AI in supply chain.

Purpose-built AI to give you the insights and signals you need for end-to-end supply chain orchestration.

Intelligent. Not Artificial.

AI in a black box or AI done right? At Kinaxis, we take a principled approach, delivering human-centered AI, expertly combined with the power of concurrency, to drive the most intelligent supply chains on the planet.

5 Principles AI


What is AI?

AI is the science of computers mimicking human intelligence to solve problems. This science encompasses many disciplines to improve speed, precision and elegance in decision-making by finding patterns in enormous volumes of data. It can generate recommendations, predict and surface insights, provide speed and scale, and automate processes, all of which enhance productivity.


Artificial intelligence in supply chain management.

AI in supply chain management is the application of this science to the various processes involved in the supply chain.

By leveraging AI, companies can improve their operational efficiency, reduce costs, increase top-line revenue and enhance customer satisfaction. AI can be applied in different areas of the supply chain, such as demand forecasting, supply planning, inventory management, transportation optimization and order management, making an impact from plan to execution.

In demand forecasting, AI can enhance historical data with market trends and other external factors to predict future demand accurately. Increased forecast accuracy helps companies optimize their inventory levels and avoid stockouts or overstocking.

Keeping supply lead times up to date in a planning system is harder than ever, and the more complicated the bill of materials, the less likely a planner is able to do more than spread outdated assumptions across parts. Not every adjustment is worth a planner’s time to update, but AI can predict lead times, based on historical patterns, and update changes automatically, flagging for intervention only those changes outside parameters the planner sets herself.

A brief history of AI in supply chain.

While the concept of AI has been around for decades, its application in supply chain management is rapidly growing in importance.

1956
Birth of AI
Organized by Marvin Minsky and John McCarthy, the Dartmouth Workshop was a pivotal event that marked the launch of AI as a field. The term was coined to highlight the idea of developing machines capable of intelligent behavior. The workshop was the moment that AI gained its name, its mission, and its major players.
1970s
AI Winter I
With limited results yet proving its value, AI funding declined drastically, ushering in a time known as the First AI Winter. AI programs were hamstrung with limited capabilities mainly due to lack of computing power at the time.
1980s
Rise of expert systems
In the 1980s, a form of AI called "expert systems" was adopted by corporations around the world and became the focus of mainstream AI research. The first expert system to reach the commercial market was known as XCON. It was a production-rule-based system designed to automatically select computer system components based on the customer requirements. Its success showcased AI's potential to assist professionals in complex problem-solving tasks.
1990s
AI Winter II
When it became clear that the innovations being made were not scalable and were much harder to build than expected, enthusiasm for the technology declined again.
2012
ImageNet and Deep Learning set the world on fire
AlexNet, designed by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton at the University of Toronto, revolutionized image classification, long considered the holy grail of AI, using an established but little respected technique, neural networks, applied in a novel way. This team’s entry won the ImageNet competition, beating prior benchmarks by orders of magnitude., This novel approach, which had grown out of collaboration from a lab funded by the Canadian government, marked the beginning of deep learning's dominance as big tech companies rapidly adopted it. The end of the second AI Winter had its deep roots in Canadian soil.
2020
Kinaxis transforms AI for supply chains
Continuing our rich history of pioneering innovation and technology and drawing upon the legacy of AI advancements born in Canada, Kinaxis becomes the only company to take AI beyond standard use cases in supply chain, applying it to solve real-world problems for both supply and demand planning.
2022
Generative AI
Deep learning continues to revolutionize the world as it is a core component behind the introduction of ChatGPT, which experienced the fastest consumer adoption of technology in history after its release in November. While it is the most well-known generative AI tool, this application area skyrocketed, based on its capabilities of language understanding and content generation. Generative AI relies on large language models, which leverage vast amounts of data to generate realistic text, analysis, and communication.

Proven innovators, trailblazing the future of AI in supply chain.

We were the first to have AI-based solutions spanning both supply and demand.  With over 60 patents granted and pending, we are actively advancing artificial intelligence in supply chain management. Our talent continues to lead the way in implementing AI into the world’s supply chains.

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