Executive Development Programme in Actionable Supply Chain Data
-- ViewingNowThe Executive Development Programme in Actionable Supply Chain Data is a certificate course designed to empower professionals with the essential skills needed to thrive in today's data-driven supply chain industry. This program emphasizes the importance of data-driven decision-making, providing learners with the tools and techniques to collect, analyze, and interpret supply chain data to drive business growth.
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⢠Data Literacy for Supply Chain Management: Understanding the basics of data literacy, data analytics, and data-driven decision making in the context of supply chain management.
⢠Data Collection and Management: Techniques for collecting, organizing, and managing supply chain data, including data governance and data quality management.
⢠Data Analysis for Supply Chain Optimization: Analyzing supply chain data to identify trends, patterns, and opportunities for improvement, with a focus on using data to optimize supply chain performance.
⢠Supply Chain Visibility and Transparency: Strategies for increasing supply chain visibility and transparency, including the use of data analytics to monitor and manage supply chain risks.
⢠Data-Driven Supply Chain Planning and Forecasting: Using data analytics to improve supply chain planning and forecasting, including demand forecasting, inventory management, and supply chain capacity planning.
⢠Data Security and Privacy in Supply Chain Management: Understanding the risks and challenges associated with data security and privacy in supply chain management, and implementing best practices to protect sensitive data.
⢠Supply Chain Data Integration and Interoperability: Strategies for integrating and interoperating supply chain data across different systems and organizations, including the use of data standards and APIs.
⢠Ethical Considerations in Supply Chain Data Analytics: Exploring the ethical considerations associated with the use of data analytics in supply chain management, including issues related to data privacy, bias, and fairness.
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