The paper first discusses the importance of discrete event simulation (DES) in the business school curriculum. It next notes how small Macintosh lap tops have become increasingly popular among business students. We next discuss what DES software is available on the Mac, first directly, then indirectly by running DES software for Windows in some way on the Mac. Noting that there is not much simple DES software on the Mac, but yet a great demand for such software from many business students, we turn to the transfer of one pedagogical software system, aGPSS, from Windows to the Mac. We here first give a brief historic background of aGPSS. Next we discuss some of the problems encountered when transferring aGPSS to the Mac. The paper ends with a brief discussion of some pedagogical aspects of using aGPSS on the Mac in the teaching of basic management science.
As a teacher of simulation at a business school, I have often been asked: Why do you teach discrete event simulation, DES, at a business school? Why are you not content by just simulating financial flows in a spreadsheet? The answer is that I have found DES, implying dynamic stochastic simulation, to be of great importance in a business curriculum for several reasons.
First, I have had DES replace, or at least complement, many analytical/optimization parts of Operations Research or Management Science methods, such as queuing theory, inventory theory, PERT/CPM and parts of Decision Analysis. My students have liked this, since this has implied a greater focus on solving problems, and fewer methods to be learnt and forgotten.
Furthermore, DES has provided my students with a better understanding of the physical processes in a firm. DES is one way of giving business students an introductory understanding of some problems in the areas of production economics, material handling, inventory management, etc. Hence, in an introductory course on production economics for business students, I have had DES play an important role. Closely related to this is the demonstration of the connection between the physical activities and the consequential financial flows. For this kind of simulation, a general-purpose simulation system is of greater interest than a system focused entirely on manufacturing.
Stochastic simulation is also required for handling the uncertainty that is the core of financial theory. We can just think of how we answer the following questions: How much will we sell next year: 100,000 units for certain or 80,000 - 120,000 units? When will customer X pay: Within 30 days for certain or with 80 percent probability within 60 days? What will the $/€ ratio be a year from now: 1.10 for certain or between 1 and 1.2? In all three cases, the last answer, indicating uncertainty, seems more reasonable. If all future payments could be forecast with certainty, all corporate debt would be as safe as government bonds and there would then not be any need for different types of financial instruments and hence no need for financial theory. Against this background, it seems strange that most simulation of the future financial position of a corporation, e.g. cash forecasts, is done using deterministic simulation, without any uncertainty, by ordinary spreadsheets. Instead most financial simulation should be stochastic.
Finally, there is a need for dynamic simulation in the form of DES, allowing us to follow each major payment, regardless of when it takes place. This can be illustrated by Figures 1 and 2 of a cash forecast of a small corporation. These diagrams have been produced by a simple aGPSS simulation program for cash forecasting, presented in Born and Ståhl (2013). The program deals with an importer who buys and sells certain machines. It pays the producer in cash directly for each unit, but provides the customers with credit. Orders arrive according to an exponential distribution, while customers' payment times vary according to an Erlang distribution. Our students can write this program after ten hours of study.
Among other important applications of DES that are of interest in a business school, I should mention the establishment of optimal equipment life, overbooking and price differentiation in the airline industry, bidding on stocks of an oil company with uncertainty about the success of an oil exploration process, valuation of perfect information in decisions under risk and a model of stocking of perishable goods in a supermarket. Another example illustrates discrete/continuous simulation as applied to business problems with the aim of doing market forecasts and evaluations of high tech stocks. Another model deals with the evaluation of European options, which for the case of constant volatility can be solved analytically, but for the case of volatility varying over time requires simulation. Other applications include simulation based costing and the use of laptop based simulation models for sales support (Ståhl 2016).