Configuration of Logistics Activities across Life-Cycle of the Firms and Performance: Proposal of a Conceptual Model

In the last years, broad changes have contributed to the enhancement of the importance of the logistics as a source of competitive advantage, not just for increasing the satisfaction of the clients, but also for improving the performance of the firms. Nevertheless, the response functions of the firms to the referred changes are different according to the life-cycle stage that they face. The present paper aims to present a conceptual model that explores the connection between the life-cycle firm’s stages, and the standard profile of logistics activities, and the correspondent impact on performance.Life Cycle; Logistics; Performance

Application of Lean Methodologies in a Neurosurgery High Dependency Unit

This study aims to apply Lean methodologies at a neurosurgery high dependency unit (NHDU) for increasing safety and quality on the care delivered to acute neuropatients and to reduce time, steps, and distance travelled by nurses accessing life support equipment (LSE). The methodology used in this study is an action research, supported by a longitudinal mixed method approach with a one‐group within‐subjects pretest‐posttest experimental type design. Resulting in a high waste of time, steps, and distance travelled to reach them. After the application of Lean methodologies, distance, steps, and time travelled by Nurses were quite improved. Lean methodologies applied in NHDU contributed to improve the organization, availability, and accessibility of LSE by putting them at the point‐of‐use. Quality and safety of patient care were also improved by allowing almost immediate life support interventions. Resistance to change was the major limitation. The Lean philosophy empowers health facility managers with tools and methodologies that help them create health gains, implement a culture of continuous improvement of care and working environment, identify and eliminate barriers, and waste that limits the work of staff in providing quality services and saving lives. This chapter highlights the responsibility of health facility managers to properly organize health units to cope with emergency situations, by allowing immediate, efficient, and effective intervention of staff

Supply chain resilience: an empirical model

This research was funded by Fundação para a Ciência e Tecnologia (Project PTDC/EMEGIN/68400/2006 and Project MIT-Pt/EDAM-IASC/0033/2008). Helena Carvalho was supported by a PhD fellowship from Fundação para a Ciência e Tecnologia (SFRH/BD/43984/2008).This paper proposes a model for management of supply chain resilience. To this end thestructured content analysis of media news is used to analyze a sample constituted by sixty two documents containing evidences of seventy seven companies that were affected by the Japan 2011 earthquake. The sample provides evidences that companies failed to sustain their operations mainly because capacity shortages and material shortages. Also provides empirical evidence of twelve resilience practices to reduce the disturbance severity and therecovery time. Based on these findings four propositions were made and aggregated topropose a model for supply chain resilience management.publishersversionpublishe

A resposta logística às alterações no ambiente das empresas

As constantes alterações que se têm feito sentir no ambiente que rodeia as empresas têm-nas obrigado a uma procura, também ela constante, por novas formas de resposta. Nesta árdua tarefa, e porque cada vez mais as empresas se preocupam em colocar “o produto certo, no local certo e no momento certo”, a logística tem vindo a conquistar um lugar de destaque ao permitir dar uma resposta mais eficiente em direcção a uma maior optimização de fluxos e de processos. Assim sendo, com este trabalho, de índole teórico, pretende-se basicamente despertar ou reforçar o interesse da comunidade científica para esta nova área do saber, que por ser ainda algo nova, principalmente na Península Ibérica, merece por isso uma atenção especial.The numerous changes that firms are facing in their environment are forcing them for new answers. In this hard mission and because more than ever the customers demands for the right product, in the right place and in the right time the logistics has been reaching an important place inside firms. Through logistics firms can reach a great optimization of the flows and the processes that through put the supply chain. In this context the main objective of this article is to call for and reinforce the importance of logistics

Modelling tourism demand: a comparative study between artificial neural networks and the Box-Jenkins methodology

This study seeks to investigate and highlight the usefulness of the Artificial Neural Networks (ANN) methodology as an alternative to the Box-Jenkins methodology in analysing tourism demand. To this end, each of the above-mentioned methodologies is centred on the treatment, analysis and modelling of the tourism time series: “Nights Spent in Hotel Accommodation per Month”, recorded in the period from January 1987 to December 2006, since this is one of the variables that best expresses effective demand. The study was undertaken for the North and Centre regions of Portugal. The results showed that the model produced by using the ANN methodology presented satisfactory statistical and adjustment qualities, suggesting that it is suitable for modelling and forecasting the reference series, when compared with the model produced by using the Box-Jenkins methodology

Forecasting tourism demand with artificial neural networks

Tourism has been viewed as an important player for the economic redevelopment of certain rural regions because of the attraction of landscapes, mountain, and the interest in second-home or investment opportunities at lower prices (Jackson & Murphy, 2002). Even with tourism‟s potential for development at all levels, there have been very few studies regarding models for estimating the local impact of tourism (Jackson & Murphy, 2006). To enhance understanding of the nature of forecasting in tourism destinations it is valuable to study systematically the possible estimative of influence that tourism destination has on an area. The main objective of this study is to present a set of models for tourism destinations competitiveness, using the Artificial Neural Networks methodology. This study focuses on two Portuguese regions - North and Centre - as tourism destinations offering a large range of tourist products, that goes beyond the beach, the mountains, the thermals not forgetting the rural tourism that has growing in the last years. These tourism destinations offer an interesting alternative to the „mass tourism‟ and have become more competitive, since the last one is normally associated with negative environmental impacts

