Avaliação de decisões estratégicas sob incerteza profunda na indústria da manufatura aditiva : uma análise a partir do método Robust Decision Making (RDM)

Abstract

Strategic Decision Making under deep uncertainty is a relevant challenge to organizations. When the available information allows sound decision making based on predictions, traditional decision making tools based on maximum expected value can lead to the right decision. Under conditions of deep uncertainty, however, decision making based on predict-then-act approaches might mislead and build overconfidence. In the 3D printing industry, uncertainty is highly relevant. While some experts forecast that this industry will worth 21 billion dollars by 2020, other estimates point that this market can have an economic impact of 550 billion by 2025. This dissertation leverages system dynamics simulation, using the Robust Decision Making (RDM) approach as the analytical framework to evaluate 3D printing Systems Manufacturers’ strategic decisions. I extend an existing competitive dynamics model allowing it to take into account expiring patents dynamics, an important aspect of the 3D printing industry. Then, I test 54 different strategies under 200 different scenarios, highlighting the most robust strategies. Afterwards, I examine the vulnerabilities of a candidate strategy using machine learning algorithms. The experiments showed that aggressive strategies dominate their conservative counterparts, using robustness as a criteria. Also, the results do not lend support to open source Research and Development strategies. Finally, I discuss managerial implications to the 3D printing industry, and theoretical contributions to the Strategic Decision-Making literature.