Upon completion of this course, participants will be able to:

• Define the landscape of supply chain data (structured, unstructured) and its visualization needs.
• Apply fundamental statistical techniques for data description, correlation, and hypothesis testing.
• Master techniques for measuring and interpreting key supply chain metrics (KPIs) and their correlations.
• Utilize data modeling to perform **What-If Analysis** and sensitivity testing for inventory or logistics decisions.
• Develop and present clear, compelling data visualizations and dashboards for executive reporting.
• Identify data integrity issues (cleansing, standardization) and establish data governance standards.
• Apply basic predictive analytics (e.g., regression) to demand forecasting and lead time estimation.
• Use data to diagnose root causes of performance failures (e.g., stockouts, delivery delays).

• Supply Chain Analysts and Data Specialists
• Inventory and Demand Planners
• Logistics and Warehouse Performance Managers
• Procurement Analysts focusing on spend and supplier data
• IT Professionals supporting SCM data warehouses
• Operations Managers who require data-driven insights

• Hands-on practical exercises using industry-standard tools (e.g., Excel/Power BI) for data manipulation and visualization.
• Group activity: developing a complete performance dashboard and presenting the findings to the class.
• Individual exercise: performing a detailed root cause analysis on a series of historical stockout events.
• Workshop on building a simple linear regression model for demand forecasting.
• Discussions on the challenges of obtaining clean, unified data from disparate sources.

Unit 1: Data Foundations and Visualization

• Categorizing SCM data: transactional, master data, external data (market, weather)
• Principles of good data governance, data integrity, and standardization
• The concept of a single source of truth for critical supply chain metrics

• Principles of effective data visualization (clarity, honesty, impact)
• Designing and tailoring performance dashboards for different audiences (operational, executive)

Unit 2: Descriptive and Diagnostic Analytics

• Review of key statistical measures (mean, median, variance, standard deviation)
• Using descriptive statistics to characterize demand variability and lead time distribution

• Conducting **Root Cause Analysis** using techniques like Pareto Charts and fishbone diagrams
• Identifying and interpreting correlation and causality in supply chain data

Unit 3: Predictive Analytics and Forecasting

• Applying linear regression and time-series analysis for basic demand forecasting
• Measuring forecast accuracy (MAD, MAPE) and utilizing forecast bias to improve planning

• Using data to predict future performance of key suppliers and logistics providers
• Modeling the probability of a stockout based on safety stock levels and variability

Unit 4: Prescriptive Analytics and Modeling

• Introduction to basic optimization concepts (linear programming) for logistics (e.g., shortest route)
• Building simple **What-If** models for inventory policy or network design scenarios

• Using data modeling to justify capital investment in automation or IT systems
• Applying analytical results to set objective service level agreements (SLAs)

Unit 5: Tools and Implementation

• Overview of standard analytics tools (SQL, Python/R, Excel, Power BI/Tableau)
• Integrating analytics with Enterprise Resource Planning (ERP) and SCM systems

• Designing a deployment roadmap for a new analytics capability
• The importance of business storytelling when presenting data findings to executives