Training on Supply Chain Data Analytics and Visualization

Module 1: The Data-Driven Supply Chain

• Moving from descriptive to predictive and prescriptive analytics.

• Identifying key data sources: ERP, TMS, WMS, IoT, and external data.

• Data governance, quality, and integrity principles.

• Case Study/Hands-on Exercise: Assess the data quality of a sample supply chain dataset (e.g., order history) by identifying missing values, outliers, and inconsistencies, and propose a data cleansing plan.

Module 2: Exploratory Data Analysis (EDA) and Visualization Fundamentals

• Principles of effective data visualization (e.g., using Gestalt principles).

• Creating univariate and bivariate visualizations to uncover patterns.

• Identifying correlations and distributions in supply chain data.

• Case Study/Hands-on Exercise: Perform EDA on a freight spend dataset to visualize spend distribution by mode, carrier, and lane, and identify initial hypotheses for cost savings.

Module 3: Advanced Demand Forecasting Techniques

• Time series analysis: decomposition, autocorrelation, and stationarity.

• Advanced models: ARIMA, Exponential Smoothing, and Prophet.

• Incorporating causal factors (e.g., promotions, economic indicators) into forecasts.

• Case Study/Hands-on Exercise: Build and compare a simple moving average model with an ARIMA model to forecast demand for a volatile SKU, evaluating forecast accuracy using MAPE.

Module 4: Inventory Optimization and Safety Stock Modeling

• Statistical methods for determining safety stock levels based on service level and lead time variability.

• Using ABC-XYZ analysis for inventory segmentation and policy setting.

• Simulation of inventory policies under demand uncertainty.

• Case Study/Hands-on Exercise: Calculate the optimal safety stock and reorder point for a portfolio of SKUs with varying demand and lead time variability to achieve a target service level.

Module 5: Network Optimization Modeling

• Fundamentals of facility location and network design models.

• Using optimization tools (e.g., Solver, Python optimization libraries) to minimize cost while meeting service constraints.

• Scenario analysis for network changes (e.g., adding a new DC).

• Case Study/Hands-on Exercise: Formulate and solve a facility location model to determine the optimal number and location of distribution centers to minimize total transportation and warehousing cost for a given demand pattern.

Module 6: Transportation Analytics and Mode Optimization

• Analyzing freight data to identify optimal mode and carrier mix.

• Creating cost-to-serve models at the customer and product level.

• Using regression analysis to understand drivers of freight cost.

• Case Study/Hands-on Exercise: Build a regression model to predict LTL freight cost based on weight, distance, and freight class, and use it to identify shipments that are overpaying compared to the model.

Module 7: Machine Learning for Supply Chain

• Introduction to supervised (classification, regression) and unsupervised (clustering) learning.

• Using clustering for customer segmentation and inventory categorization.

• Using classification for predicting late deliveries or supplier risk.

• Case Study/Hands-on Exercise: Build a classification model (e.g., logistic regression or decision tree) to predict which shipments are likely to be late based on carrier, lane, day of week, and weather data.

Module 8: Data Visualization and Dashboarding with Tableau/Power BI

• Advanced dashboard design principles for executive and operational users.

• Creating interactive dashboards for KPIs, root cause analysis, and scenario exploration.

• Telling a compelling story with data to influence stakeholders.

• Case Study/Hands-on Exercise: Design an interactive supply chain control tower dashboard in Tableau or Power BI that provides real-time visibility into orders, inventory, and shipments, with drill-down capabilities for root cause analysis.

Module 9: Data Storytelling and Communication

• Structuring a data-driven narrative for different audiences (executives vs. operators).

• Avoiding common pitfalls in data presentation.

• Building a business case using data and visualizations.

• Case Study/Hands-on Exercise: Develop a 5-slide presentation for senior leadership that uses data visualizations to present findings on a significant freight spend increase and recommend a specific mitigation strategy.

Module 10: Advanced Analytics Implementation and Culture

• Building a Center of Excellence (CoE) for supply chain analytics.

• Managing the lifecycle of an analytics project.

• Fostering a data-driven culture within logistics teams.

• Case Study/Hands-on Exercise: Create a project charter for implementing a predictive maintenance analytics solution for a fleet, including scope, stakeholders, data requirements, and expected ROI.