| Popis: |
The automotive industry in Brazil has grown considerably in recent decades in terms both of amount of investments and creation of new products, employment and income generation, service offerings and the development of new technologies. Automotive industry companies are inserted in an extremely competitive market and subject to the oscillations in economy, therefore, facing the current marketing challenges, it’s indispensable that companies and organizations stay updated in their definitions of priorities and in the allocation of available resources. Seeking this competitive context, the study contemplates the prospection of future scenarios in an automotive industry company in the southern Brazil, aiming the construction of scenarios pointing to events that, positively or negatively, may cause impacts to the company in the next five years, implementing strategies that minimize socioeconomic instabilities. For this, the method described by Grumbach was used, which is based on the software Puma version 4.0 as a support to choose the best decision making, identifying possibilities and alternatives for evolution and presenting trends through meetings with experts in the subject studied. The first ten scenarios consisted of the occurrence or non-occurrence of the events were analyzed and interpreted, being classified in the most probable, trend and ideal scenario. It was also evidenced the degree of influence and dependence between the events, which generated the cross impact matrix. In addition, possible future scenarios were constructed and analyzed demonstrating that the company studied has potential events that could serve as alternatives for the sector. However, no ideal scenario was identified for the next five years, due to the uncertain environment caused by the political/economic crisis experienced in the country. In any event, the study provided the definition of strategies in turbulent environments in which the company is inserted and contributed to the understanding of the uncertainties experienced, showing the degree of influence and dependence between the events. |
