972 resultados para Manufacturing Execution Systems


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This article investigates whether (1) cross-functional integration within a firm and the use of information systems (IS) that support information sharing with external parties can enhance integration across the supply chain and wider networks and (2) whether collaboration with customers, suppliers and other external parties leads to increased supply chain performance in terms of new product development and introduction of new processes. Data from a high-quality survey carried out in Taiwan in 2009 were used, and appropriate econometric models were applied. Results show that the adoption of IS that enhance information sharing is vital not only for the effective communication with suppliers and with wider network members, but their adoption also has a direct effect across a firm's innovative effort. Cross-functional integration appears to matter only for the introduction of an innovative process. Collaboration with customers and suppliers affected a product's design and its overall features and functionality, respectively. © 2013 Copyright Taylor and Francis Group, LLC.

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DUE TO COPYRIGHT RESTRICTIONS ONLY AVAILABLE FOR CONSULTATION AT ASTON UNIVERSITY LIBRARY AND INFORMATION SERVICES WITH PRIOR ARRANGEMENT

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Discrepancies of materials, tools, and factory environments, as well as human intervention, make variation an integral part of the manufacturing process of any component. In particular, the assembly of large volume, aerospace parts is an area where significant levels of form and dimensional variation are encountered. Corrective actions can usually be taken to reduce the defects, when the sources and levels of variation are known. For the unknown dimensional and form variations, a tolerancing strategy is typically put in place in order to minimize the effects of production inconsistencies related to geometric dimensions. This generates a challenging problem for the automation of the corresponding manufacturing and assembly processes. Metrology is becoming a major contributor to being able to predict, in real time, the automated assembly problems related to the dimensional variation of parts and assemblies. This is done by continuously measuring dimensions and coordinate points, focusing on the product's key characteristics. In this paper, a number of metrology focused activities for large-volume aerospace products, including their implementation and application in the automation of manufacturing and assembly processes, are reviewed. This is done by using a case study approach within the assembly of large-volume aircraft wing structures.

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Parallel processing is prevalent in many manufacturing and service systems. Many manufactured products are built and assembled from several components fabricated in parallel lines. An example of this manufacturing system configuration is observed at a manufacturing facility equipped to assemble and test web servers. Characteristics of a typical web server assembly line are: multiple products, job circulation, and paralleling processing. The primary objective of this research was to develop analytical approximations to predict performance measures of manufacturing systems with job failures and parallel processing. The analytical formulations extend previous queueing models used in assembly manufacturing systems in that they can handle serial and different configurations of paralleling processing with multiple product classes, and job circulation due to random part failures. In addition, appropriate correction terms via regression analysis were added to the approximations in order to minimize the gap in the error between the analytical approximation and the simulation models. Markovian and general type manufacturing systems, with multiple product classes, job circulation due to failures, and fork and join systems to model parallel processing were studied. In the Markovian and general case, the approximations without correction terms performed quite well for one and two product problem instances. However, it was observed that the flow time error increased as the number of products and net traffic intensity increased. Therefore, correction terms for single and fork-join stations were developed via regression analysis to deal with more than two products. The numerical comparisons showed that the approximations perform remarkably well when the corrections factors were used in the approximations. In general, the average flow time error was reduced from 38.19% to 5.59% in the Markovian case, and from 26.39% to 7.23% in the general case. All the equations stated in the analytical formulations were implemented as a set of Matlab scripts. By using this set, operations managers of web server assembly lines, manufacturing or other service systems with similar characteristics can estimate different system performance measures, and make judicious decisions - especially setting delivery due dates, capacity planning, and bottleneck mitigation, among others.

