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Red Fabric

THE LATEST HEADLINES (Jan 2024)

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 enhancing 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 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, impacting 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 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is harder than ever, and the more complicated the bill of materials, the less likely a planner can do more than spread outdated assumptions across parts.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.

AI you can understand. AI you can trust.

Explainability and democratization build trustworthiness that fosters adoption when delivered on a foundation of responsible AI. These values are a blueprint for creating AI-powered software that prioritizes people, delivers transparency, and safeguards your data and privacy.

A brief history of AI in the supply chain.

While the concept of AI has been around for decades, its application in supply chain management is 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 behaviour. 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 a 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 worldwide 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 select computer system components based on the customer requirements automatically. 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 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 the supply chain, applying it to solve real-world problems for 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 language understanding and content generation capabilities. Generative AI relies on large language models, which leverage vast amounts of data to generate realistic text, analysis, and communication.

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