Supply Chain Analytics Course

Lesson 1 • Analytics Maturity in Supply Chains

Introduces the descriptive-diagnostic-predictive-prescriptive maturity model. Helps practitioners benchmark their organization and set realistic improvement targets.

Lesson 2 • Supply Chain Structure and Data Landscape

Maps the end-to-end supply chain and identifies data generated at each node. Establishes the information architecture that all subsequent analytics work depends on.

Lesson 3 • Data Quality and Governance Basics

Covers dimensions of data quality—accuracy, completeness, timeliness—and introduces governance policies that ensure reliable analytics outputs. Prevents downstream errors in all analytical models.

Lesson 1 • Data Cleaning and Preparation

Addresses missing values, duplicates, outliers, and inconsistent formats that degrade supply chain datasets. Students apply cleaning techniques using spreadsheet and scripting tools.

Lesson 2 • Data Extraction and ETL Processes

Teaches extract-transform-load workflows for moving data from source systems to analytical environments. Covers scheduling, error handling, and incremental load strategies.

Lesson 3 • Introduction to Analytical Environments

Orients students to spreadsheet, SQL, and Python/R environments used throughout the course. Establishes a consistent toolset for all subsequent analytical exercises.

Lesson 4 • Enterprise Data Sources and Systems

Surveys ERP, WMS, TMS, and supplier portals as primary data sources. Explains how each system captures transactional data relevant to supply chain analytics.

Lesson 1 • Communicating Insights to Stakeholders

Develops the skill of translating analytical findings into concise, action-oriented narratives for operations and leadership teams. Bridges the gap between data output and business decision.

Lesson 2 • Exploratory Data Analysis Techniques

Introduces EDA methods—histograms, box plots, scatter plots, and correlation matrices—to uncover patterns before modeling. Guides hypothesis generation about supply chain behavior.

Lesson 3 • Visualization Tools and Implementation

Provides hands-on practice with BI tools to build live-connected supply chain dashboards. Covers data source connection, calculated fields, and publishing workflows.

Lesson 4 • Descriptive Statistics for Supply Chain

Covers measures of central tendency, dispersion, and distribution shape applied to lead times, order quantities, and costs. Builds statistical intuition needed for all advanced analytical methods.

Lesson 1 • Demand Patterns and Segmentation

Classifies demand into smooth, intermittent, lumpy, and erratic patterns using statistical criteria. Segmentation determines which forecasting method is appropriate for each SKU.

Lesson 2 • Forecast Accuracy and Bias Management

Establishes a forecast performance measurement system and identifies systematic bias sources. Students implement bias correction routines and continuous improvement review cycles.

Lesson 3 • Machine Learning Forecasting Models

Introduces gradient boosting, random forests, and LSTM networks for demand forecasting at scale. Compares ML accuracy and interpretability against classical statistical methods.

Lesson 4 • Time-Series Forecasting Methods

Covers moving averages, exponential smoothing, and Holt-Winters models for trend and seasonal demand. Students calibrate smoothing parameters and evaluate fit using error metrics.

Lesson 1 • Deterministic Inventory Models

Covers the economic order quantity model and its extensions for quantity discounts and production runs. Provides the analytical baseline before introducing stochastic demand.

Lesson 2 • Safety Stock and Reorder Point Calculation

Calculates safety stock using demand and lead-time variability under target service level constraints. Connects directly to the forecasting error metrics developed in the previous chapter.

Lesson 3 • Multi-Echelon Inventory Optimization

Extends single-location models to distribution networks with multiple stocking tiers. Students allocate safety stock across echelons to minimize total network inventory cost.

Lesson 4 • Inventory Policy Simulation and Testing

Uses Monte Carlo simulation to stress-test inventory policies under demand and supply uncertainty. Validates analytical model outputs before live implementation.

Lesson 1 • Supplier Risk Assessment and Monitoring

Quantifies financial, geographic, and operational risks across the supplier base using scoring models. Supports proactive risk mitigation before disruptions affect production.

Lesson 2 • Supplier Performance Measurement

Designs a balanced supplier scorecard covering quality, delivery, cost, and responsiveness dimensions. Enables objective supplier comparison and performance-based contract management.

Lesson 3 • Spend Analysis and Category Intelligence

Cleanses and classifies procurement spend data to reveal category concentration, maverick buying, and savings opportunities. Provides the factual foundation for strategic sourcing decisions.

Lesson 4 • Sourcing Optimization Models

Applies linear programming to allocate purchase volumes across suppliers under cost, capacity, and risk constraints. Students solve multi-supplier sourcing problems with real procurement data.

Lesson 1 • Route Optimization Techniques

Covers vehicle routing problem formulations and heuristic solution methods for last-mile and inbound logistics. Students apply optimization algorithms to reduce mileage and delivery time.

Lesson 2 • Transportation Cost and Mode Analysis

Analyzes freight spend by mode, lane, and carrier to identify cost reduction and consolidation opportunities. Builds the spend visibility needed for carrier negotiation and mode optimization.

Lesson 3 • Carrier Performance Analytics

Measures carrier on-time performance, damage rates, and cost compliance using TMS and invoice data. Supports data-driven carrier selection, scorecarding, and contract enforcement.

Lesson 4 • Last-Mile and Delivery Analytics

Analyzes last-mile cost drivers, failed delivery rates, and customer density to optimize final delivery operations. Addresses the highest-cost and most customer-visible segment of logistics.

Lesson 1 • Supply Chain Risk Quantification

Applies value-at-risk, scenario analysis, and fault tree methods to quantify financial exposure from supply disruptions. Enables risk-adjusted decision-making across sourcing, inventory, and logistics.

Lesson 2 • Analytics Strategy and Roadmap Design

Guides students in building a prioritized analytics roadmap aligned to supply chain strategy and organizational capability. Culminates the course by connecting all analytical skills to long-term value creation.

Lesson 3 • Digital Twin and Simulation Modeling

Introduces digital twin concepts and discrete-event simulation for testing supply chain design changes before deployment. Reduces implementation risk for major structural decisions.

Lesson 4 • Prescriptive Analytics and Optimization

Extends descriptive and predictive work into prescriptive models that recommend optimal actions under constraints. Covers linear, integer, and stochastic programming applied to supply chain strategy.