Quantitative modeling and analysis of business processes

In this course students learn

• How to construct a descriptive model (input variables, output variables)
• How to create a prescriptive model (objective function, decision variables, restrictions) from a descriptive model
• How to decide which model(s) from literature can be applied or adjusted in specific situations in practice
• How to identify assumptions under which a model yields a correct solution to the problem
• How (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
• How to interpret the results obtained with a model (worst case or best case)
• How to statistically test the model assumptions
• How to verify and validate a model
• How to 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

You must meet the following requirements

• Registered for a degree programme other than
• HBO-TOP Applied Physics, Pre-Master

• Additional handouts and publications