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AbstractThis chapter aims to identify the supply chain (SC) issues that can be considered “horizontal”, as they are cross–sectorial and faced by most companies operating both in production and distribution sectors, and to propose a set of policy recommendations that can support public and private organisations to promote and foster innovation and competitiveness of future European SCs. The definition of the Key Horizontal Issues (KHI) is the basis for developing 12 policy recommendations regarding infrastructure requirements, technological and organisational improvements and regulatory developments needed to set the stage for the European SCs for the future. Specifically, the policy recommendations entail assuring appropriate standards and legislation for European SCs; educating and training professionals for the future SCs; drafting of international agreements aiming at future European SCs; supporting and fostering incentives and funding schemes; promoting reference bodies for European SCs; and establishing infrastructure for fostering of future European SCs.

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Purpose This paper investigates the effect of the fit between supply and demand uncertainty (SDU) and supply chain responsiveness (SCR) (SC fit) on business and innovation performance in Brazilian companies. Design/methodology/approach The study presented an analysis carried out on an empirical study based on a sample of 150 manufacturing companies. Business and innovation performance of companies with different types of SC fit ( high-high and low-low fits) and misfit (positive and negative) are compared and discussed. Findings The results indicated that SC fit had a positive effect on both business and innovation performance. Further analyses suggested that companies with SC fit present similar business performance, independent of the level of SDU that characterizes the environment where they compete, while companies in environments with higher levels of uncertainty tend to present superior innovation performance. Companies with positive and negative misfit present similar performance. Originality/value An analysis of the literature showed that there is no consensus when it comes to the definitions and measurements of SC fit. The paper investigates the effects of SC fit on business and innovation performance, while previous empirical studies have mainly addressed its impact on financial performance. Moreover, this study compares the effects of two types of fit and two types of misfit and assesses SC fit in Brazilian manufacturing companies, analyzing the context of an under-researched reality.

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O presente artigo objetiva identificar estratégias metodológicas existentes no âmbito dos estudos métricos da informação que contribuem para o mapeamento de inovação. Em termos metodológicos, este estudo qualitativo de caráter exploratório e descritivo parte do levantamento da produção de conhecimento relacionada às tipologias dos estudos métricos na base de dados Web of Science, por meio da relação do termo inovação nas palavras-chave e keywords plus dos artigos. Os resultados destacam a aplicação da inovação nas metrias da informação no âmbito da altmetria, bibliometria, cientometria, patentometria e webometria.

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Weather conditions have a major impact on citizens’ daily mobility. Depending on weather conditions trips may be delayed, demand may be changed as well as the modal shift. These variations have a major impact on the use and operation of public transport, particularly in transport systems that operate close to capacity. However, the influence of weather conditions on transport demand is difficult to predict and quantify. For this purpose, an artificial neural network model – the Multilayer Perceptron – is used as a regression model to estimate the demand of urban public transport buses based on weather conditions. Transit bus ridership and weather conditions were collected along a year from a medium-size European metropolitan area (Oporto, Portugal) and linked under the assumption that individuals choose the travel mode based on the weather conditions that are observed during the departure hour, the hour before and two hours before. The transit ridership data were also labelled according to the hour, day of the week, month, and whether there was a strike and/or holiday or not. The results demonstrate that it is possible to predict the demand of public transport buses using the weather conditions observed two hours before with low error for the entire network (MAE = 143 and RMSE = 322). The use of weather conditions allow to decreases the error of the prediction by ~8% for the entire network. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

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Public opinion is nowadays a valuable data source for many sectors. In this study, we analysed the transportation sector using messages extracted from Twitter. Contrasting with the traditional surveying methods that are high-cost and inefficient used in transportation sector, social media are popular sources of crowdsensing. This work used BERT embeddings, an unsupervised pre-trained model released in 2018, to classify travel-related terms using tweets collected from three distinct cities: New York, London, and Melbourne. In order to understand if a simple model can have a good performance, we used unigrams. A list of 24 travel-related words was used to classify the messages. Popular words are train, walk, car, station, street, and avenue. Between 3% to 5% of all messages are classified as traffic-related, while along the typical working hours of the day the values is around 5-6%. A high model performance was obtained, with precision and accuracy higher than 0.80 and 0.90, respectively. The results are consistent for all the three cities assessed. © 2021 IEEE.

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