“Spreadsheet simulation” refers to the use of a spreadsheet as a platform for representing simulation models and performing simulation experiments. This tutorial explains the reasons for using this platform for simulation, discusses why this is frequently an efficient way to build simulation models and execute them, describes how to setup a spreadsheet simulation, and finally examines some limitations on the use of spreadsheets for simulation.

Proceedings of the 2004 winter simulation conference

This introductory tutorial is an overview of simulation modeling and analysis. Many critical questions are answered in the paper. What is modeling? What is simulation? What is simulation modeling and analysis? What types of problems are suitable for simulation? How to select simulation software? What are the benefits and pitfalls in modeling and simulation? The intended audience is those unfamiliar with the area of discrete event simulation as well as beginners looking for an overview of the area. This includes anyone who is involved in system design and modification - system analysts, management personnel, engineers, military planners, economists, banking analysts, and computer scientists. Familiarity with probability and statistics is assumed.

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Introduction to modeling and simulation

Identifying causal relationships is an important aspect of scientific inquiry. Causal relationships help us to infer, predict, and plan. This research investigates the causal relationships between two constructs, perceived enjoyment (PE) and perceived ease of use (PEOU), within the nomological net of user technology acceptance. PE has been theorized and empirically validated as either an antecedent or a consequence of PEOU. We believe that there are two reasons that account for this ambiguity the conceptual coupling of PE and PEOU and the limitations of covariance-based statistical methods. Accordingly, we approach this inconsistency by providing more theoretical reasoning and employing an alternative statistical method, namely Cohen’s path analysis. Specifically, as suggested by previous research on the difference between utilitarian and hedonic systems, we propose the conditional dominance of causal directions. Empirical results from two studies using different technologies and user samples support the theoretical claim that the PE∆PEOU causal direction outweighs the PEOU∆PE direction for utilitarian systems. There are both theoretical and the methodical contributions of this research. The approach applied in this research can be generalized to study causal relationships between conceptually coupled variables, which otherwise may be overlooked by confirmatory methods. We encourage researchers to pay attention to causal directions in addition to causal connectedness.

Causal Relationships Between Perceived Enjoyment and Perceived Ease of Use: An Alternative Approach

Many simulation experiments require much computer time, so they necessitate interpolation for sensitivity analysis and optimization. The interpolating functions are 'metamodels' (or 'response surfaces') of the underlying simulation models. Classic methods combine low-order polynomial regression analysis with fractional factorial designs. Modern Kriging provides 'exact' interpolation, i.e., predicted output values at inputs already observed equal the simulated output values. Such interpolation is attractive in deterministic simulation, and is often applied in computer aided engineering. In discrete-event simulation, however, Kriging has just started. Methodologically, a Kriging metamodel covers the whole experimental area; i.e., it is global (not local). Kriging often gives better global predictions than regression analysis. Technically, Kriging gives more weight to 'neighboring' observations. To estimate the Kriging metamodel, space filling designs are used; for example, latin hypercube sampling (LHS). This paper also presents novel, customized (application driven) sequential designs based on cross-validation and bootstrapping.

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Kriging interpolation in simulation: a survey

Simulation is a widely applied tool to study and evaluate complex systems. Due to the stochastic and complex nature of real world systems, simulation models for these systems are often difficult to build and time consuming to run. Metamodels are mathematical approximations of simulation models, and have been frequently used to reduce the computational burden associated with running such simulation models. In this paper, we propose to incorporate metamodels into Decision Support Systems to improve its efficiency and enable larger and more complex models to be effectively analyzed with Decision Support Systems. To evaluate the different metamodel types, a systematic comparison is first conducted to analyze the strengths and weaknesses of five popular metamodeling techniques (Artificial Neural Network, Radial Basis Function, Support Vector Regression, Kriging, and Multivariate Adaptive Regression Splines) for stochastic simulation problems. The results show that Support Vector Regression achieves the best performance in terms of accuracy and robustness. We further propose a general optimization framework GA-META, which integrates metamodels into the Genetic Algorithm, to improve the efficiency and reliability of the decision making process. This approach is illustrated with a job shop design problem. The results indicate that GA-Support Vector Regression achieves the best solution among the metamodels.

A systematic comparison of metamodeling techniques for simulation optimization in Decision Support Systems

Web site developers creating 'data-based Web pages' that interact with organizational databases need to know server- and client-side processing.

