816 resultados para facility logistics
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This document shows the operations per year for airports in South Carolina broken down by air taxi, local, itin, military and totals.
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This is a list of airports in South Carolina and the kinds of planes that are based there. It is broken down by facility name, FAA ID, type, single, multi, jet, helicopter, glider, military and ultralight.
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This is a list of airports in South Carolina and the kinds of planes that are based there. It is broken down by facility name, FAA ID, type, single, multi, jet, helicopter, glider, military and ultralight.
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La eliminación de barreras entre países es una consecuencia que llega con la globalización y con los acuerdos de TLC firmados en los últimos años. Esto implica un crecimiento significativo del comercio exterior, lo cual se ve reflejado en un aumento de la complejidad de la cadena de suministro de las empresas. Debido a lo anterior, se hace necesaria la búsqueda de alternativas para obtener altos niveles de productividad y competitividad dentro de las empresas en Colombia, ya que el entorno se ha vuelto cada vez más complejo, saturado de competencia no sólo nacional, sino también internacional. Para mantenerse en una posición competitiva favorable, las compañías deben enfocarse en las actividades que le agregan valor a su negocio, por lo cual una de las alternativas que se están adoptando hoy en día es la tercerización de funciones logísticas a empresas especializadas en el manejo de estos servicios. Tales empresas son los Proveedores de servicios logísticos (LSP), quienes actúan como agentes externos a la organización al gestionar, controlar y proporcionar actividades logísticas en nombre de un contratante. Las actividades realizadas pueden incluir todas o parte de las actividades logísticas, pero como mínimo la gestión y ejecución del transporte y almacenamiento deben estar incluidos (Berglund, 2000). El propósito del documento es analizar el papel de los Operadores Logísticos de Tercer nivel (3PL) como promotores del desempeño organizacional en las empresas colombianas, con el fin de informar a las MIPYMES acerca de los beneficios que se obtienen al trabajar con LSP como un medio para mejorar la posición competitiva del país.
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En esta tesis se presenta la influencia que tiene la Cultura Nacional en el desempeño Logístico de una Nación, en especial de los 123 primeros países evaluados por el Índice de Desempeño Logístico (LPI) realizado por el Banco Mundial. Este estudio se realiza a partir de los seis ítems evaluados en el LPI y de las seis Dimensiones Culturales de Hofstede, para relacionar estas calificaciones se empleará el programa estadístico SPSS Statistical Package for the Social Sciences obteniendo como resultado posibles correlaciones para probar la hipótesis del efecto de la cultura en el desempeño logístico y establecer patrones de comportamiento en los mejores países de cada correlación. De acuerdo a los resultados obtenidos de estas correlaciones, se encontró que si existe una relación entre la cultura nacional y el desempeño logístico de un país, al igual que las dimensiones culturales más influyentes para obtener dichos resultados y se identificaron los países que cuentan con condiciones sobresalientes que facilitan tener altas posiciones en el LPI.
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La gestione del fine vita dei prodotti è un argomento di interesse attuale per le aziende; sempre più spesso l’imprese non possono più esimersi dall’implementare un efficiente sistema di Reverse Logistics. Per rispondere efficacemente a queste nuove esigenze diventa fondamentale ampliare i tradizionali sistemi logistici verso tutte quelle attività svolte all’interno della Reverse Logitics. Una gestione efficace ed efficiente dell’intera supply chain è un aspetto di primaria importanza per un’azienda ed incide notevolmente sulla sua competitività; proprio per perseguire questo obiettivo, sempre più aziende promuovono politiche di gestione delle supply chain sia Lean che Green. L’obiettivo di questo lavoro, nato dalle esigenze descritte sopra, è quello di applicare un modello innovativo che consideri sia politiche di gestione Lean, che dualmente politiche Green, alla gestione di una supply chain del settore automotive, comprendente anche le attività di gestione dei veicoli fuori uso (ELV). Si è analizzato per prima cosa i principi base e gli strumenti utilizzati per l’applicazione della Lean Production e del Green supply chain management e in seguito si è analizzato le caratteristiche distintive della Reverse Logistics e in particolare delle reti che trattano i veicoli a fine vita. L’obiettivo finale dello studio è quello di elaborare e implementare, tramite l’utilizzo del software AMPL, un modello di ottimizzazione multi-obiettivo (MOP- Multi Objective Optimization) Lean e Green a una Reverse Supply Chain dei veicoli a fine vita. I risultati ottenuti evidenziano che è possibile raggiungere un ottimo compromesso tra le due logiche. E' stata effettuata anche una valutazione economica dei risultati ottenuti, che ha evidenziato come il trade-off scelto rappresenti anche uno degli scenari con minor costi.
