We have made great strides in mathematics-based supply chain systems over the decades. But we still have many challenges with accurately planning, pricing, and achieving profitability. When there is sudden change, as we have been subjected to repeatedly over the last ten years — from the financial crisis, to social and political unrest, environmental/weather events, and most recently the pandemic — we get left flat-footed. We need, therefore, to broaden our perspective on how to plan and execute in a world of constant change. AI/ML algorithms and the big data they leverage can open the door to new possibilities.
AI/machine learning, data resource management technologies, IoT, cloud platforms, and end-to-end visibility platforms are all part of the technology mix. We are just beginning to learn how to really use many of these technologies and capabilities. Among these technologies, AI/ML tops the list of what users want to hear about. They are learning hat AI and machine learning are not some esoteric algorithmic black box applied to rarefied demand problems, but are used to address day-to-day tasks as well as more strategic analysis.
We have been hearing so much about AI and Machine Learning in the media, but the specifics are vague. Not anymore.Based on our interviews and research into AI and Machine learning algorithms and the various both practical and visionary use cases, we have published our latest report, which you can download here: AI/Machine Learning — How Do We Use It?
This paper is part of a series to explain AI/Machine Learning specifically for the supply chain audience, defining, demystifying, and now getting to the use cases. Stay tuned for a new installment on the future of AI/machine learning and what supply chain teams need to do to successfully implement AI/machine learning.
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