Controllereship as an administrative unit to support industry managers in the operational and financial controll of organizations

O serviço de controladoria e os sistemas de controle interno são ferramentas básicas para a eficácia da gestão empresarial e tendem a contribuir para a continuidade da empresa, geração de recursos, emprego e renda e, consequentemente, para o desenvolvimento social. O objetivo desta pesquisa é identificar as principais atividades, sistemas de controle interno e artefatos de controladoria empregados por gestores no controle operacional e financeiro de indústrias. Embasam teoricamente este estudo, entre outros, os autores Borinelli (2006), Padoveze (2016), Crepaldi (2017), Martins (2018). O método empregado foi estudo de caso composto, de natureza empírica, do tipo descritivo-exploratório e as unidades de análise, três empreendimentos industriais com atividades produtivas distintas. Serviu de instrumento de coleta de dados entrevista semiestruturada. Os dados receberam tratamento qualitativo. Os resultados da pesquisa revelam que, mesmo não estando consolidada em uma estrutura administrativa específica, a controladoria ajuda na identificação de oportunidades de melhoria, na redução dos custos e maximização dos resultados financeiros.This research aims to identify the main activities, internal controls and Controllership artifacts that assist industry managers in the operational and financial control of these organizations. In this sense, the research was concerned with answering the following problem question: What are the main activities, internal controls and Controllership artifacts that assist the managers of the industries in the operational and financial control of these organizations? The study was justified by the significance of internal controls in the effectiveness of business management, which tends to provide the continuity of the Company, and the consequent social development arising from the generation of resources, employment and income. To support this research, Borinelli (2006), Padoveze (2016), Crepaldi (2017), Martins (2018) were cited as the main authors. As for the methodological procedures, it was characterized as a composite study, of an empirical nature, of the descriptive-exploratory type and had as units of analysis three industrial enterprises with distinct productive activities. For data collection, semi-structured interviews were used, whose data received qualitative treatment. The results of the research revealed that the Controllership, even though it is not consolidated in a specific administrative structure, is perceived as a facilitator in the identification of opportunities for improvement, in the reduction of costs and in the maximization of financial results. This research aims to identify the main activities, internal controls and Controllership artifacts that assist industry managers in the operational and financial control of these organizations. In this sense, the research was concerned with answering the following problem question: What are the main activities, internal controls and Controllership artifacts that assist the managers of the industries in the operational and financial control of these organizations? The study was justified by the significance of internal controls in the effectiveness of business management, which tends to provide the continuity of the Company, and the consequent social development arising from the generation of resources, employment and income. To support this research, Borinelli (2006), Padoveze (2016), Crepaldi (2017), Martins (2018) were cited as the main authors. As for the methodological procedures, it was characterized as a composite study, of an empirical nature, of the descriptive-exploratory type and had as units of analysis three industrial enterprises with distinct productive activities. For data collection, semi-structured interviews were used, whose data received qualitative treatment. The results of the research revealed that the Controllership, even though it is not consolidated in a specific administrative structure, is perceived as a facilitator in the identification of opportunities for improvement, in the reduction of costs and in the maximization of financial results

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

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