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SRI has examined the organosolv (organic solvation) pulping of Australian bagasse using technology supplied by Ecopulp. In the process, bagasse is reacted with aqueous ethanol in a digester at elevated temperatures (between 150ºC and 200ºC). The products from the digester are separated using proprietary technology before further processing into a range of saleable products. Test trials were undertaken using two batch digesters; the first capable of pulping about 25 g of wet depithed bagasse and the second, larger samples of about 1.5 kg of wet depithed bagasse. From this study, the unbleached pulp produced from fresh bagasse did not have very good strength properties for the production of corrugated medium for cartons and bleached pulp. In particular, the lignin contents as indicated by the Kappa number for the unbleached pulps are high for making bleached pulp. However, in spite of the high lignin content, it is possible to bleach the pulp to acceptable levels of brightness up to 86.6% ISO. The economics were assessed for three tier pricing (namely low, medium and high price). The economic return for a plant that produces 100 air dry t/d of brownstock pulp is satisfactory for both high and medium pricing levels of pricing. The outcomes from the project justify that work should continue through to either pilot plant or upgraded laboratory facility.
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It is questionable whether activities like construction, including maintenance and repair, can be considered a single entity or industry - on the basis that different sectors of construction/maintenance use fundamentally distinct resource and skill bases. This creates a number of issues including the development of competition and reform policy. de Valance deployed the Structure-Conduct-Performance model (SCP) to delineate sectors of new/installation construction activity and, in doing so, proposes that there exists multiple market structures in a given project. The purpose of this paper is to apply the SCP model to a different sector of construction activity, that is air conditioning maintenance and test de Valance's proposition concerning the existence of multiple market structures in a supply chain but this time to a built facility. The research method combines secondary data concerning the "Structure" component of the SCP model and primary data with regard to the "Conduct" and "Performance" parts of the SCP model. The results provide further support (beyond de Valance's analysis of new/installation activity) that a sector system approach using the SCP model is a more effective way to analyse market structures in construction activity. This paper also supports de Valance's proposition concerning the existence of multiple market structures in a supply chain to a project/facility.
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Experience plays an important role in building management. “How often will this asset need repair?” or “How much time is this repair going to take?” are types of questions that project and facility managers face daily in planning activities. Failure or success in developing good schedules, budgets and other project management tasks depend on the project manager's ability to obtain reliable information to be able to answer these types of questions. Young practitioners tend to rely on information that is based on regional averages and provided by publishing companies. This is in contrast to experienced project managers who tend to rely heavily on personal experience. Another aspect of building management is that many practitioners are seeking to improve available scheduling algorithms, estimating spreadsheets and other project management tools. Such “micro-scale” levels of research are important in providing the required tools for the project manager's tasks. However, even with such tools, low quality input information will produce inaccurate schedules and budgets as output. Thus, it is also important to have a broad approach to research at a more “macro-scale.” Recent trends show that the Architectural, Engineering, Construction (AEC) industry is experiencing explosive growth in its capabilities to generate and collect data. There is a great deal of valuable knowledge that can be obtained from the appropriate use of this data and therefore the need has arisen to analyse this increasing amount of available data. Data Mining can be applied as a powerful tool to extract relevant and useful information from this sea of data. Knowledge Discovery in Databases (KDD) and Data Mining (DM) are tools that allow identification of valid, useful, and previously unknown patterns so large amounts of project data may be analysed. These technologies combine techniques from machine learning, artificial intelligence, pattern recognition, statistics, databases, and visualization to automatically extract concepts, interrelationships, and patterns of interest from large databases. The project involves the development of a prototype tool to support facility managers, building owners and designers. This final report presents the AIMMTM prototype system and documents how and what data mining techniques can be applied, the results of their application and the benefits gained from the system. The AIMMTM system is capable of searching for useful patterns of knowledge and correlations within the existing building maintenance data to support decision making about future maintenance operations. The application of the AIMMTM prototype system on building models and their maintenance data (supplied by industry partners) utilises various data mining algorithms and the maintenance data is analysed using interactive visual tools. The application of the AIMMTM prototype system to help in improving maintenance management and building life cycle includes: (i) data preparation and cleaning, (ii) integrating meaningful domain attributes, (iii) performing extensive data mining experiments in which visual analysis (using stacked histograms), classification and clustering techniques, associative rule mining algorithm such as “Apriori” and (iv) filtering and refining data mining results, including the potential implications of these results for improving maintenance management. Maintenance data of a variety of asset types were selected for demonstration with the aim of discovering meaningful patterns to assist facility managers in strategic planning and provide a knowledge base to help shape future requirements and design briefing. Utilising the prototype system developed here, positive and interesting results regarding patterns and structures of data have been obtained.
