Course Overview
This cutting-edge course explores how Artificial Intelligence and Machine Learning are revolutionizing supply chain management. From foundational AI, ML, DL, and Gen AI concepts to statistical reasoning, predictive modeling, Gen AI frontiers, demand intelligence under uncertainty, and transportation network optimization — you will transform your supply chain into an AI-driven intelligent system across 6 comprehensive modules.
What You'll Achieve
Understand AI, ML, DL, and Gen AI fundamentals and their supply chain applications
Apply statistical reasoning to validate AI models for supply chains
Build predictive models from regression to actionable insights
Leverage Generative AI for supply chain frontiers and innovation
Forecast demand with uncertainty quantification and context awareness
Optimize transportation networks and solve the last mile paradox with AI
Course Curriculum
Master the following skills through our comprehensive modules:
Introduction to AI, ML, DL, Gen AI
7 topics
8 hours
Topics Covered:
- SCM foundations: strategic backbone from supplier to customer delivery
- Artificial Intelligence: scope, history, classical AI, and the ChatGPT moment
- Machine Learning: supervised, unsupervised learning, and reinforcement learning in SCM
- ML in practice: demand forecasting with Random Forest, SKU classification with K-Means, route segmentation with DBSCAN
- Deep Learning: neural networks, core components, input/hidden/output layers
- Generative AI in supply chain: dynamic demand forecasting, real-time risk assessment, supplier relationship management, NLP for communication
- Gen AI frontiers: bias mitigation, system integration, predictive maintenance, human-AI collaboration
Statistical Reasoning for Supply Chain Analytics
7 topics
8 hours
Topics Covered:
- Data types in SCM: qualitative vs quantitative, nominal, ordinal, interval, ratio, discrete vs continuous
- Measures of central tendency and dispersion: mean, median, mode, range, variance, standard deviation
- Outlier detection: box plots, IQR method, Z-score analysis
- Inferential statistics: sampling distributions, confidence intervals, hypothesis testing (Z-test, t-test, chi-square)
- Data cleaning for supply chains: handling missing data, error detection, validation rules, data maturity model
- Excel tools for data cleaning: FILTER, Power Query, inventory reconciliation
- Demand forecasting foundations: time series decomposition, exponential smoothing, ARIMA, safety stock calculation, lead time forecasting
Predictive Modeling: From Regression to Insights
7 topics
8 hours
Topics Covered:
- Independent and dependent variables in supply chains: demand, logistics, production, inventory factors
- Historical roots of regression in SCM and transportation logistics
- Simple linear regression: the core equation, mathematical foundation, p-values in hypothesis testing
- Business case: estimating freight cost by distance using Excel regression analysis
- Interpretation of intercept, slope, R-squared, and prediction for new routes
- Multiple regression: adding load weight as predictor, multivariate coefficients, model assumptions
- Critical pitfalls: multicollinearity, non-linearity, and limitations in complex networks
Gen AI in SCM Frontiers
7 topics
8 hours
Topics Covered:
- Historical evolution of generative AI: from GANs to transformers and LLMs
- Core architecture: embedding layers, attention mechanisms, post-generation validation, and bias detection
- Training data dynamics, model scaling, and ethical challenges (deepfakes, misinformation, copyright)
- Prompt engineering: context, role assignment, chain-of-thought, multi-modal prompting, iterative optimization
- Gen AI in SCM: demand forecasting, supplier risk assessment, dynamic logistics planning, inventory optimization
- Limitations: adversarial conditions, pandemic/geopolitical blind spots, transparency in audit trails
- Future outlook: AI as co-pilot, on-device Gen AI, probabilistic risk assessment under variable demand
Demand Intelligence: Forecasting with Uncertainty Context
7 topics
8 hours
Topics Covered:
- Demand planning fundamentals: data aggregation, forecasting methods compared, external factors, technology role
- Demand management process: influencing demand through pricing, promotions, cross-functional alignment
- Forecasting methodology: time series decomposition (level, trend, seasonality, noise), linear regression for forecasting
- Forecast accuracy metrics: MAE, MSE, MAPE, feedback loops for continuous improvement
- New product forecasting: market research, analogous product comparison, scenario planning, iterative refinement
- ML forecasting approaches: LSTM/RNN, Random Forest, Gradient Boosting, ARIMA, preprocessing and model selection
- Gen AI forecasting: pattern recognition prompts, trend projection, seasonal adjustment, outlier detection, external factor integration, narrative-based forecasting, confidence intervals, iterative refinement
Transportation, Networks, Last Mile Paradox
10 topics
8 hours
Topics Covered:
- Transportation in supply chains: cost vs service tradeoff, working capital impact, total landed cost equation
- Transportation mode economics: trucking, rail, ocean freight, air transport, pipeline, intermodal cost analysis
- Transportation operations: routing strategies, consolidation, cross-docking, hub-and-spoke, milk runs, 3PL/4PL roles
- Supply chain network design: strategic vs tactical, facility location, capacity allocation, total network cost (TC = T + F + I + H + P)
- Network architecture: centralized vs decentralized, hub-and-spoke, regional DCs, inventory positioning, safety stock pooling
- Network optimization: center of gravity method, mixed-integer optimization, flow optimization, AI-driven design
- Uncertainty and resilience: stochastic demand, disruption planning, multi-echelon inventory, risk pooling, robust optimization
- Last-mile delivery: cost drivers, e-commerce impact, urban logistics, speed economics, AI-driven demand clustering, delivery promise prediction
- Vehicle routing problem: mathematical formulation, time windows, dynamic routing, AI-driven optimization with reinforcement learning
- AI-driven logistics: digital twins, end-to-end optimization, autonomous planning, multi-agent systems, ROI analysis
Practical Applications
- Build AI-powered demand forecasting systems that reduce forecast error by 30-50%
- Implement Gen AI tools for automated supply chain reporting and documentation
- Create predictive models for inventory optimization and stockout prevention
- Design intelligent decision support systems for real-time supply chain decisions
- Develop ML-based anomaly detection for supply chain disruptions
Certification
Receive an official 'AI Applications in Supply Chain Management' certificate from Think Supply Chain upon successful completion
Career Impact
AI skills are the most valuable in modern supply chain management. Professionals who can bridge supply chain expertise with AI implementation are among the highest paid in the industry.
Your Instructor
Hazem Hamza
Supply Chain & Data Science Consultant
With over 12 years of experience in supply chain management across manufacturing, retail, and logistics industries, Data Science and Software Engineering projects, Hazem brings practical expertise and academic excellence to help professionals advance their careers.
Ready to Enroll?
40 Hours
Limited seats available
40 Hours Training
Official Certificate
Expert Instructor
Practical Projects
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