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1. AI and Data Fundamentals for the Digital Supply Chain
1.1. Fundamentals of AI applied to the Supply Chain
- General introduction to AI: history and evolution
- Main concepts and terminology of Artificial Intelligence
- Differences between Machine Learning, Deep Learning and other branches of AI
- Introduction to AI models and their applications in the supply chain
- AI working environment: main tools, languages and platforms
1.2. Practical workshop and initial application
- Implementing a local AI Project
- Teamwork to develop solutions applied to the Supply Chain
- Presentation of results and feedback on the projects carried out
- Identification of key learnings and definition of next steps
- Additional resources and Individual Action Plan
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2. AI in Demand Planning (Forecast to Plan)
2.1. Application of AI in the Forecast to Plan process
- How AI plays a role in improving the demand planning process
- Management and quality of the data needed to apply AI in forecasting
- Automated product portfolio segmentation
- Selecting the AI model to generate the base forecast
- Analysis, validation and confidence building in AI-generated forecasting
2.2. Advanced planning use cases
- Introduction to the business case and configuration of the AI environment
- Demand segmentation using AI techniques
- Base forecast generation with Machine Learning models by segment
- KPI measurement and root-cause analysis of deviations
- From forecast to operational plan
- Presentation and discussion of results
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3. AI in Purchase to Pay and Order to Cash
3.1. AI Models in P2P and O2C
- Application of AI in Purchase to Pay
- Intelligent automation of operational procurement
- Prediction and management of supply risks using AI
- AI applied to the Order to Cash process and financial flow optimization
- International benchmark (Amazon, Alibaba)
- Application of AI in the different stages of the O2C process
3.2. AI optimization use cases
- Practical application in simulated supplier management environment
- Supplier approval and evaluation using predictive models
- Simulating purchasing decisions using AI
- Applications of AI in order management and logistics operations
- Optimizing warehousing and distribution using AI
- Real time visibility and Control Towers for decision-making
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4. Practical Application and Transfer to the Company
4.1. From "what" to "how"
- Make vs. buy: standard tools vs. in-house development
- Role of technology providers and specialized consulting firms
- Data governance and cybersecurity in AI projects
- Organizational impact: new roles, skills and change management
- KPIs and measurement of the value generated by AI in the organization
4.1. Practical strategy and implementation workshop
- Work in groups by sector or type of company
- Designing an AI adoption roadmap
- Identification of quick wins and priority pilot
- Cultural and organizational barrier analysis
- Presentation and strategic discussion of proposals
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