INCORPORATING DECISION SUPPORT SYSTEMS INTO MANAGEMENT SIMULATION GAMES: A MODEL AND METHODOLOGY Developments in Business Simulation & Experiential Exercises, Volume 11, 1984 261 INCORPORATING DECISION SUPPORT SYSTEMS INTO MANAGEMENT SIMULATION GAMES: A MODEL AND METHODOLOGY William F. Muhs, Montana State University Richard W. Callen, Montana State University ABSTRACT In an attempt to overcome some of the limitations and criticisms of simulation games, a model was developed linking a simulation game (IMAGINIT) with a decision support system (IFPS) for use in Business Policy courses. The procedure provides students with a methodology (student generated) that should improve their decision making and strategic planning. INTRODUCTION Although simulation games have been used in collegiate schools of business for two decades, they have been subjected to extensive and often critical reviews (Neuhauser, 1976; Wolfe, 1976; Frazer, 1978). A fairly frequent theme of criticism has centered upon the lack of a model or system allowing game participants to better utilize and integrate the data generated (Hand and Sims, 1975; Lill, et al., 1980). Wolfe (1976) stated that: The use of simulations does not appear to encourage a deliberate and objectively analytical approach to strategy making and organizational structuring nor does it lend to the generation of systematic control or management information systems (p. 54). It seems ironic that on the one hand simulation games are designed to replicate (in various degrees) a real- world managerial system, yet on the other hand do not provide a means of massaging data as done in the real world. It is the authors’ contention that this missing link may account for a good deal of the criticism by both simulation game players and administrators. There is no question that students can be subjected to a great deal of information overload and uncertainty in many simulation games. Certainly some of this is necessary and intentional in say, a business policy course if it is to be consistent with AACSB guidelines. Yet even with one decision variable the student may be faced with hundreds or even thousands of potential combinations. One approach is to simply expect (or demand) that the student will apply the decision-making tools he/she has learned in business school. To utilize many of these tools (even with computerized assistance) requires a great deal of time and effort which may become secondary to the immediate time demands for understanding the games’ mechanics, group decisions, and broader planning. Another pitfall is that many students have never used these tools in an applied environment. The problem is exacerbated by the fact that complex simulation games make it impossible, impractical, or very costly to generate numerous simulations utilizing different decision values on a completely interactive basis. In order to overcome some of these limitations, the authors have designed a model incorporating a decision support system into a total enterprise simulation game which more closely reflects what managers are doing in today’s business world. SIMULATION GAME The simulation game utilized is THE IMAGINIT MANAGEMENT GAME (Barton, 1978) which one of the authors has administered in business policy and strategy courses for the past eight years. IMAGINIT is a fairly complex, interactive, total business simulation game. The game has a high degree of uncertainty requiring considerable skill in the decision-making process. In the business policy course, the game is played on a team basis and typically consists of a ‘practice” play with three simulated periods and then a “real” play with ten simulated periods utilizing a different version of the game. In the past, students were required to prepare pro-forma income statements and cash budgets before submitting their decisions. The intent being to get them closer to a “what-if” mode of thinking and analyzing cause-effect relationships both on strategic and tactical variables. The results always appeared quite mixed; some groups did actually prepare multiple versions based on different values and assumptions while others conducted superficial analyses. When asked to supply a rationale for their decision, the frequent response was “we didn’t assess the potential impact of the change.” Clearly, the need exists for an improved methodology. DECISION SUPPORT SYSTEM The use of DSS by numerous managers is well documented in the literature. For example, Klein (19s2) reported that s5% of the largest firms in the United States utilized computer based financial modeling and that the most frequently used tool was “what-if” analysis followed by sensitivity analysis. Furthermore, of the typical corporate departments, these tools were most frequently used by the strategic planning group. However, it is extremely difficult if not impossible for decision makers to construct experiments which exactly replicate various phenomena under examination. That is, the manager/decision maker does not have the equivalent of the scientist’s laboratory. Fortunately, it is possible to utilize modern computer technology to emulate some of the numerous variables and parameters which effect one or more related decisions. Although the laboratory provided by DSS tools are not as realistic or nearly as perfect as the laboratory of the Developments in Business Simulation & Experiential Exercises, Volume 11, 1984 262 scientist, it is a step in the right direction and will continue as the technology is improved and integrated into executive support systems (Hayen and Callen, 1983). The use of DSS generators provides the decision maker with tools to improve the quality, effectiveness, efficiency, and productivity in structured decision tasks. The components of the system allow the manager to assess, “what has been,” “what is,” “what does it mean,” and to perform “what ifs” and “risk analysis.” The “what has been” and the “what if” component is accessed through the data base system, while the “what does it mean” component is approach-. ed through the statistical analysis. The “what ifs” and “risk analysis” are approached through the modeling system. If the decision maker does not require much data and the