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Kindergartens in China offer structured full-day programs for children aged 3-6. Although formal schooling does not commence until age 7, the mathematics program in kindergartens is specifically focused on developing young children’s facility with simple addition and subtraction. This study explored young Chinese children’s strategies for solving basic addition facts as well as their intuitive understanding of addition via interview methods. Results indicate a strong impact that teacher-directed teaching methods have on young children’s cognitions in relation to addition.
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Experience plays an important role in building management. “How often will this asset need repair?” or “How much time is this repair going to take?” are types of questions that project and facility managers face daily in planning activities. Failure or success in developing good schedules, budgets and other project management tasks depend on the project manager's ability to obtain reliable information to be able to answer these types of questions. Young practitioners tend to rely on information that is based on regional averages and provided by publishing companies. This is in contrast to experienced project managers who tend to rely heavily on personal experience. Another aspect of building management is that many practitioners are seeking to improve available scheduling algorithms, estimating spreadsheets and other project management tools. Such “micro-scale” levels of research are important in providing the required tools for the project manager's tasks. However, even with such tools, low quality input information will produce inaccurate schedules and budgets as output. Thus, it is also important to have a broad approach to research at a more “macro-scale.” Recent trends show that the Architectural, Engineering, Construction (AEC) industry is experiencing explosive growth in its capabilities to generate and collect data. There is a great deal of valuable knowledge that can be obtained from the appropriate use of this data and therefore the need has arisen to analyse this increasing amount of available data. Data Mining can be applied as a powerful tool to extract relevant and useful information from this sea of data. Knowledge Discovery in Databases (KDD) and Data Mining (DM) are tools that allow identification of valid, useful, and previously unknown patterns so large amounts of project data may be analysed. These technologies combine techniques from machine learning, artificial intelligence, pattern recognition, statistics, databases, and visualization to automatically extract concepts, interrelationships, and patterns of interest from large databases. The project involves the development of a prototype tool to support facility managers, building owners and designers. This Industry focused report presents the AIMMTM prototype system and documents how and what data mining techniques can be applied, the results of their application and the benefits gained from the system. The AIMMTM system is capable of searching for useful patterns of knowledge and correlations within the existing building maintenance data to support decision making about future maintenance operations. The application of the AIMMTM prototype system on building models and their maintenance data (supplied by industry partners) utilises various data mining algorithms and the maintenance data is analysed using interactive visual tools. The application of the AIMMTM prototype system to help in improving maintenance management and building life cycle includes: (i) data preparation and cleaning, (ii) integrating meaningful domain attributes, (iii) performing extensive data mining experiments in which visual analysis (using stacked histograms), classification and clustering techniques, associative rule mining algorithm such as “Apriori” and (iv) filtering and refining data mining results, including the potential implications of these results for improving maintenance management. Maintenance data of a variety of asset types were selected for demonstration with the aim of discovering meaningful patterns to assist facility managers in strategic planning and provide a knowledge base to help shape future requirements and design briefing. Utilising the prototype system developed here, positive and interesting results regarding patterns and structures of data have been obtained.