AI/ML has created radically new and different capabilities in supply chain solutions. This requires a rethink of the process of achieving results and value from those solutions.
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AI/ML has had a big impact on demand planning, improving forecast granularity and accuracy. It also has revolutionized inventory optimization, which has become ever more critical as companies increasingly push more inventory out to the edges of their distribution network and implemented hyper-local distribution strategies to meet ever more rapid delivery-time expectations.
We explore the use of AI and machine learning in optimizing pricing and promotions to reduce markdowns, increase sales and profit margins, and maximize various other objectives.
Uses of AI and Machine Learning in pricing, promotions, demand planning and forecasting, and inventory management.
AI/ML requires a reimagining of the system development and adoption lifecycle. We discuss the move to a more agile approach, potential use of AI/ML for data cleansing, and the new skillsets and changing roles and responsibilities required.
We examine the potential for AI/ML in enabling an ‘always on’ business model via continuous planning and execution. As well, we look at how AI/ML is a foundation for autonomous supply chains.
A discussion of how AI/ML can help companies become more resilient and deal with change and uncertainty.
It’s 2021! and we want to contemplate the future of technology and how it will change our world.
Oh yes, there are those usual outsized proclamations that AI and machine learning will provide close to $3 trillion of savings in 2021 – that’s right now. Then there is a bleaker view depicting the lack of preparedness for the future. These are interesting points of view, yes, but they don’t provide the guidance we may need to make reality-based decisions to address our challenges and take advantage of practical and competitive gaining innovations.
Our new report, AI/Machine Learning—How Do We Use It?, is based on numerous interviews and research into AI and Machine Learning algorithms, including many practical and visionary use cases.
Supply Chain Networks are evolving to become increasingly autonomous, letting intelligent software agents make simple decisions.
Here we discuss the origins and architecture of the two major types of supply chain networks: 1) Integrator Networks, and 2) Real-time SVoT Networks. As well, we explore the challenges of master data management for a supply chain network.