This paper presents a soft landing model and an experimental platform. The aim of the modeling effort is to transparently represent the process of landing a spacecraft on the surface of a celestial body. The process of landing is a challenging task because there are two main contradictory performance criteria to be met simultaneously; the landing duration should be as short as possible, but at the same time crashing the spacecraft to the surface should be avoided. If the only criterion was to prevent crashing the spacecraft, that would not be difficult to achieve by slowing down the landing process. However, long landing duration necessitates extensive use of fuel, which should also be avoided. As a summary, the main goal in the soft landing problem is to land the spacecraft as gently and as fast as possible. The model and the modeling process presented in this paper will serve as a modeling case to be used in teaching. Based on the soft landing model presented in this paper, we also developed a platform for simulation experiments. Our simulation-based discovery learning environment can be used to introduce dynamic complexity. It can also be used as an introductory control design tool for physics, engineering, and interested social sciences students.
The purpose of this study is to introduce system dynamics as a methodology to analyze intra-organizational innovation diffusion processes. Therefore, a purely algebraic model is replicated and analyzed in a system dynamics environment before it is extended by relaxing the restrictive assumption that intra-group diffusion and inter-group diffusion take place consecutively. The findings of this study suggest that the parallel occurrence of intra-group and inter-group diffusion can change the outcome of the diffusion process significantly. In addition, system dynamics is used to illustrate and analyze the complex dynamics of the diffusion process. The interplay between the self-reinforcing dynamics of intra-group diffusion and the balancing dynamics of inter-group diffusion is heavily influenced by the structure of the network between groups. The simulations suggest that adopter-dominated groups should be connected to each other, while non-adopter-dominated groups should be isolated in order to increase the probability and speed of successful innovation diffusions. Major limitations of the study are that only one network structure between groups was examined and that all groups are considered to be homogeneous.
Increasing concern regarding the cost, security, and environmental impact of fossil fuel energy use is driving research and investment towards developing the most strategic methods of converting biomass resources into energy. Analyses to date have examined theoretical limitations of biomass-to-energy through resource availability assessments, but have not thoroughly challenged competing tradeoffs of biomass conversion into liquid fuel versus electricity. Existing studies have focused on energy crops and cellulosic residues for biomass-to-energy inputs, however the conversion of these biomass resources is often less energetically efficient compared to fossil energy sources. Waste streams are beginning to be recognized as valuable biomass to energy resources. Municipal solid waste (MSW) is a low-cost waste biomass resource with a well-defined supply infrastructure and does not compete for land area or food supply, making it a potentially attractive renewable feedstock for energy conversion. The Waste Biomass to Energy Pathway model (WBEM) described here demonstrates a system dynamics approach to analyze the impact of converting MSW biomass to either bioelectricity or liquid fuel. The WBEM incorporates macro-scale feedback from supply chain costs, energy sector impacts, and greenhouse gas (GHG) production within the competing pathways of MSW to 1) landfill, 2) electricity, and 3) liquid fuel within California.
This study empirically demonstrates that software firms in a niche market with relatively short-life cycle may experience a similar growth pattern that firm grows after a period of performance deficit. A system dynamics model is built to capture the essential interactions across industry- and firm-levels. It is found that the growth trajectory of worse-before-better offers an explanation of the high exit rate in software industry in which small and medium enterprises are the majority. Furthermore, it is also found that though activities of market development and service and activities of product development and enhancement were important in pursuing survival and growth, software firms with different attitudes towards growth emphasized differently on the these activities by different human resource management and allocation policies. In this paper, we argue, and show, that entrepreneurs attitude towards growth and his or her adopted growth strategies determine how worse to experience and the extent to grow. This is significant to system dynamists because it shift our attention from traditional growth dynamics exploration to the observation and explanation of why firms experience growth or failure differently. The difference of growth among software firms and its implications is deliberately discussed.
Customer lifetime value (CLV) is the core content of customer relationship management. With the increasingly fierce market competition, more and more enterprises realize the importance of maintaining long-term strategic partnership with customers. In this paper, we established a system dynamics model of CLV and use SF Company as an example. The model simulation results showed that the intensity of competition, price levels and investment levels all affect CLV. Reducing the intensity of competition can increase the CLV. More investment will raise service quality and then promote CLV. Reducing the price level increases CLV in the short term. However, in the long run, less income leads to less profit and less investment which can decrease CLV.