We offer a theory of competitive process improvement to explain the process of how best business practices emerge through dynamic interactions between competing processes. Grounded on the history of the interaction between two distinctive competing processes, Mass Production System vs. Toyota Production System, we employ the lens of competitive dynamics to develop a formal model. Three insights emerged: for sustainable competitive advantage, (1) a firm needs to invest in explorative activities at an early and continuous fashion; (2) external competitive tension plays a vital role in managing internal tension of organizational learning; (3) a firm may commit perception biases when interpreting others learning (re)actions.
Identifying the effects of change in construction has been a topic of discussion and debate for several years, especially those changes that delay contractors and disrupt productivity.
In recent years, Taiwan government has offered incentives and supportive policies such as tax reduction with a hope to foster the development of domestic online game industry. However, domestically developed online games are failed to dominate the online game market. Over seventy percents of the market share is occupied by foreign games, especially those from Korea. In this paper, a system dynamics model is built to explore the growth and competition dynamics of the online game market. The model shows that multiple reinforcing feedback loops and limited market size together led to the rapid but temporal market growth. The market reached its limit so quickly that Taiwans domestic game developers lost the opportunity to grow because of unavoidable time delays in R&D capacity expansion and game development and commercialization process. The online game case shows that market growth and R&D expansion that contrast sharply in lead times could cause tough barriers that are far beyond late entrants abilities to conquer, even governmental support might hardly be useful.
Feedback dynamic complexity is an important feature of complex systems. Professor Jia Renan and his SD group began their study of the theory and application of SD feedback dynamic complexity analysis method since 1985, and proposed a series of approaches successively, which have constructed an approach system of SD feedback dynamic complexity analysis of complex systems. Four major functions of SD feedback dynamic complexity analysis were extracted in this paper, which are formulating feedback model for the system problem; constructing feedback model for Successful case; simulation; feedback loops calculation and management policy analysis. Many questions and further study on each of the four functions were respectively proposed. The paper is also a SD research summary of the group for nearly three decades. We believe that there will be many benefits for the system dynamics community in developing the method system of SD feedback dynamic complexity analysis.
This paper contrasts the tradeoffs of modeling the same dynamic problem at a micro scale and at a macro scale of analysis: discrete system simulation (DS) versus continuous system simulation or system dynamics (SD). Both are employed to model the influence of entertainment education on terrorist system decay, with implications for field application. Each method optimizes different design, scope/scale, data availability/accuracy, parameter settings, and system sensitivities. Whether the research served by the computer model is applied or theoretical, DS tends to be useful for understand low-level individual unit/step influences on system change over time, whereas SD tends to shine when a wide-angle focus upon sociological/aggregate change is required.