Ebook Extending the Industry Evolution Management Flight Simulators: Premium Golf Club and Solar Panel Industries
The Industry Evolution Management Flight Simulator (MFS), developed by the System Dynamics group at the MIT Sloan School of Management, is currently used in the MBA Strategic Management curriculum as a complement to more traditional teaching methods (Hsueh, Dogan, & Sterman 2006). The industry evolution MFS portrays a generic firm and industry structure that is then adapted to represent various industries and strategic environments. Hsueh et al. 2006 provides an overview of the generic structure employed and describes two such applications: the salt industry, where players compete and make decisions only on price, to illustrate classic competitive dynamics in concentrated industries, and the video game console industry, where additional feedbacks through network externalities and complementary assets are central to success.
Here, we describe two new applications that were used in the spring 2007 MBA elective “Technology Strategy” at the MIT Sloan School of Management. Both simulators introduce students to new strategic environments and decision making challenges that extend the lessons of the salt and video game simulators. The first extension portrays the premium golf club industry, based on the experience of Callaway Golf during the 1990s. Callaway experienced tremendous growth following the introduction of its innovative “Big Bertha” driver, only to succumb to the typical “boom and bust” pattern as the market saturated. The Callaway simulator challenges students to explore growth and market saturation in the presence of learning curves and strong brand equity effects. Second, we present a MFS of the photovoltaic solar panel industry, based on the case of the SunPower Corporation, a relatively small technology leader in this growing market. Players must decide how aggressively to price and how much to invest in R&D and process technology so as to capture benefits from learning and process improvement, while also recognizing the importance of knowledge spillovers. In addition, the simulator allows students to experience the potential for profit and growth in an industry that is important to issues of energy and global sustainability.
Management Flight Simulators have several advantages over traditional case study methods of teaching strategy. Although case discussions do allow students to experience a situation similar to an actual board room, they do not provide the opportunity to test hypotheses or formulate strategies over the range of a company’s lifetime. As a result, students may fail to appreciate the complex feedback environment in which decisions must inevitably be placed. Furthermore, without the ability to test hypotheses students may be vulnerable to various judgmental biases including hindsight bias, overconfidence, or confirmation bias (Simon 1979, Kahneman, Slovic and Tversky 1982, Plous 1993). By allowing controlled experimentation and providing feedback on the long term and distal consequences of decisions, management flight simulators overcome some of these limitations and provide a valuable tool for management education.
The Industry Evolution Management Flight simulator has two main goals. The first is to develop the strategic thinking skills of students by allowing them to formulate strategies and test hypotheses across a broad range of strategic environments. Second, by experiencing such a range of environments, students can build specific industry knowledge and learn to appreciate how context may shape the strategies that they choose. For example, strategies may differ greatly depending upon whether the product is a commodity (salt), whether network externalities exist (video games), whether the product is durable or consumed, and whether functionality can be improved (golf clubs), and whether knowledge spillovers are strong (solar). To allow for such a wide range of applications, a the underlying model has a broad model boundary and generates many industry dynamics with few exogenous variables. In addition, the model itself is generic and can be easily adapted by changing parameters and adjusting the relative strength of feedback loops. (If necessary, entire sections can be made inactive for particular industry applications.) Table 1, repeated from Hsueh, Dogan & Sterman (2006) provides an overview of the model boundary. Similarly, Figure 1 describes the structure of the generic model. (For more details on the generic structure see Hsueh et al. 2006).
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