16 research outputs found
Strategic Outsourcing? The Philips Case in the LCD TV Market
This work seeks to assess the outsourcing process adopted by Philips for producing LCD televisions. The authors used the Resource-based Theory and the Transaction Cost Theory for analyzing the case. Based on industry data and interviews with ten of the company’s senior executives, this study sought to understand how the decision making process was carried out and how it affected the company's activities in the field of LCD televisions. Philips has lost its competitiveness in the Brazilian market – it went from being the leader in 2006 to fourth place in 2009, both as far as regards its sales volume and value. The executives pointed out that the LCD panel is an important resource and that its supply by third parties represented a high transaction cost. The results illustrate the complementarity that exists between the RBT and TCT theories in a competitive environment that has few players
Estratégia de Suprimentos: Um Quase Experimento Sobre Quantidade e Localização dos Fornecedores
Objective: Evaluate the impact of a second supply source on price, quality and stock levels at the purchasing company. Relevance: The supply strategy defines how many suppliers an item should have and where it should be located. If working with a single supplier may bring relational benefits, working with multiple sources may reduce opportunism risks. If a local supplier reaps benefits on account of its proximity, a global supplier may result in lower costs. Methodology: The quasi-experimental method was used to follow-up a purchasing policy change at an auto parts company, which took on a second supply source for low complexity items. The research collected data on costs, quality and stock coverage for 1,480 items between 2007 and 2012, analysing new supplier’s entrance and location. Results: Introducing a second supplier reduced the price, especially when a new international supplier was introduced. Decreases in quality or increases in stock coverage were not observed.Objetivo: Avaliar o impacto da entrada de uma segunda fonte de fornecimento no preço, na qualidade e nos níveis de estoque da empresa compradora. Relevância: A estratégia de suprimentos define quantos fornecedores um item deve ter e qual deve ser a sua localização. Trabalhar com um fornecedor traz benefícios relacionais; trabalhar com múltiplas fontes reduz riscos de oportunismo. Um fornecedor local alavanca benefícios pela proximidade; um global resulta em menores custos. Metodologia: O método quase experimental foi usado para acompanhar a alteração da política de compras de uma empresa de autopeças, que passou a adotar uma segunda fonte de suprimentos. A pesquisa coletou dados de custo, qualidade e tempo de cobertura de estoque para 1480 itens entre 2007 e 2012, analisando a entrada de novo fornecedor e sua localização. Resultados: A adoção de um segundo fornecedor reduziu o preço, principalmente na inserção de novo fornecedor internacional. Não foi observada piora da qualidade ou cobertura de estoques
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