Advances in technology, such as cloud computing and big data, are among the reasons behind the recent surge of interest in artificial intelligence. There have been numerous success stories about application of AI, including in healthcare, finance, transportation and search engines (the last in the form of large language models such as ChatGPT).

Supply chain automation can be traced back to the 1960s, with development of the first computers for tasks such as inventory management and logistics planning. In the 1970s and 1980s, the arrival of decision-support systems and optimization software allowed for more complex planning and optimization. In the 1900s 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, manufactures, logistics and customers. And 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, such as demand forecasting, risk management and predictive maintenance.

During that period, many large corporations became increasingly aware of the potential benefits of AI, leading to a surge 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. In 2017, JD.com used drones to deliver packages to rural areas. In 2018, Walmart used an AI-powered system to predict when products will sell out. In 2018, The Coca-Cola Company 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 can track shipments in real time, predict delays, and optimize routing and scheduling to reduce transportation costs and improve delivery times. In 2020, Nestle installed an AI-powered demand forecasting 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. In 2021, PepsiCo acquired an AI-powered supply chain visibility system that uses machine learning to track shipments in real time and predict potential disruptions. 

With every passing day, AI continues to be integrated into supply chain management systems and processes, with a focus on improving efficiency, reducing costs and increasing sustainability. Most current applications include:

• Demand forecasting, where AI is used to analyze historical data to forecast demand more accurately.
• Route optimization, based on real-time traffic data, weather conditions and other factors, to reduce transportation costs, improve delivery times and enhance customer service.
• Warehouse management, for monitoring inventory, tracking the movement of goods, optimizing layouts and enhancing operational efficiency.
• Supplier management, for identifying and qualifying suppliers, monitoring supplier performance and assessing risks.
• Predictive maintenance, for analyzing sensor data from equipment to predict when maintenance is needed.

While AI has gained a lot of media attention and has recently captured the imagination of the masses through OpenAI, it has been extensively used for supply chain automation and optimization for many years, and its implementation and adaptation continue to increase. While the current hype surrounding AI might suggest that it’s a new trend, it’s important to recognize that the technology has been a part of supply chain management for many years. We may not yet fully understand the potential impact of AI on processes, but one thing is certain: the trend toward AI adoption in supply chain management is only set to grow stronger moving forward.