Uma proposta de planejamento estratégico para implantação de uma empresa do setor de produções e eventos de porte regional / A strategic planning proposal for the implementation of a regional production and events company

Este artigo utiliza a Matriz SWOT como ferramenta estratégica em um estudo de caso sobre a viabilidade da abertura de uma empresa do setor de produções e eventos de porte regional no município fluminense de Campos dos Goytacazes. A matriz SWOT propõe um melhor alinhamento estratégico entre a necessidade do mercado e o serviço oferecido por uma empresa, sendo este a chave para um melhor posicionamento competitivo da empresa perante as concorrentes. Várias ferramentas podem ser utilizadas para a realização de um planejamento estratégico, a escolhida para tal foi a matriz SWOT e suas quatro variáveis e a ferramenta do BSC, uma vez que são mecanismos que possibilitam a análise interna e externa, os responsáveis conseguem elaborar estratégias para obter vantagem competitiva e o melhor desempenho organizacional, além de situar e localizar de forma real a empresa no meio atuante. Os objetivos do presente estudo é demonstrar como uma empresa gestora de eventos pode utilizar a matriz SWOT e o BSC como ferramentas estratégicas para embasar seus planos de negócios, auxiliando na identificação dos melhores cenários para abertura de uma unidade. Através da análise dos resultados do estudo de caso, o cenário aparentemente favorável, assim como a análise dos ambientes externos e internos à luz da análise SWOT encorajam a abertura de uma empresa do setor de produções e eventos de porte regional no município, uma vez que a demanda local é definida encorajando o investimento de capital

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost