CDM® certification helps professionals acquire skills in Data Analytics in Supply Chain Management

The CDM® certification exam assumes that you have a close working relationship In the Supply Chain Industry

The American Certified Tourism Professional (CDM)® certification shows future employers that you have the practical knowledge, insights, and professional expertise to understand how Supply chain analytics uses data analytics to manage, improve, and support supply chain operations. Today, supply chains are of critical importance to the development and maintenance of the modern economy, providing not only luxury goods to consumers but also basic necessities like fuel and food. As distribution networks grow, so does the need for data professionals capable of ensuring they run without a hitch. That’s where supply chain managers and analysts come in.

The American Certified Tourism Professional (CDM®) certification provides assessment and tests candidates on how Supply chain analytics uses data analytics methodologies and tools to improve supply chain management, operations, and efficiency. Due to their extensive reach and complex organization, the modern supply chain network produces a wealth of big data, which can be analyzed to understand trends, identify inefficiencies, and develop insightful solutions. Supply chain analysts use analytics to make data-driven decisions surrounding the development, maintenance, and optimization of global distribution networks.

You’ll learn more about supply chain analytics, explore different types that are used every day, and find a list of its benefits. You’ll also learn the principles underlying the digital transformation of supply chains, browse a list of common tools, and encounter some courses that can help get you started on this impactful career today.

Topics covered include

• Forecasting for Supply Chain Planning and Management
• Review of Statistical Foundations: Linear Regression Measuring Demand’s Price Elasticity
• Review of Multiple Regression and Step-wise Selection of Predictive Variables
• Forecasting Demand with Multiple Price Elasticity
• Model Over-fitting and Model Cross-Validation
• Dynamic Regression Forecasting Models