Integrating web sites and databases

1. INTRODUCTION Today, business processes and decision making depend heavily on information systems such as Enterprise Resource Planning (ERP). ERP are complex information systems, which integrate business processes and decision-making at the organizational level. Understanding business processes and being able to use enterprise software are skills in great demand by industry and many business schools require the teaching of hands-on skills in ERP. However, it is a challenge for instructors to teach and students to learn business processes and ERP software in the classroom since business students often lack knowledge of real-world business processes and have limited IT skills available to operate an ERP software application (Leger, 2006; Seethamraju, 2011). To overcome this difficulty, many business schools have introduced ERP simulation software to their curriculum. Using simulation games in business education is an innovative pedagogical approach. By playing software games, students can understand better business processes and ERP from learning by doing (Leger, 2006). ERPsim (ERP Simulation Game) is an ERP teaching-learning software tool developed by HEC Montreal, Canada. ERPsim simulates a real-world marketplace in which virtual companies can operate business processes using a commercial version of SAP software (Leger, 2006). In the classroom, student teams operate a virtual wholesale beverage distribution company using a SAP client. Each team uses standard ERP reports and transactions to manage all business processes involved in the marketing, inventory, sales, and forecasting of various bottled water products. The teams analyze these transactions and review financial reports during the simulation and compete against each other in the same marketplace with the goal of maximizing profit. The simulated marketplace provides students with opportunities to practice their business strategies and to develop hands-on skills to manage business processes using SAP clients. "Using the SAP simulation, students also develop technical skills through direct interaction with an actual SAP client." (Cronan and Douglas, 2012, p. 4). Worldwide, over 130 universities have adopted ERPsim (https://erpsim.hec.ca/en/about/participating_universities) in their IS or other business courses. Pedagogical evidences suggest that ERPsim improves students' learning performance in IS courses (Leger, 2006; Seethamraju, 2011; Cronan and Douglas, 2012). However, an extensive literature review indicates that little is known about causal relationships among cognitive-psychological factors, learning behavior and learning outcomes. There is also a lack of theory-supported empirical studies on the effectiveness of ERPsim in students' learning behavior and performance. In particular, no empirical study has investigated how cognitive-psychological antecedents influence students' learning behavior and outcomes when they used ERPsim as a learning tool. It is not known what these factors are and how they improve students' learning performance when using ERPsim. This study aims to close the research gap with an empirical examination of the effects of some psychological factors on students' learning behavior and outcomes when they participate in ERPsim games in the classroom. Specifically, a theoretical model is proposed to investigate the effects of enjoyment and cognitive appraisal on the behavioral intention to use the learning tool and the effectiveness of the learning tool. The effects of enjoyment and cognitive appraisal on behavior are well acknowledged in both IS and pedagogy literature (e.g., Davis, Bagozzi, and Warshaw, 1992; Venkatesh, 2000; Van der Heijden, 2004; Wakefield and Whitten, 2006; Beaudry and Pinsonneault, 2005; Fadel and Brown, 2010). The contribution of this study is twofold. First, the researchers extend upon prior research of ERPsim by focusing on causal relations among antecedent variables and learning behavior and outcomes. …

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How Does ERPsim Influence Students' Perceived Learning Outcomes in an Information Systems Course? An Empirical Study.

This paper points out the need for performance measures in the context of simulation optimization and suggests six such measures. Two of the measures are indications of absolute performance, whereas the other four are useful in assessing the relative performance of various candidate metamodels. The measures assess performance on three fronts: accuracy of placing optima in the correct location, fit to the response, and fit to the character of the surface (expressed in terms of the number of optima). Examples are given providing evidence of the measures' utility—one in a limited scenario deciding which of two competing metamodels to use as simulation optimization response surfaces vary, and the other in a scenario of a researcher developing a new, sequential optimization search procedure.

Performance Measures for Selection of Metamodels to be Used in Simulation Optimization

Using a project as a principal tool in course pedagogy can have many pitfalls. After examining feedback from our project-based courses in MIS, we implement strategies directed at addressing the problems we found. The course pedagogy uses complementary objectivist- and constructivist-learning environments and is guided by recommendations for successful project management found in the literature. Objectivist content (classroom experience) and constructivist activity (the project) are linked but content precedes the activity in a controlled way. Successful project management tactics guide administrative practices, such as student team selection and group equity. To determine the effectiveness of the strategies in providing a challenging learning experience with few administrative problems, a questionnaire was developed and administered to the students in two MIS courses. The feedback on the pedagogy and administration was positive, but issues still remained. Recommendations for practice are given.

Using Group Projects in Mis: Strategies for Instruction and Management

Nonparametric metamodeling for simulation optimization

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