statistical analysis is not too complex, then the modeling system may perform all three functions. In this paper, the authors illustrate the use of a DSS generator called the Interactive Financial Planning System, (IFPS) which was developed by Execucom Corporation of Austin, Texas. The use of IFPS provides students with the ability to analyze systematically both tactical and strategic variables of a simulated business without the cumbersome paper work normally encountered in such projects. It is difficult to incorporate and illustrate to students the use of computer technology in many areas of business but DSS provides the student with a portfolio of computer tools which can improve the quality and quantity of decisions in a short period of time. The student also develops an understanding of the risk and returns associated with these systems. Clearly, students make decisions not IFPS. What is IFPS? IFPS is a decision support system generator. It has been developed over the last several years and is used by more than four hundred major U.S. corporations and over one hundred and fifty universities. It is one of the more popular and widely used planning systems. IFPS is not what is commonly referred to as a spreadsheet language which is available on many micro computers. IFPS is a sophisticated computer based financial modeling or planning and budgeting system. Some of the major features of IFPS are: (1) User friendly, (2) User interface is simple and natural, (3) English syntax of language and supports the use of common business terminology, (4) Easy to learn and master, (5) Non-procedural language, (6) Dynamic versus static system, i.e., the system accommodates diverse levels of proficiency; and, (7) IFPS has demonstrated its maintainability, reliability and availability. LINKING IFPS AND IMAGINIT The incorporation of IFPS and the IMAGINIT Game is a relatively simple task. Both IFPS and IMAGINIT are written in FORTRAN. The task of joining the systems is merely a process of passing data between IMAGINIT and IFPS. IMAGINIT requires the decision maker or decision group to provide various decision values which are inputted to the simulator then entered into the IMAGINIT simulation game. The data items are the various decision variables which the student/group must determine from period to period. This process continues interactively for a number of predefined periods. The ultimate goal being to maximize the firm’s wealth in a competitive environment. IFPS is utilized by the student/group each period to examine various alternatives in terms of tactical and strategic variables. Each period’s output variables which are normally printed and distributed to the student/group are written to a file and this data is passed to IFPS as a data file. The data is then accessed by an IFPS model. The latter may be a pro- forma income statement, cash flow budget, sources and uses of funds statement, or a balance sheet model. This is determined by the students proficiency and creativity with respect to the DSS generator - IFPS. The student/group is able to experience the use of a DSS generator and focus their energies on various aspects of the game without the drudgery of numerous mundane hand calculations. That is, the student/group concentrates on elements of the data base and their relationship with other variables in terms of complex interactions which would be difficult to illustrate without numerous additional experiments and experiences. In other words, IMAGINIT creates the data base consisting of finance, production, marketing, personnel, and other data. IFPS provides a data base management and model system for the decision maker to effectively explore alternatives. Perhaps the most important phenomena is that students will develop their own models, and develop an understanding of the relationship of key variables. If a student can develop an IFPS model of a particular phenomenon (for example a cash flow projection), the student/group then understands the phenomenon and develops a deeper understanding of the problem. OPERATING THE SYSTEM In order to utilize the DSS generator, the student merely accesses the university’s computer where IFPS resides. (Appendix I) The student/group logs onto the system and enters a single command which executes IFPS. The student/group then provides IFPS with a file name where one or more predefined models have been developed. The student/group is able to explore numerous alternatives and combinations of alternatives on all IMAGINIT variables for each of their decision periods (a group/ student which has no experience with IFPS can utilize a command file procedure which determines all values of the variables interactively). Appendix II represents the IMAGINIT pro-forma income model designed by the authors. Appendix III illustrates an example of how students use IFPS. The first illustration is an example of a new set of student decisions and the resulting pro-forma income statement. The second illustration is “what-if” where we have changed the price of one of the products and determined the effect on net earnings. Students can also perform sensitivity analyses by specifying percentage changes in price and the resulting impact on net earnings (Appendix III). The final example we have chosen to show is a goalseeking situation where students can specify the desired or target net earnings. The IFPS model then calculates the value of the variable, ceteris paribus, necessary to achieve this result (Appendix III). Developments in Business Simulation & Experiential Exercises, Volume 11, 1984 263 SUMMARY While we have shown only a few examples of IFPS operations on an IMAGINIT game data base, the potential exists for many more operations. For example, IFPS allows for Monte Carlo risk analysis which would be useful as a strategic planning tool. Hopefully, the methodology of combining an active student generated data base in the form of a simulation game with a DSS will go a long way toward improved decision making. Other potential benefits include improved strategic planning and moving closer to a “pro-active” mode of planning rather than “re-active.” At the present time, the authors have had limited student use of the