AKTU AI in Supply Chain Management (BMBAOM04) Syllabus PDF – MBA (Business Analytics) 4th Semester

Course Objectives

1. Understand the foundational role of AI in optimizing supply chain operations.
2. Explore AI technologies like machine learning, NLP, computer vision, and digital twins in SCM.
3. Apply AI in demand forecasting, inventory optimization, and supplier relationship management.
4. Develop skills for AI-enabled logistics, smart warehousing, and real-time decision-making.
5. Evaluate the ethical, technical, and strategic implications of deploying AI in SCM environments.

Unit 1: Introduction to AI in Supply Chain Management (6 Hours)

Overview of Supply Chain Management (SCM) and current challenges, Role of Artificial Intelligence in modern SCM, Types of AI technologies used: ML, NLP, Computer Vision, etc., AI vs traditional analytics in supply chain decision-making, Data sources and data quality in SCM, Role of IoT and sensor data in AI-driven SCM, Digital twins in the supply chain ecosystem, Case studies of AI adoption in global supply chains

Unit 2: AI for Demand Forecasting and Inventory Management (6 Hours)

Introduction to demand forecasting: challenges and objectives, Traditional vs AI-based forecasting methods, Machine learning algorithms for forecasting (e.g., ARIMA, LSTM, Prophet), Inventory classification and segmentation (ABC/XYZ with AI), Stockout and overstock prediction using ML models, Tools for AI forecasting (Python, R, Power BI), Case studies: Forecasting in retail/FMCG using AI

Unit 3: AI in Procurement and Supplier Relationship Management (8 Hours)

Strategic sourcing with AI decision support, AI-based supplier selection and evaluation, Predictive risk analysis in procurement, NLP in contract analysis and compliance, Price prediction models for commodities and raw materials, AI for fraud detection in procurement processes, Automation in purchase order and invoice processing, Supplier sentiment analysis using NLP, Multi-criteria decision-making with AI, Case studies: AI in procurement optimization (e.g., GE, Unilever)

Unit 4: AI in Logistics and Transportation Management (8 Hours)

Role of AI in logistics planning and control, Route optimization using ML algorithms, Real-time tracking with AI and IoT integration, Predictive maintenance for transport vehicles, Vehicle scheduling using reinforcement learning, Traffic and weather prediction for logistics efficiency, AI for dynamic shipment consolidation, Cost reduction strategies with transport AI models, Green logistics and sustainability with AI support, Case studies: FedEx, DHL, and Amazon AI in transportation

Unit 5: Smart Warehousing with AI (8 Hours)

Concept of smart warehousing and Industry 4.0, Warehouse layout and space optimization using AI, Robotics and AI-based automated material handling, AI in barcode scanning and inventory tracking, Vision-based inspection and sorting using computer vision, Predictive analytics for warehouse demand planning, Workforce scheduling and optimization, Integration of AI with Warehouse Management Systems (WMS), Energy management and sustainability in smart warehouses, Case studies: Flipkart, Alibaba, and AI-enabled warehouses

Course Outcomes

Suggested Readings

Text Books

1. Artificial Intelligence for Supply Chain Planning by Nicolas Vandeput, Publisher: Independently published
2. Supply Chain Analytics by Peter W. Robertson, Publisher: McGraw Hill Education
3. Artificial Intelligence for Logistics by Wolfgang Kersten & Thorsten Blecker Additional Resources:
4. IBM Supply Chain AI Toolkit (free online tools)
5. Python libraries: Prophet, Scikit-learn, Statsmodels
6. Power BI for SCM dashboards
7. Research papers from Journal of Business Logistics, International Journal of Physical Distribution & Logistics Management
8. MIT OpenCourseWare – Supply Chain Fundamentals with AI modules