nestle installed an AI-powered system that uses machine learning to predict demand.
  • In 2021, Procter & Gamble implemented an AI-powered supply chain optimization system that uses machine learning to optimize inventory levels and improve logistics planning. PepsiCo acquired an AI-powered supply chain visibility system that uses machine learning to track shipments in real time and predict potential disruptions.
During the 2010s, many large corporations became increasingly aware of AI’s potential benefits, leading to a rise in investment and adoption of the technology.
  • In 2016, Google DeepMind developed an AI system for Google’s data centers that could reduce energy consumption by 40%.
Google Deep Mind logo
  • In 2017, JD.com—also known as Jingdong, a pioneering e-commerce platform with supply chain at its core—used drones to deliver packages to rural areas.
JD.com logo
  • In 2018, Walmart used an AI-powered system to predict when products would sell out. Coca-Cola implemented an AI-powered supply chain optimization system that uses machine learning to predict demand and optimize production and inventory levels.
Walmart and Coca Cola logo
  • In 2019, Maersk partnered with IBM to develop an AI-powered shipping platform that tracks shipments in real time, predicts delays, and optimizes routing and scheduling to reduce transportation costs and improve delivery times.
Maersk logo
  • In 2020, Nestle installed an AI-powered system that uses machine learning to predict demand.
Nestle logo
  • In 2021, Procter & Gamble implemented an AI-powered supply chain optimization system that uses machine learning to optimize inventory levels and improve logistics planning. PepsiCo acquired an AI-powered supply chain visibility system that uses machine learning to track shipments in real time and predict potential disruptions.
P&G and Pepsico logo
AI is increasingly being integrated into supply chain management systems to enhance efficiency, reduce costs, and promote sustainability. Current applications primarily focus on:
  • Demand forecasting analyzes historical data to forecast demand more accurately.
icon of globe with two arrows facing the opposite direction
  • Route optimization uses real-time traffic data, weather conditions, and other factors to reduce transportation costs, improve delivery times, and enhance customer service.
icon of moving van with geo tag above it
  • Warehouse management monitors inventory, tracks goods movement, optimizes layouts, and enhances operational efficiency.
icon of forklift
  • Supplier management identifies and qualifies suppliers, monitors supplier performance, and assesses risks.
icon of check list on paper with pen
  • Predictive maintenance analyzes sensor data from equipment to predict when maintenance is needed.
icon of gear with box inside
We may not yet fully understand AI’s potential impact on processes, but one thing is certain: The trend toward AI adoption in supply chain management is only set to grow stronger.
AI is increasingly being integrated into supply chain management systems to enhance efficiency, reduce costs, and promote sustainability. Current applications primarily focus on:
  • Demand forecasting analyzes historical data to forecast demand more accurately.
icon of globe with two arrows facing the opposite direction
  • Route optimization uses real-time traffic data, weather conditions, and other factors to reduce transportation costs, improve delivery times, and enhance customer service.
icon of moving van with geo tag above it
  • Warehouse management monitors inventory, tracks goods movement, optimizes layouts, and enhances operational efficiency.
icon of forklift
  • Supplier management identifies and qualifies suppliers, monitors supplier performance, and assesses risks.
icon of check list on paper with pen
  • Predictive maintenance analyzes sensor data from equipment to predict when maintenance is needed.
icon of gear with box inside
We may not yet fully understand AI’s potential impact on processes, but one thing is certain: The trend toward AI adoption in supply chain management is only set to grow stronger.