AI taking over Supply Chains. Are you ready?

AI taking over Supply Chains. Are you ready? Featured Image

I had a very interesting talk recently with Paul Bradley, CEO of Caprica International. One of the industry leaders best connected with latest technology start-ups. One of the most interesting topics? AI! It made me realize that change is happening faster than most of us anticipate and AI is a big part of it. Listen to the podcast here.

Moving things forward – “Every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. These were the words used in 1955 to launch the very first research project that coined the term ‘artificial intelligence.


In a recent survey of Accenture’s clients, 70% of executives said they are increasing investments in AI compared with two years ago. One of the most obvious places to start is the supply chain, where we could see full automation.

In Gartner’s recent ‘Predicts 2016: Reimagine SCP Capabilities to Survive,’ the research firm revealed that their recent survey had found supply chain organizations expected the level of machine automation in their supply chain processes to double in the next five years

AI projects in Supply Chain and Logistics

Great examples of innovative projects based on artificial intelligence in supply chain:

  • IBM has super machine learning capabilities because of Watson. IBM has recently launched Watson Supply Chain aimed at creating supply chain visibility. Gaining supply risk insights. The system uses cognitive technology to track and predict supply chain disruptions. How you ask? By gathering and correlating external data from disparate sources. For example social media, newsfeeds, weather forecasts and historical data.
  • ToolsGroup’s supply chain optimization software has strong footing in machine-learning technology. One area of application is new product introduction. The software begins with creating a baseline forecast for the new product. The algorithm learns from early sell-in and sell-out demand signals. It layers this output to determine more accurate demand behavior. In turn, this feeds through to optimized inventory levels and replenishment plans.
  • TransVoyant is able to collect and analyze one trillion events each day. They use machine learning. With data from sensors, satellites, radar, video cameras and smartphones. In logistics applications, its algorithm tracks the real-time movement of shipments. It calculates their estimated time of arrival. It factors in the impact of weather conditions, port congestion and natural disasters. Not bad for a smart algorithm.
  • Sentient uses machine learning to deliver purchasing recommendations to e-commerce shoppers. They use image recognition. Rather than only using text searches and attributes like color or brand. The software find visual correlations with the items that the shopper is currently browsing. It does it through visual pattern matching. Visual data to the next level.
  • Rethink Robotics’ collaborative robots use artificial intelligent software. It allows the robot to perceive the environment around it. With this data it behaves in a way that’s safe, smart and collaborative for humans working alongside production lines. Robots will be very handy assistants.

The big players are already working hard on it

The major tech giants, particularly Google, have made it a major priority. They are already creating and developing such vehicles with a low accident rate. The market for self-driving cars and trucks is expected to grow to more than 10m vehicles by 2020. Representing an annual growth rate of 134%. It’s a matter of time before we see them on main roads.

In factories, Siemens is one company that is already using AI and automation . Their lights-out factory has automated some of its production lines. They can run without supervision for weeks at a time. This could work alongside autonomous vehicles to enable manufacturing. The goods can be transported to another AI-run factory. No need for human involvement whatsoever.

Danone, is already using analytics with machine learning capabilities. They use it to analyze their demand planning. Which resulted in a rapid 20% reduction in forecast error and a 30% reduction in lost sales.


Consider these stats from the 2017 MHI Industry report. The speed of supply-chain transactions from one e-tailer on Black Friday:

“A reported 426 orders per second were generated from the website throughout the day. That equates to over 36 million order transactions, an estimated 250 million picking lines at the distribution centers (DC), 40 million DC package loading scans, 40 million inbound sortation hub scans, 40 million outbound sortation hub scans, 40 million inbound regional sortation facility scans and 40 million outbound delivery truck scans.”

How should industry leaders respond? The answer is clear. Supply-chain companies must embed “analysis, data, and reasoning into the decision-making process. Position analytics as a core capability across the entire organization. Strategic planners through line workers, providing insight at the point of action.”




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