Purpose: Analysis of the production and storage capacity of soybeans and corn in Mato Grosso (Brazil) and its projections until the year 2022. Methodology/Approach: Data regarding Mato Grosso's soy and corn production, area planted, and storage and yield capacity were organized for the years 1995 to 2018. Exponential smoothing models were then used to preview the grain storage balance for the period between 2019 and 2022. Findings: Grain production is expected to grow 11.9%, while the increase in storage capacity is expected to be 9.7%. Therefore, the results showed that the gap between production and storage may increase by 118%, between the harvests of 2017/18 and 2021/22. Moreover, the deficit in storage capacity will represent 53% of the total harvest in 2021/22. Research Limitation/implication: Interferences in the analyzed variables, such as economic, environmental, or technological events, may affect the forecasts' outcomes. Originality/Value of paper: The estimation combines exponential smoothing models with a non-parametric resampling technique. KEYWORD: agribusiness, logistics bottlenecks, agriculture infrastructure. PREVISÃO DA CAPACIDADE DE ARMAZENAMENTO USANDO O MÉTODO DE SUAVIZAÇÃO EXPONENCIAL RESUMO Objetivo: Análise da produção e capacidade de armazenamento de soja e milho em Mato Grosso (Brasil) e suas projeções até o ano de 2022. Metodologia/Abordagem: Organizou-se os dados da produção área plantada, produtividade de soja e milho para o estado de Mato Grosso, bem como a capacidade de armazenamento, para o período entre 1995 e 2018. Posteriormente, modelos de suavização exponencial foram utilizados para prever o saldo de armazenagem de grãos no estado de 2019 a 2022. Conclusões: Espera-se um crescimento de 11,9% na produção de grãos e apenas 9,7% de incremento da capacidade de armazenagem, conforme projeções. Nesse sentido, os resultados evidenciaram que o distanciamento entre produção e armazenagem poderá crescer 118% entre a safra de 2017/18 e 2021/22. Ademais, o déficit será de 53% da safra total em 2021/22. Limitação/implicação da pesquisa: Intervenções nas variáveis analisadas, tais como eventos econômicos, ambientais, ou tecnológicos, poderão afetar o resultado das previsões. Originalidade/valor do trabalho: O trabalho combina os modelos de suavização exponencial com a técnica não paramétrica de reamostragem bootstrap.

Metro systems are high-capacity urban rail networks designed to provide fast, reliable, and efficient transportation. This article presents a comparative benchmarking study of six leading metro systems in Brazil and six prominent international cases, aiming to identify best practices and recurring challenges based on key operational, planning, design, governance, and performance indicators. The Brazilian systems analyzed are located in Rio de Janeiro, São Paulo, Belo Horizonte, Fortaleza, Recife, and Salvador, while the international cases include London, Paris, Tokyo, Berlin, New York, and Madrid. The methodology combined documentary research with technical analysis of public data sources, institutional reports, and performance indicators. The results reveal significant contrasts in network scale, operational efficiency, governance models, funding mechanisms, and integration with urban planning. São Paulo’s system stands out for its network robustness, automation, and consolidated monitoring framework, while other Brazilian cities face limitations in service coverage and financial sustainability. The international cases offer valuable insights into fare integration, the use of emerging technologies, and the application of performance metrics to foster more sustainable and efficient high-capacity urban transit systems. The findings provide relevant evidence to support policymakers, transport authorities, and urban planners in improving the planning, management, and sustainability of high-capacity urban transit systems.