Applying System Dynamics to Confront Complex Decision Making in R&D Systems

Research and development has always played a strategically important role in the survival and the growth of technology-based firms. R&D is positively linked to company performance measures such as sales, productivity and shareholder returns and in addition to this, facing increased competition, as a result of globalization the viability of many established firms is based on the efficiency of their R&D activities. Similar to other business models, R&D systems are characterized by complex structure and dynamic behavior. It's often difficult to predict or trace the consequences of certain decisions due to the existence of delays, non-linearities and causal feedback inherent to such systems, in other words, the effect of making a decision in one part of the R&D function can manifest itself in another part of the business system at an unexpected time. There's a widely held belief that System Dynamics is the only way for challenging the increasing complexity of decision making in general and the best solution to avoid unintended consequences. This paper investigates the links between complex decision making in R&D management by describing how a dynamic simulation model, which broadly characterizes the causal feedback structure and the dynamic behavior of a typical R&D system, can be built to increase R&D systems efficiency. In this paper, first we start with defining R&D and its different types, and highlighting the importance of R&D. Then systems are defined and we explore complexity and its different types within the systems. As the next step we provide an answer to the question, how decision making complexities are originated in R&D. Then we take a brief look at the history of system dynamics and we become familiar with system dynamics tools (i.e. Causal Loop Diagrams and Stock and Flow Diagrams). Finally we get into modeling complexities faced in R&D decision making by system dynamics approach, starting from basic models and ending with more complex ones