Quantitative modeling and analysis of business processes

In this course, students gain hands-on experience building and applying quantitative decision-support models. You will learn to transform real-world problems into descriptive and prescriptive models, select and adapt methods from the literature, and test model assumptions. Working with realistic datasets, you will clean, analyze, and interpret data, validate models, and perform sensitivity analysis. Through lectures, videos, and interactive feedback sessions, students tackle practice-oriented cases in small groups, implementing solutions in Python and developing both technical and problem-solving skills. More specifically, students in this course will learn how to:

• construct a descriptive model (input variables, output variables)
• create a prescriptive model (objective function, decision variables, restrictions) from a descriptive model
• decide which model(s) from literature can be applied or adjusted in specific situations in practice
• identify assumptions under which a model yields a correct solution to the problem
• (empirical) input data for a model can be collected, cleaned, and analyzed and, if applicable, compared with a theoretical distribution function which can be applied in a decision support model
• interpret the results obtained with a model (worst case or best case)
• statistically test the model assumptions, verify and validate a model and perform a sensitivity analysis

The goal of this course is to teach students how to use quantitative modeling and statistical techniques when analyzing the performance of a company. If they have passed this course, students should be able to:

• Convert subjective statements of complex Industrial Engineering problems into claims on the operational characteristics and performance
• Support the conversion by conducting quantitative analyses learnt in prerequisite courses
• Identify the applicability of models learnt in prerequisite courses to an Industrial Engineering problem
• Indicate suitable models/methods learnt from prerequisite courses to answer an Industrial Engineering problem
• Identify the right distribution functions based on the provided information about an Industrial Engineering problem
• Calculate parameter values of a selected distribution function based on a statistical analysis learnt in the prerequisite courses
• Recall how to implement mathematical models learnt in prerequisite courses in Python
• Interpret the solution of a mathematical model to generate managerial insight on an Industrial Engineering problem

Je moet voldoen aan de volgende eisen

• Ingeschreven voor een andere opleiding dan
• HBO-TOP Applied Physics, Schakelprogramma

• Additional handouts and publications