Supply Chain Analytics

Objectives:

The course aims to give the students

• an overview of supply chain management,
• understanding of how to use advanced optimization techniques and artificial intelligence (AI) algorithms to solve and analyze decision problems, and
• ability to solve decision problems occurring in different segments of a supply chain, with a focus on the transportation and logistics industry.

Content:

The topics that are covered in this course includes (but are not limited to):

• Demand forecasting
  • Time Series analysis (Cumulative, Naïve, Moving Average, Exponential Smoothing)
  • Regression analysis
• Inventory Management
  • Economic Order Quantity (EOQ)
  • Single period inventory models
  • Probabilistic inventory models
• Production and Scheduling
  • Optimization models
  • Heuristics
• Supply Chain Network Design
  • Network optimization
  • Facility location problems
  • Covering problems
• Freight Transportation:
  • Last mile delivery (case studies)
  • Maritime transportation (case studies)
  • Future of delivery systems

The course contains a wide range of practical optimization problems in supply chain as case studies.

Upon successful completion of the course, the student will be able to demonstrate the following learning outcomes, categorized by knowledge, skills, and general competence:

Knowledge

• Demonstrate understanding of optimization and decision-making challenges in supply chain management.
• Demonstrate understanding of fundamental optimization and artificial intelligence algorithms. Student can explain concepts of optimization and artificial intelligence algorithms.

Skills

• Apply optimization and AI algorithms to solve supply chain problems such as network design, freight transportation, production planning, and scheduling.
• Apply techniques from optimization and artificial intelligence, combined with economic insights acquired in other courses, to improve a company's logistics solutions.
• Construct mathematical models for network optimization, production, and scheduling problems.
• Apply regression and time series models to historical data for demand forecasting.
• Make decisions on inventory replenishment using quantitative methods.

General Competence

• Discuss real-world applications of optimization and AI in supply chain decision-making.
• Distinguish between various optimization techniques and evaluate their suitability for different problem contexts.