Supply Chain Analytics
Advanced modeling and simulation applications for supply chains utilizing technology, financial, and performance analysis, and customized programming. This course is intended to help students develop the analytical skills needed to examine unstructured business problems, develop decision models, analyze alternatives, and make sound recommendations for action.
The course may cover descriptive and predictive analytics using R, optimization using the Excel Solver, Monte Carlo simulation using @Risk, and LINGO.
Considering specific operations, finance, and marketing problems, the techniques we will study are:
- Problems of optimization such as resource allocation
- Risk analysis
- Data analytics
- Building and analyzing models using various Excel-based tools and add-ins
- Interpreting economic value of model solutions
Expected Learning Objectives
The course supports the following Learning goals:
- Cross Disciplinary Competence: Students practice translating descriptions of decision making problems in various disciplines into formal models, and investigate those models in an organized fashion.
- Critical Thinking: Students skillfully build customized computer models for use in decision support, interpret model results, drawing conclusions supported by the results and effectively present these conclusions.
- Communication: Students’ ability to identify results of analysis and present them in useful ways to support decision making will be strengthened.
- Selection of Topics Covered:
- Advanced analytics tools and data mining
- Structured approach to modeling business problems
- Applying application techniques using software tools
- Decision-making under uncertainty using simulation
- Advanced regression
- Advanced forecasting
- Linear optimization techniques
- Non-linear optimization and genetic algorithms
- Simulation modeling
- Risk analysis
- Financial models
- Inventory and supply chain models
Course Pedagogy and Immersive/Experiential Activities
This course blends traditional in-person activities and practice with online lecture videos. Class time is used to explore, discuss, and review advanced concepts and to assess learning progress. Students engage in the following ways:
- Guided Practice: structured activities outside of the class that introduce modeling concepts through reading, watching video lectures, and other activities.
- Self-Assessments: exercises focusing on quantitative skills and conceptual understanding of the material introduced through guided practice, encouraging a focus on learning the material in deep ways.
- Class Activities: focused on the advanced learning objectives for the course by working problems that promote a deeper understanding of optimization and simulation.
- Concept Quizzes: encountered at the conclusion of each major course topic, students have the opportunity to demonstrate competence in that topic within an in-class quiz.
- Modeling Projects: in addition to the guided practice problem sets and concept quizzes, students have the opportunity to complete three projects that apply modeling techniques to real world applications utilizing real world data. These projects are intended to challenge students to present results in ways that are readily understood.
Students are provided with course notes, textbook resources, lectures.
Grades are typically determined by performance in a series of Practice Problem Sets that progressively cover the course material, three extensive business modeling projects, and three exam assessments.