This training course will highlight:

• • The shift from deterministic to adaptive optimization models
  • AI’s role in improving forecasting, routing, and inventory
  • Managing data risks and modeling assumptions
  • Balancing automation with human oversight
  • Strategic and ethical risks of scaling AI

By the end of this training course, participants will be able to:

• • Identify where AI creates value in decision-making under uncertainty
  • Evaluate and ensure data integrity and mitigate input bias
  • Apply AI forecasting models with confidence intervals and probabilistic outputs
  • Understand constraint-based optimization using robust and stochastic approaches
  • Use AI for real-time logistics and warehouse decisions
  • Implement human-AI interaction strategies with explainability and oversight
  • Assess ethical, reputational, and compliance risks in AI deployment

This course is ideal for professionals and leaders in operations, analytics, and digital transformation, including:

• • Supply Chain Managers and Planners
  • Operations and Logistics Analysts
  • Data Scientists and AI Strategists
  • Digital Transformation Leaders
  • Risk and Compliance Managers
  • Procurement and Inventory Controllers

This course combines interactive lectures, case studies, AI tool demonstrations, and group workshops. Participants will explore simulated environments for AI forecasting and optimization, and engage in scenario-based decision-making that tests their understanding of risk, model drift, and ethical boundaries.

Day One: AI in Operational Decision-Making

• • From static models to adaptive, AI-powered tools
  • Operational domains where AI creates measurable value
  • Understanding uncertainty and dynamic decision environments
  • AI tools in Cost Optimization and DSS
  • AI in Big data analytics

Day Two: Data Quality and Forecasting with AI

• • Risks of input data volatility and bias amplification
  • Backtesting models and detecting model drift
  • Machine learning in forecasting: point vs. probabilistic estimates
  • Managing exposure ranges with confidence intervals
  • Machine Learning and Deep learning algorithms

Day Three: Optimization under Constraints

• • Linear vs. nonlinear models in uncertain contexts
  • Robust vs. stochastic optimization approaches
  • Simulation tools for evaluating trade-offs and constraints
  • Transportation Model with AI
  • AI Support and enhancement
  • Machine learning optimization

Day Four: AI for Logistics, Routing, and Inventory

• • Real-time routing decisions using AI algorithms
  • Inventory balancing and replenishment with AI support
  • Simulating warehouse layouts and labor productivity with AI
  • RFID and AI
  • Drones and AI
  • Simulations within AI

Day Five: Human Oversight, Ethics & Strategic Risk

• • Human-AI collaboration and decision overrides
  • Designing for explainability and transparency
  • Black swan testing and stress-testing AI models
  • Regulatory, ethical, and reputational considerations
  • Different Framworks
  • Final Project

BTS attendance certificate will be issued to all attendees completing minimum of 80% of the total course duration