According to Gartner, deployment of AI has tripled in the past year, with 37% of companies deploying it in 2019, compared to 25% in 2018. Artificial Intelligence (AI) and Machine Learning (ML) appear to be two of the key technologies for a more resilient supply chain.



Artificial intelligence has got quite a few subfields: Neural Networks, Evolutionary Computation, Vision, Robotics, etc. Today we are going to talk about the sub-domain, Machine Learning.

The Massachusetts Institute of Technology (MIT) defines Machine Learning (ML) as a “subfield of artificial intelligence, which is broadly defined as the capability of a machine to imitate intelligent human behavior.’


Without being explicitly programmed, many systems can now automatically learn and improve from experience. Hence, ML focuses on developing mathematical models that can access data and use it to learn for themselves.


In this world of constant innovation, there is no doubt that technology and Artificial Intelligence will play a significant role in building and mastering supply chain resilience. Also, will develop appropriate cloud platforms that will facilitate accelerated predictions for more sustainable planning.

Using AI to constantly analyze trends and predictions will move from unknown uncertainty to calculated and known variables. If a movement is detected early, it can be reversed and avoid negative impacts.


How can AI and ML assist in resilient Supply Chain planning?

  • Procurement strategy: AI would help control resources better, optimize costs more accurately, and manage suppliers more efficiently.
  • Uncertainty management: Traditionally, supply chains are based on trends and fluctuations in demand. As a result, they are making predictions can be complex. However, AI would provide innovative solutions and simulations that can help companies predict and better understand the effects of these uncertainties.
  • Cognitive analytics:  AI would make it possible for users to identify new and more predictable insights using cognitive analytics. Cognitive analytics is used to naturally detect, analyze, predict, and respond to unexpected disruptions to maximize profits and avoid losses.
  • Probabilistic forecasting: ML techniques to understand forecast variability at the record level. Unlike single-valued forecasts, probabilistic demand simulation establishes a range of possible demand forecasts. 

AI is definitely a key tool for supply chain managers today.  It makes it possible to accelerate decision-making and optimize production, transport, storage, etc. 

There is a wide range of applications for artificial intelligence. Explore other possible fields of application >.



Using AI tools like ML will help companies manage uncertainty and risks more efficiently. It is now clear that AI can strongly contribute to overall supply chain resilience.

By adjusting inventory recommendations and continuously improving forecasts using AI, it is possible to achieve further inventory reduction and reduce the risk of shortages while providing the same level of service.

At Flowlity, we achieved an average 20% reduction of our customers’ inventories by combining advanced machine & deep learning algorithms and statistics. This enables us to establish new scenarios and prevent crises with great precision constantly.


Discover the secrets of this technique by watching the replay of our Webinar >.