The modern supply chain faces unprecedented challenges: volatility in demand, global disruptions and the need for maximum operational efficiency. In this context, the Artificial Intelligence (AI) emerges as the key technology for transforming logistics management, allowing for a smarter and more proactive decision-making.
The AI Course for Supply Chain Management addresses the growing demand for professionals capable of implementing AI solutions that optimise every link in the chain, from the demand forecasting up to the inventory management and route optimisation.
Pupils will learn to apply machine learning algorithms for analyse large volumes of data, identify patterns y predict trends.
Cross-cutting
AI Academy
35 hours
AI for Supply Chain Management
AI Academy
35 hours
Introduction
Objectives
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Understanding the role of AI in the supply chain.
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Forecasting demand and optimising stock levels using AI.
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Improving transport efficiency and routes.
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Applying AI to smart warehouse management.
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Identifying and mitigating risks in the chain using AI.
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Developing AI solutions for traceability.
Table of Contents
TEACHING UNIT 1. FUNDAMENTALS OF THE SUPPLY CHAIN AND THE ROLE OF AI
The birth of mass production
The quest for flexibility and global logistics
The emergence of the ‘Big Data’ ecosystem’
AI agents
TEACHING UNIT 2. DATA ANALYSIS AND BIG DATA IN LOGISTICS
Big Data vs. Artificial Intelligence
Optimisation and cost reduction
Data quality: the key to success
Technology ecosystem in data management
Use cases
Current challenges in implementation
TEACHING UNIT 3. DEMAND FORECASTING USING MACHINE LEARNING
Demand forecasting in the supply chain
A new era
Applications and specialisation
Use cases
TEACHING UNIT 4. OPTIMISING INVENTORY AND WAREHOUSES USING AI
Optimising stock and warehouses using AI
Warehouse orchestration: The WMS system
The warehouse of the future
Use cases
Some conclusions
TEACHING UNIT 5. ROUTE AND TRANSPORT OPTIMISATION USING AI
The transformation of logistics through AI, Big Data and the IoT
The start of the planning processes
Algorithms to the rescue
AI and the paradigm shift
Environmental responsibility
Beyond route optimisation
Conclusion: The supply chain as a single flow
TEACHING UNIT 6. RISK DETECTION AND SUPPLY CHAIN RESILIENCE USING AI
AI in logistics: challenges, investment and resilience
The transition to AI
Types of risks in the supply chain
Use cases
Supply chain management in a crisis
The risks involved in adopting AI
Conclusions
TEACHING UNIT 7. AI AND AUTOMATION IN THE SUPPLY CHAIN
The cornerstones of the modern supply chain: AI, data and automation
Technology stack
Information flow
From ERP and WMS to cognitive systems: the evolution of supply chain management
Scalability with cloud computing
Use cases for the digital twin across different industries
Relevant case study: Inditex
Relevant case study: Mercadona – logistical excellence and automation
The democratising role of Software as a Service (SaaS)
Conclusion: technology for the benefit of all
TEACHING UNIT 8. IMPLEMENTATION, ETHICS AND FUTURE TRENDS IN AI IN LOGISTICS
Ethical governance and sustainability in the supply chain
Supply chains: transparency, AI and ethics
The future of AI in logistics: disruptive technologies
Applications in logistics and use cases
Quantum computing in logistics
Future scenarios (beyond 2030)
Cybersecurity in the supply chain: a critical challenge
The digital divide in the supply chain
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