ML/AI Techniques to Handle Complex Challenges

An overview of the applications of Machine Learning (ML) and Artificial Intelligence (AI) in supply chain management.
Highlights
🤖 Use cases for demand sensing and inventory optimization
🚧 Challenges of implementing AI in supply chains, such as data fragmentation and lack of access
💡 New AI techniques like large language models (LLMs) and decision chaining
🔮 Future applications of AI in supply chains, including autonomous decision-making
🤔 Level of adoption and impact of AI in supply chains is still evolving, but the potential is significant
Summary: The last webinar in the Summer 2023 Series from the MITx MicroMasters Program in Supply Chain Management (SCM)! Course leads Laura Allegue and Kellen Betts host Bhaskar Ballapragada, Chief Technology Architect at TroughPut Inc.
Interactions between supply chain entities (e.g., suppliers, customers, manufacturers, distribution, warehousing) can result in complex and often unintended consequences making planning for these using traditional techniques challenging. In this session, we will discuss new Machine learning / Artificial Intelligence techniques to handle these complex interactions with use cases of how variations in one or more entities (demand or supply lead times) impact the flow of goods, production planning, and overall supply chain decisions. We will look at examples such as how the seasonality of demand impacts optimization of resources and capacity management, how supplier disruptions can have cascading effects on fulfilling orders, the impact of parts availability on maintenance, and others.
The MITx MicroMasters® Program in Supply Chain Management Website: micromasters.mit.edu/scm/
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That's an internal factor and what's about external factor should take in to account.