Customization is a critical success factor in the current business environment. Companies need to improve supply chain management for the design and manufacturing quality of their products to 'be compatible with the individuals ' needs most rapidly and at lowest cost possible. One of the most important components that foster fast and inexpensive customization is the integration among partners in the supply chain. In the context, collaboration in the supply chain is the first step for improved data exchange, strategic use of information and homogenization of systems used for effective communication. As part of the integrution mechanism, the Collaborative Planning, Forecasting and Replenishment (CPFR) Model is a valuable technological innovation tool to strategically, tactically, and operationally support the implementation of several types of transactions among the partners. This paper bears two objectives: presenting the CPFR Model as a technological innovation, describing interaction issues between strategic partners during that process and showing how Knowledge Management and CSCW applied to the CPFR Model can provide support to supply chain management, so that the partners in this chain can increase their customization capability and improve the design process.
Resumo A literatura que relaciona políticas públicas com a estrutura socioespacial urbana mostra que, ao concentrar investimentos em áreas de mais alta renda, o Estado reforça as desigualdades, em vez de mitigá-las. Conhecida como “causação circular”, essa dinâmica é geralmente estudada com base na comparação entre os bairros de uma cidade. Nesse tipo de análise, os bairros de classe média e média-alta emergem como áreas reiteradamente valorizadas pelos investimentos públicos, em detrimento dos bairros pobres da cidade, incluindo as favelas. Neste artigo, testamos a hipótese de que essa dinâmica não é exclusiva da escala da cidade, podendo ser observada também na escala do bairro. Para isso, analisamos a localização dos equipamentos e serviços públicos dentro das três maiores favelas da cidade do Rio de Janeiro, buscando correlacionar essa localização com a condição socioeconômica dos moradores. Utilizando os dados do Censo Demográfico 2010 agregados por setor censitário, esta análise mostra uma concentração de equipamentos e serviços nas áreas de mais alta renda de cada favela, sugerindo um processo análogo ao que é observado na cidade como um todo.
PurposeBrazil is struggling as the unemployment rate is 12.4% and nearly 13m people are unemployed. The fourth Industrial Revolution is advancing, and the country needs to consider how it will impact the labor market. This work explores the impact of automation on the Brazilian workforce to supply decision-makers with information about the subject.Design/methodology/approachThe authors converted the probability of computerization from the seminal work of Frey and Osborne to each of the more than 2,500 occupations in Brazil. They then crossed the automation probability with socioeconomic information about workers and companies available in the Brazilian Ministry of Labor Database.FindingsIn total, 60% of employment in Brazil is expected to be highly impacted by automation in the coming decades, with eight out of the ten occupations with the biggest workforce being highly automatable. Automation probability decreases as workers' education level increases, with the most significant difference between workers with higher education and those without it. The results show other inequalities in the impact of automation: the higher the wage, the lower the automation probability of occupations; the bigger the company, the lower the automation index; and workers from 16 to 24 years old have considerably higher chances of being automated.Originality/valueThis work is the first to study, in the context of the fourth Industrial Revolution, the impact of automation in Brazil with a socioeconomic analysis.
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