R&D Strategy Model in Power Plant Equipment's Manufacturing Industries in Order to Achieve Business Objectives

Subject Areas : Strategic Management Researches

ali  namani seyghalan ¹ (Department of technology management, Science and Research branch, Islamic Azad University, Tehran, Iran.)
Abbas  Khamseh ² (Department of industrial management, Karaj branch, Islamic Azad University, Alborz, Iran)
taghi  torabi ³ (Department of Economics, Science and Research branch, Islamic Azad University, Tehran, Iran)

Received: 2019-06-05 Accepted : 2019-08-26 Published : 2020-05-21

Keywords: R&D, R&D strategy, Power Plant Equipment's Manufacturing Industries, Business objectives,

Abstract :

Power plant equipment's manufacturing industries need to move beyond the boundaries of knowledge and technology, due to the use of advanced technologies in the production processes. What facilitates this movement is research and development in these companies. The present study seeks to develop a dynamic model of research and development strategy to achieve business objectives using the dynamic system modeling method as a powerful tool to analyze the factors affecting the system and analyze the relationships between them and predict future system behavior based on pre-defined scenarios. The causal loops have been extracted and modeled with the viewpoint of experts in the power industry, and Vensim software has been used for modeling. In this study, three scenarios are considered to increase the achievement of R&D strategy objectives. In the baseline scenario, it is assumed that the values of the decision parameters and levers are consistent with the past trend. In optimistic scenario, R&D budget and external collaborations will improve and the organization is more successful in attracting external funding and support for R&D projects, Also, in this scenario, the quality of project management is improved by reducing project execution time and project failure rate. In the pessimistic scenario, the above variables change in the opposite direction. The simulation results show that the 20% change in most of the variables will affect more than 20%, indicating the effectiveness of the change.

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