O impacto da variável Páscoa na previsão da procura turística

Com este estudo pretende-se analisar o impacto do feriado móvel da Páscoa na previsão da procura turística, para as regiões Norte e Centro de Portugal. De salientar que a série temporal “Dormidas Mensais registadas nos estabelecimentos hoteleiros”, considerada como significativa da actividade turística, devido às suas características, denota que os fenómenos influenciam de forma distinta a procura turística, nas regiões em estudo. Assim, tendo por base modelos não lineares, sustentados pela metodologia das Redes Neuronais Artificiais (RNA), vai-se verificar se os resultados sofreram alterações significativas antes e após a utilização da variável dummy Páscoa. A inclusão desta nova variável no modelo prende-se com o facto de se ter detectado, em estudos anteriores, alguns valores atípicos na série temporal, dormidas mensais nos estabelecimentos hoteleiros nas regiões em estudo, pelo que se tenciona captar esse efeito

Modelação da procura turística: um estudo comparativo entre redes neuronais artificiais e a metodologia de Box-Jenkins

The present research aims to explore and to evidence the utility of the methodology of Artificial Neural Networks (ANN) in the analysis of tourism demand as an alternative to the Box-Jenkins methodology. The first methodology has arising interest in the economic and business area since several researches have verified that methodology presents a valid alternative to classical methods of forecasting allowing giving answer to situations in which the traditional ones will be of difficult to apply (Thawornwong & Enke, 2004). According to Hill et al. (1996) and Hansen et al. (1999) ANN show capacity to improve the time-series forecasts through of additional information analysis decreasing their dimension and reducing their complexity. For that, each one of the referred methodologies focused in the treatment, analysis and modeling of the tourism time-series: Monthly Guest Nights in Hotels registered between January 1987 to December 2006, since it is one of the variables that better explain the effective tourism demand. The Study was performed for two regions of Portugal: North region and Centre region. Considering the results, and according to the Criteria of MAPE for model evaluation proposed by Lewis (1982), the ANN model presented acceptable statistical qualities and adjustments satisfied. Being so, it is adequate not only for the modelling but also to the prediction of times series, when compared to the model performed by Box- Jenkins methodology. We intended also to evaluate the performance and competiveness of the tourism destinations - North region and Center region of Portugal - by main origin markets and to analyse how it is distributed their portfolio of origin markets for the period of 1997 to 2006. The Market Share Analysis tool proposed by Faulkner (1997) was applied and it was observed an high dependency of the domestic market for both regions.O presente estudo pretende explorar e evidenciar a utilidade da metodologia das Redes Neuronais Artificiais como uma alternativa à metodologia de Box-Jenkins, na análise da procura turística. A primeira metodologia tem vindo a suscitar interesse na área das ciências económicas e empresariais, pois pelos trabalhos de investigação realizados tem-se verificado que a mesma apresenta uma alternativa válida a métodos clássicos de previsão, conseguindo dar resposta a situações que pelos métodos clássicos seriam de difícil tratamento (Thawornwong & Enke, 2004). Hill et al. (1996) e Hansen et al. (1999), referem que as ANN mostram capacidade para melhorar a previsão de séries temporais através da análise de informação adicional, diminuindo a sua dimensão e reduzindo a sua complexidade. Para tal, cada uma das metodologias referidas centrou-se no tratamento, análise e modelação da série temporal de turismo: “Dormidas Mensais nos Estabelecimentos Hoteleiros”, registadas no período de Janeiro de 1987 a Dezembro de 2006, uma vez que é uma das variáveis que melhor traduz a procura efectiva. O estudo foi realizado para as regiões Norte e Centro de Portugal. Os resultados obtidos, e tendo por base a classificação do MAPE proposto por Lewis (1982), revelaram que o modelo obtido, utilizando a metodologia das Redes Neuronais Artificiais, apresentou qualidades estatísticas e de ajustamento satisfatórias evidenciando ser adequado para a modelação e previsão da série de referência, quando comparado com o modelo produzido pela metodologia de Box-Jenkins. Pretendeu-se ainda, com este estudo, avaliar o desempenho e a competitividade dos destinos turísticos - Região Norte e Região Centro, de Portugal - por principais mercados emissores e analisar como se encontra distribuída a sua carteira de mercados emissores, para o período de 1997 a 2005. Utilizou-se para o efeito o instrumento de análise proposto por Faulkner (1997), tendo-se observado uma grande dependência do mercado interno, para ambas as regiões

Artificial neural networks versus Box Jenkins methodology in tourism demand analysis

Several empirical studies in the tourism area have been performed and published during the last decades. The researchers are unanimous upon considering that in the planning process, decisionmaking and control of the tourism sector, the forecast of the tourism demand assumes an important role. Nowadays, there is a great variety of methods for forecasting that have been developed and which can be applied in a set of situations presenting different characteristics and methodologies, going from simple approaches to more complex ones. In this context, the present study aims to explore and to evidence the usefulness of the Artificial Neural Networks methodology (ANN), in the analysis of the tourism demand, as an alternative to the Box-Jenkins methodology. ANN has been under attention in the area of business and economics since, in this field, it presents this methodology as a valid alternative to classical methods of forecasting allowing its application for problems in which the traditional ones would be difficult to use (Thawornwong & Enke, 2004). As referred by Hill et al. (1996) and Hansen et al. (1999), ANN shows 1 ability for improving time-series forecasts by mining additional information, diminishing their dimensionality, and reducing their complexity. In this way, for each methodology treatment, analysis and modeling of the tourism time-series: “Nights Spent in Hotel Accommodation per Month” registered between January 1987 and December 2006, was carried out since is one of the variables that better explains the effective tourism demand. The study was performed for the North and Center regions of Portugal. Considering the results, and according to the Criteria of MAPE for model evaluation in Lewis (1982), the ANN model presented an acceptable goodness of fit and good statistical properties and is, therefore, adequate for modelling and prediction of the reference time series, when compared to the results obtained by the methodology of Box-Jenkins