Module 0: Basics of Python and SQL
Kick off with Python essentials: write code, work with NumPy arrays, Pandas dataframes, and build simple visuals with Matplotlib. Practice debugging with AI tools like ChatGPT, and connect SQL queries with Python to pull and manage data.
Module 1: Descriptive Analytics
Work with data from SQL, Excel, and CSV files, clean it, and turn it into reports in Excel, Word, and PowerPoint. Analyze supply chain metrics like inventory turnover, stockouts, OTIF, demand variability, and customer/order trends. You’ll also apply text analytics with AI, practice data storytelling, and build dashboards in tools like Power BI and Streamlit to share insights.
Module 2: Diagnostic Analytics
Go beyond “what happened” to uncover why. Use correlation, regression, and causal inference to explain issues like stockouts, demand drops, and supplier performance. Apply machine learning methods like Random Forests, clustering, and anomaly detection to identify drivers of delays, cancellations, and bottlenecks.
Module 3: Predictive Analytics
Move from explaining the past to anticipating the future. Build regression and classification models to forecast demand, predict lead times, and assess supplier risk. Apply time-series forecasting with ARIMA, SARIMA, and tools like Prophet to tackle intermittent demand, evaluate forecast accuracy, and combine models for more reliable business decisions.
Module 4: Prescriptive Analytics (Simulation)
Test decisions before making them. Run Monte Carlo and discrete-event simulations in Python to model risks, bottlenecks, and “what-if” scenarios for inventory, logistics, and production. Work with probability distributions, Markov chains, and scenario planning (best, worst, likely cases), and explore digital twins to evaluate policies and prepare for disruptions.
Module 5: Prescriptive Analytics (Heuristics and Optimization)
Learn how to design and optimize supply chain networks and decisions with Python. You’ll work on problems like where to place facilities, how much to stock, how to balance production lines, and how to route deliveries — using both heuristics and optimization (LP, MILP, metaheuristics). The module also covers uncertainty (newsvendor, stochastic programming), multi-objective trade-offs, and decision tools like AHP, giving you a toolkit to balance cost, service, and resilience.
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