Generative AI in Procurement & SCM Transformation
- Description
- Curriculum
- Reviews

Generative AI in Procurement & SCM Transformation
By fostering cross-functional collaboration, the Bootcamp encourages the sharing of best practices and lessons learned across different domains. while building the necessary skills and play book for successful AI implementation.
Generative AI in Procurement & SCM Transformation Learning Outcomes
Leverage AI to drive innovation ,optimize design and enhance Procurement Supply Chain efficiency
- Integrate AI seamlessly into existing product lifecycle management and SCM systems
- Integrate AI seamlessly into existing product lifecycle management and SCM systems
- Develop strategies for assessing organizational readiness and managing change
- Address ethical considerations and ensure responsible AI implementation
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1What is AI and Generative AI ?
- Overview of AI, machine learning, deep learning
- Generative AI models: What they are and how they work
- Industry and functional applications of generative AI
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2Overview on Procurement & SCM
introduction to the fundamental concepts and practices of procurement and supply chain management..Participants will gain insights into the strategic importance of these functions in modern business operations.
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3Tools ,Models, Data
This course explores the application of artificial intelligence, data analytics, and advanced technologies in procurement and supply chain management. Participants will learn about cutting-edge tools and techniques used to optimize operations and decision-making in modern supply chains.
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4Leading and emerging Tools Capabilities & Use Cases
Leading tools features and capabilities and emerging challengers and Use cases:
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6Applications
Data-driven insights, strategic options generation, scenario planning, and decision-making assistance
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7Use Cases
Predictive analytics for demand forecasting, business intelligence reports, optimizing procurement decisions
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8Hands on Lab
- Participants will use a provided dataset to:
- a) Generate data insights using automated analysis tools
- b) Create multiple strategic options based on the insights
- c) Run scenario simulations to test different strategies d) Use AI-assisted decision-making tools to select the best option
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9Challenges & Limitations
Challenges and Limitations - Dealing with data quality issues and biases - Balancing AI recommendations with human expertise and intuition - Ethical considerations in AI-driven decision-making - Discussion: Potential pitfalls and how to avoid them
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11Advancements in AI & Quantum computing
Advancements in natural language processing and conversational AI - Integration of IoT and edge computing for real-time decision support - The potential of quantum computing in complex decision optimization - Group activity: Brainstorming future applications in procurement and SCM
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12Preparing for the Future: Skills and Organizational Readiness
workforce planning ,skills,training needs projections and cultural tweeks for innovation
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13Assessing organizational readiness for AI driven design process
- By thoroughly assessing the organization's current state, gaps, and change management capabilities, leaders can develop a robust implementation strategy that addresses the unique challenges and opportunities within their design and product development ecosystem for their business.
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14Implementation Levers
key implementation strategies, organizations can ensure a smooth and successful integration of AI into their design and product development processes, leveraging the full potential of this transformative technology.