This module develops capability in analyzing forecast accuracy and understanding the factors that influence demand variability. You will examine how external and internal drivers such as promotions, pricing changes, seasonality, and market conditions impact demand patterns and forecast performance. The module introduces causal analysis techniques, including the use of regression, to explain forecast error and improve planning decisions. In addition, you are introduced to more advanced forecasting approaches, including machine learning models, and how these models can be evaluated against baseline methods. The module also emphasizes how forecasting is not only a statistical process but also a decision-making activity that integrates data, business context, and scenario planning. By the end of the module, you will be able to analyze forecast accuracy, identify key drivers of demand variability, and evaluate forecasting approaches to support planning decisions.

This module develops capability in interpreting supply chain performance using key performance indicators (KPIs) and operational metrics. You will examine how performance is measured across supply chain functions, including service levels, inventory efficiency, transportation performance, and operational execution. The module introduces commonly used KPIs such as OTIF (On-Time In-Full), inventory turn, fill rate, dwell time, and carrier performance metrics. It also focuses on how performance data is analyzed to identify issues, prioritize improvements, and support operational decision-making. You will examine how metrics are used to monitor performance and how data-driven insights inform corrective actions. By the end of the module, you will be able to interpret KPI data, identify performance gaps, and use metrics to support supply chain decisions.

This module develops capability in analyzing and visualizing supply chain data using common analytical tools and techniques. You will examine how data is extracted, structured, and analyzed to support supply chain decision-making. The module introduces core analytical tools including Excel for data manipulation, SQL for data extraction, and business intelligence (BI) platforms for visualization and reporting. It emphasizes how data analysis supports performance monitoring, issue identification, and operational insight. You will also explore how dashboards and visualization tools enable drill-down analysis and real-time visibility into supply chain performance. By the end of the module, you will be able to interpret supply chain data using analytical tools, extract relevant information, and use visualizations to support decision-making.

This module develops capability in evaluating supply chain costs, tracking budgets, and analyzing financial performance. You will examine how costs are structured across supply chain operations, including transportation, warehousing, and order fulfillment. The module introduces activity-based costing approaches used to calculate cost per unit, shipment, or distance. It also focuses on how budgets are monitored and how variance analysis is used to identify differences between planned and actual performance. You will examine how financial data is interpreted to identify cost drivers, prioritize corrective actions, and support operational decision-making. By the end of the module, you will be able to interpret cost structures, analyze budget performance, and communicate financial insights in a structured format.

For the final project, you will complete an integrated scenario combining forecasting, KPI analysis, data interpretation, and cost evaluation. Using a structured case, you will: evaluate forecast accuracy and demand drivers, analyze KPI data to identify performance issues, interpret data using analytical tools, assess cost performance and identify variance drivers, and recommend actions to improve supply chain performance.

This module develops capability in interpreting AI-supported forecasting within demand planning processes. You will examine how machine learning models enhance traditional forecasting methods by identifying patterns across large datasets and incorporating external signals such as promotions, pricing, and market changes. The module focuses on how AI-generated forecasts are interpreted, validated, and compared against baseline models. You will examine how forecast outputs must be assessed for accuracy, reliability, and business relevance before being used in planning decisions, including how forecast confidence and variability influence decision-making. By the end of the module, you will be able to interpret AI-generated forecasts, evaluate forecast performance, and apply AI-supported insights within demand planning contexts.

This module develops capability in interpreting AI-supported optimization and decision-making within supply chain operations. You will examine how AI is used to support routing decisions, detect anomalies in operational data, and generate predictive insights. The module focuses on how AI-generated recommendations are evaluated using operational context, performance data, and risk considerations. It also emphasizes evaluation of trade-offs between automation and human oversight in supply chain decision-making, including considerations of accuracy, risk, and accountability. By the end of the module, you will be able to interpret AI-driven recommendations, evaluate their reliability, and apply them to support operational decisions.