process. However, indications are that students will now have the opportunity to improve considerably their simulation game performance and learn more from this added experiential experience. Developments in Business Simulation & Experiential Exercises, Volume 11, 1984 264 Developments in Business Simulation & Experiential Exercises, Volume 11, 1984 265 Developments in Business Simulation & Experiential Exercises, Volume 11, 1984 266 REFERENCES [1] Barton, Richard F., The IMAGINIT Management Game (Lubbock: Active Learning, 1978). [2] Frazer, J. Ronald, “Educational Values of Simulation Gaming,? Proceedings of the Association for Business Simulation and Experiential Learn- 1978. [3] Hand, Herbert H., and Henry P. Sims, “Statistical Evaluation of Complex Gaming Performance,” Management Science, Vol. 21, No. 6, February 1975. [14] Hayen, Robert L., and Richard W. Callen, IFPS-An Introduction (Omaha: First Horizon Corp., 1983). [5] Klein, Richard, “Computer-Based Financial Modeling,” Journal of Systems Management, May 1982. [6] Lill, David J., James B. Shannon, and Robin T. Peterson, “An Experiential Approach to Teaching Business Through General Systems Theory and Simulation,” Journal of Experiential Learning and Simulation, Vol. 2, No. 2, June 1980. [7] Neuhauser, John J., “Business Games Have Failed,” Academy of Management Review, Vol. 1, No. 4, October 1976. [8] Wolfe, Joseph, “The Effects and Effectiveness of Simulations in Business Policy Teaching Applications,” Academy of Management Review, Vol. 1, No. 2, April 1976. Table of Contents Volume 11, 1984 Simulation Gaming as a Means of Researching Substantive Issues: Another Look A Further Test of the Group Formation and its Impacts in a Simulated Business Environment Impact of Economic Patterns on Student Performance in Computer Business Simulation Games Majority Fallacy Game with Independent Student Simulation and a Case Introducing the Marketing Channel Laboratory A Comparative Evaluation of a Marketing Game A Study of Comparative Effectiveness of Problem-Solving Technologies The Impact of Hierarchical and Egalitarian Organization Structure on Group Decision Making and Attitudes Risk-Free Decision Making The EX-STRA Export Strategy Game Computer Education for Management Students Developing a Computer Game/Job Simulation to Teach Functional Literacy Skills Experiencing Socialization First Hand: An Experiential Exercise in Organizational Socialization Networking Distributive Versus Integrative Approaches to Negotiation: Experiential learning Through a Negotiation Simulation Managerial Education and the Real World: Foudations for Designing Educational Tools Diagnosing Group Climate to Improve Supervisory Effectiveness Student background as a Factor in Simulation Outcomes: The Collective bargaining Example The Use of Pre-Plays in Management Education Experiencing the Process Debrief: A Workshop ABSEL Megatrend Roots MEGATRENDS for Business Simulation and Experiential Learning The Effects and Consequences of the Megatrends on Simulation Gaming: One View Opportunities for the Future: ABSEL's Role Experiential Learning-Based Discussion vs. Lecture Based Discussion: A Comparative Analysis in a Classroom Setting An Evaluation of the Minitab Package in Teaching Business Statistics Concepts A Path Analytic Study of the Effects of Alternative Pedagogies Developing and Using Weighted Application Blanks: An Experiential Exercise Building Airplanes Individual vs. Group Grade: An Exercise in Decision making A Marketing Plan Exercise: Development of Interteam Cooperation Using a Coordinated Experiential Approach Using Student Experience as the Basis for a Consumer Behavior Learning Exercise Student Evaluations of Instructors: What do Students Believe? A Description of the SOFTCAT Computer Assisted Teaching System Comparisons of Practitioners' and Professors' Perceptions of Business Policy Content and Learning Methods The Perceived Relationship Between Pedagogies and Attaining Course Objectives in the Business Policy Course The Use of Simulation in the Teaching of Business Policy A Research Study on Strategic Decisions in a Business Simulation Strategic Management Decision Making Researched Via Simulation Gaming Using Simulation to Investigate Factors in Competitive Bidding Combining Experiential Learning and management Assistance A Model for Teaching Management Skills Putting Experience Back into Experiential Learning: A Demonstration The Teaching and Behavioral Measurement of Managerial/Organizational Competencies: Developing Experiential Exercises and Simulations A Simulation Game Model for Conglomerates QCLAB - A Microcomputer Laboratory in Quality Control CTSS: A Commodity Trading Simulation System Problem Solving: An Exercise on Learning, Coaching, and Operant Conditioning A Demonstration of the Effects of Feedback as a Category of Reinforcement The Assessment of Feedback and Disclosure in Interpersonal Relations: An Experiential Exercise A Study to Determine Whether the Teaching of Basic Grammar Skills in Business Communication Classes Improves Students' Business Letter Writing Corporate Maladies Through the Eyes of the Memo Writer: A Seldom Used Experiential Tool Executive Bailout at Shake & Spear, Inc. The H.E./L&P Merger Intercultural Nonverbal Communications: An Experiential Exercise The Evolving Business Policies Course - Is Management Gaming the Logical Pedagogy? The Use of Decision Simulations in Management Training Programs: Current Perspectives Humanizing the Business of Medicine: The Use of Simulated Patients to Train medical Students Systematic Integration of Simulation Methods in a Graduate Management Curriculum Modeling Non-Price Factors in the Demand Functions of Computerized Business Using Spacial Relationships to Estimate Demand in Business Simulations Two Algorithms For Redistribution Of Stockouts In Computerized Business Simulations Leadership And Strategic Behavior A Comparison Of Two Business Strategy Simulations For Microcomputers Incorporating Decision Support Systems Into Management Simulation Games: A Model And Methodology Using Micro-Computers To Support The Analysis Of Complex Cases: It's As Easy As 1-2-3 Strategic Formulation Consistent With Pims: A Micro-Computer Application