Theory:

• Definitions and types of simulation. Systemic approach. Analysis of the Monte Carlo simulation technique.
• Historical evolution of simulation. Wider applications of simulation. Advantages and disadvantages of simulation. Analysis of the simulation process. Simulation programming languages.
• Meaning of random numbers. Historical review of the use of random numbers. Random number generators.
• Outline of randomness tests: theories and examples. Analysis of chi-square (X²) test, equal distribution test, serial test, gap test, poker test, coupon collector’s test, transfer test, runs test (Wald–Wolfowitz).
• Analysis of the steps that need to be followed for a simulation (recording historical values, calculating probabilities and random numbers intervals, creating a model, simulating, drawing conclusions).
• Business simulations. Detailed application of complex business simulations – examples (e.g. cash flow model, loss model, company transaction model, machine performance improvement model). 
• Queuing theory – description of theory, characteristics of the queuing process, application of simulation to queuing theory (e.g. queuing in a bank).
• Game theory – Outline of basic theory – Categories of games – Areas of game theory – Categories of games. Application of simulation to business decision making. Application of simulation to game theory (e.g. inventory, stock exchange).
• Meaning and definitions of business games. Applications and uses of business games. Meaning, definition and uses of virtual enterprises. Description of business game and virtual enterprise implementation framework.

Workshops:

• Basic concepts: mental model, stock, flow.
• An introduction to simulation software: description of capabilities, description of environment of use.
• Gradual running of a simple modeling example. Introduction of stocks, flows, other variables, connectors, variable data entry. Use of tables and graphs. Use of graphical functions. Application and configuration of simulation.  Application and configuration of sensitivity analysis. Use of tools to present simulated model and simulation.
• Implementation of specific business processes for a better understanding of the simulation software. Types of complexity in simulation models (bank stock application, bank stock with delay application, etc.).
• Feedback systems modeling. Examples of one step processes (Little’s Law). Queuing systems. Examples of multi-step processes – serial and parallel processes. Supply chain modeling. Beer game example. Use of distributions in simulation modeling (uniform, normal, exponential, Poisson, binomial). Process improvement modeling.

This course focuses on business simulations and business games, examining the way in which simulation and games are developed and applied in different fields. Various key concepts and techniques relating to business simulations and business games will be analysed, while the use of different examples, applications with real data, and case studies will help students to develop the necessary skills to apply simulations and games. Finally, in addition to business games, virtual enterprise programmes will be analysed using applications.

Current literature, applications, and case studies, combined with the workshop component of the course, will help students to understand academic concepts and develop skills, enabling them to:

• apply business simulation for problem solving;
• perform modeling and simulation of business processes;
• be aware of the importance of business games and virtual enterprises;
• develop and implement business games; and
• use business process simulation software.