BAN403 Simulation of Business Processes

• Topics

  Simulation, i.e., experimentation with a computer model of a real system in order to determine the effect of changes in the system, is the most widely used management science method in practice. The use of simulation in business is increasing as the cost of modeling and running experiments with computers has been drastically reduced.

  It is particularly useful when the actual business process is complex and characterized by uncertainty in demand, processing capacity and delivery times, future interest and currency rates, etc.

  Simulation allows us to capture dynamics and uncertainty in these systems as they evolve over time. It is also possible to model more details of a real system when compared to an analytical or optimization-based model. Experiments are then conducted at a fraction of time and expenses of similar experiments in real systems. A sensitivity analysis of different "what-if" scenarios allows us to evaluate risk of proposed changes to the modeled system as well as forecast outcomes of business decisions before they are implemented in real-world.

  Simulation is gradually replacing some project management methods and traditional analytical tools of operations research to solve problems related to queuing theory, inventory theory, etc. It also represents a predictive modeling tool for robust planning and risk reduction in many systems. It is widely applied for resource capacity planning, finding and removal of bottlenecks in complex systems, evaluation of effectiveness of current and improved systems, alternative capital investment decisions and analysis of cash flows of businesses.

  The course is based on discrete-event process-centric simulation (DES) methodology and relevant theories. The simulation package from AnyLogic with its process modeling library and rich flowcharting capabilities will be used by students to model and analyze different systems.

  Throughout the course students will learn different examples of simulation applications from a wide range of systems: banking (e.g., consumer credits), healthcare, logistics, inventory and manufacturing (e.g. capacity planning), telecommunications (e.g. call and support centers), retail and service (e.g. airport processes), etc.

  The students will need to solve a compulsory exercise (a business case) to demonstrate obtained skills in simulation modeling.

  Towards the end of the course, the students will work on a group-based simulation project of their own choice in close cooperation with a company or an organization. Establishing contact with a company, identification and formulation of the problem’s objectives, and modelling of the actual system and suggestions for improvements of the processes will provide students with the insights of advantages of simulation for their future work in business.

• Learning outcome

  By the end of this course the students are able to

  (Knowledge)

  • Understand the characteristics of discrete-event simulation methodology, impact of uncertainty factors on performance of modeled systems.
  • Understand a typical DES modeling software environment, including input and output of data, modeling concepts, blocks and flowcharting capabilities, experiment design functionality.
  • Recognize real-world problems and situations where DES models can be used for improved decision making.

  (Skills)

  • Build own simulation models in order to analyze business cases that have been discussed in the course.
  • Perform input data collection and analysis for a simulation project.
  • Design, implement, and validate simulation models of real business processes.
  • Incorporate stochastic phenomena into simulation models.
  • Analyze and calibrate models based on observed input data.
  • Evaluate uncertainty in simulation estimates.
  • Interpret simulation results.
  • Draw the necessary conclusions to support business decisions.

  (General competence)

  • Cooperate in a team-based research project.
  • Present simulation project and its results to diverse audiences.

• Teaching

  • The teaching is based on lectures with computer exercises. Two lectures a week (4 hours per week). Additionally, 1-2 guest lectures will be included.
  • The counseling sessions with groups will be available in the initial phase of their project work. Thereafter the groups have approximately one month to complete their projects.

• Required prerequisites

  Basics of Statistics and Probability Theory.

• Credit reduction due to overlap

  Course identical to BUS423.

• Requirements for course approval

  • During the course the students shall complete a compulsory exercise using the course’s simulation software and hand in a written report.
  • The group’s project work will be based on approved proposals. A written proposal for the exam project must be handed in for approval.

  Note: There might be announced additional compulsory activities in the course prior to the registration deadline.

  Course approval from BUS423 is valid for BAN403.

• Assessment

  Group-based (1-4 students) written project report and oral exam. It will be possible to start working on the project report already in week 3, but the intensive work period with the report is planned to be between week 14 and week 18.

  The grade for written project report will be announced to groups prior to the oral exam (held in week 20).

  Each group will present its project to the grading committee during the oral exam. A group-based oral exam will be used to adjust or confirm the initial grade for the course. The adjustment is individual and can be at most one grade, and the adjusted grade will constitute the overall course grade.

  Parts of the exam cannot be taken independently of each other. This course is a continuation of BUS423 and the total number of attempts applies to the course (not the course code).

• Grading Scale

  A - F.

• Computer tools

  The students will use version of AnyLogic (Windows, Max, Linux), and, to a limited extent, its built-in programming language for additional scripts. R can be used for simulation input and output analysis.

• Literature

  • White K.P., Ingalls R.G. Introduction to Simulation, Proceedings of the 2015 Winter Simulation Conference.
  • Mahdavi A. The Art of Process-Centric Modeling with AnyLogic, 2019.