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AI in Supply Chain and Logistics
Unlocking AI in Supply Chain and Logistics: Professional Staff Learning Path
Designed for Professional Staff to build practical, job-relevant AI skills.
Includes six courses that create a structured and connected learning path.
Serves as a convenient and cost-effective (a savings of $210) way to build essential expertise about AI in logistics.
Unlocking AI in Logistics and Supply Chain: Foundations and Everyday Applications.
Explain what artificial intelligence is, how it differs from related technologies, and how AI systems learn, adapt, and make decisions.
Describe how AI is used in everyday life and across global industries, with emphasis on its current and emerging applications in logistics and supply chain operations.
Evaluate the opportunities, challenges, and organizational impacts of AI adoption—including ethical, workforce, and sustainability considerations.
Assess how AI supports professional growth and organizational strategy, and identify the skills needed to work effectively in an AI-enabled logistics environment.
Demonstrate an understanding of the role of change management and personal adaptability in helping individuals and teams transition to AI-driven workflows.
Unlocking AI in Logistics and Supply Chain: The Evolution of AI in Supply Chain
Explain how AI has evolved within logistics and supply chain management—from rule-based automation in the 1980s to machine learning, intelligent automation, autonomous operations, and generative AI today.
Identify key technological breakthroughs and their impact on modern operations, including forecasting, supplier risk management, procurement automation, warehousing, and transportation optimization.
Describe what makes a supply chain data-driven and recognize the major data sources, integration methods, and analytics tools that enable real-time visibility and smarter decision-making.
Differentiate between descriptive, predictive, and prescriptive analytics and explain how AI transforms data into proactive insights and optimized actions across procurement, warehousing, transportation, and customer fulfillment.
Assess the opportunities and challenges organizations face when adopting AI and data-driven practices, including issues related to data quality, systems integration, organizational readiness, and workforce skills.
Unlocking AI in Logistics and Supply Chain: Working with Advanced AI in Modern Supply Chains
Explain how advanced AI architectures—including digital twins, AI-powered agents, and agentic AI—work together to support end-to-end supply chain decision-making.
Evaluate real-world applications of AI across planning, execution, risk management, and compliance in modern logistics networks.
Differentiate between task-based AI agents and agentic AI systems, and assess where each delivers the greatest operational and strategic value.
Identify the tools, platforms, and data integrations that enable AI to scale effectively across ERP, WMS, TMS, and supply chain ecosystems.
Apply leadership and governance considerations to deploy AI responsibly, balancing automation, human oversight, risk, and organizational readiness.
Unlocking AI in Supply Chain: AI for Customer Service and Sustainability
Explain how AI enhances customer experience in logistics by improving visibility, responsiveness, personalization, and proactive issue resolution through tools such as chatbots, predictive insights, and AI-driven CRM systems.
Analyze how AI supports sustainability and ESG objectives by optimizing transportation, inventory, and operations to reduce emissions, waste, and resource use while improving transparency and compliance.
Evaluate real-world use cases where AI-driven insights deliver both measurable sustainability outcomes and improved customer satisfaction, highlighting the balance between automation and human expertise.
Assess organizational readiness and apply course concepts to identify practical opportunities for responsible AI adoption, considering data quality, system integration, and workforce skills.
Unlocking AI in Logistics and Supply Chain: Ethics, Innovation, and Change
Evaluate responsible AI deployment in logistics and supply chains by analyzing the impacts of data quality, bias, ethical standards, regulatory requirements, cybersecurity risks, and governance frameworks on AI-driven decision-making.
Assess the strategic role of advanced AI technologies in transforming supply chain ecosystems, including autonomous vehicles, drones, robotics, digital twins, and human–AI collaboration models, while identifying associated operational opportunities and systemic risks.
Apply principles of ethical leadership and responsible automation to workforce planning, organizational design, and AI governance, ensuring secure, transparent, and trustworthy AI adoption across enterprise environments.
Lead AI-enabled organizational transformation by implementing effective change management strategies, addressing cultural and structural barriers, engaging the workforce, developing future-ready skills, and sustaining long-term AI-driven innovation.
Unlocking AI in Logistics and Supply Chain: Benefits, Challenges, and Risks of Adoption
Evaluate the strategic benefits and limitations of AI in logistics and supply chains, including impacts on resilience, performance, and decision quality.
Identify organizational, ethical, and governance risks that commonly undermine AI adoption and long-term value creation.
Assess geopolitical, regulatory, and data sovereignty risks affecting AI-enabled global supply chains.
Apply leadership frameworks to guide responsible, resilient, and human-centered AI adoption across complex logistics networks.