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Parallel processing is prevalent in many manufacturing and service systems. Many manufactured products are built and assembled from several components fabricated in parallel lines. An example of this manufacturing system configuration is observed at a manufacturing facility equipped to assemble and test web servers. Characteristics of a typical web server assembly line are: multiple products, job circulation, and paralleling processing. The primary objective of this research was to develop analytical approximations to predict performance measures of manufacturing systems with job failures and parallel processing. The analytical formulations extend previous queueing models used in assembly manufacturing systems in that they can handle serial and different configurations of paralleling processing with multiple product classes, and job circulation due to random part failures. In addition, appropriate correction terms via regression analysis were added to the approximations in order to minimize the gap in the error between the analytical approximation and the simulation models. Markovian and general type manufacturing systems, with multiple product classes, job circulation due to failures, and fork and join systems to model parallel processing were studied. In the Markovian and general case, the approximations without correction terms performed quite well for one and two product problem instances. However, it was observed that the flow time error increased as the number of products and net traffic intensity increased. Therefore, correction terms for single and fork-join stations were developed via regression analysis to deal with more than two products. The numerical comparisons showed that the approximations perform remarkably well when the corrections factors were used in the approximations. In general, the average flow time error was reduced from 38.19% to 5.59% in the Markovian case, and from 26.39% to 7.23% in the general case. All the equations stated in the analytical formulations were implemented as a set of Matlab scripts. By using this set, operations managers of web server assembly lines, manufacturing or other service systems with similar characteristics can estimate different system performance measures, and make judicious decisions - especially setting delivery due dates, capacity planning, and bottleneck mitigation, among others.

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Reconfigurable HW can be used to build a hardware multitasking system where tasks can be assigned to the reconfigurable HW at run-time according to the requirements of the running applications. Normally the execution in this kind of systems is controlled by an embedded processor. In these systems tasks are frequently represented as subtask graphs, where a subtask is the basic scheduling unit that can be assigned to a reconfigurable HW. In order to control the execution of these tasks, the processor must manage at run-time complex data structures, like graphs or linked list, which may generate significant execution-time penalties. In addition, HW/SW communications are frequently a system bottleneck. Hence, it is very interesting to find a way to reduce the run-time SW computations and the HW/SW communications. To this end we have developed a HW execution manager that controls the execution of subtask graphs over a set of reconfigurable units. This manager receives as input a subtask graph coupled to a subtask schedule, and guarantees its proper execution. In addition it includes support to reduce the execution-time overhead due to reconfigurations. With this HW support the execution of task graphs can be managed efficiently generating only very small run-time penalties.

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Reconfigurable hardware can be used to build multi tasking systems that dynamically adapt themselves to the requirements of the running applications. This is especially useful in embedded systems, since the available resources are very limited and the reconfigurable hardware can be reused for different applications. In these systems computations are frequently represented as task graphs that are executed taking into account their internal dependencies and the task schedule. The management of the task graph execution is critical for the system performance. In this regard, we have developed two dif erent versions, a software module and a hardware architecture, of a generic task-graph execution manager for reconfigurable multi-tasking systems. The second version reduces the run-time management overheads by almost two orders of magnitude. Hence it is especially suitable for systems with exigent timing constraints. Both versions include specific support to optimize the reconfiguration process.

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Vision systems are powerful tools playing an increasingly important role in modern industry, to detect errors and maintain product standards. With the enlarged availability of affordable industrial cameras, computer vision algorithms have been increasingly applied in industrial manufacturing processes monitoring. Until a few years ago, industrial computer vision applications relied only on ad-hoc algorithms designed for the specific object and acquisition setup being monitored, with a strong focus on co-designing the acquisition and processing pipeline. Deep learning has overcome these limits providing greater flexibility and faster re-configuration. In this work, the process to be inspected consists in vialsâ pack formation entering a freeze-dryer, which is a common scenario in pharmaceutical active ingredient packaging lines. To ensure that the machine produces proper packs, a vision system is installed at the entrance of the freeze-dryer to detect eventual anomalies with execution times compatible with the production specifications. Other constraints come from sterility and safety standards required in pharmaceutical manufacturing. This work presents an overview about the production line, with particular focus on the vision system designed, and about all trials conducted to obtain the final performance. Transfer learning, alleviating the requirement for a large number of training data, combined with data augmentation methods, consisting in the generation of synthetic images, were used to effectively increase the performances while reducing the cost of data acquisition and annotation. The proposed vision algorithm is composed by two main subtasks, designed respectively to vials counting and discrepancy detection. The first one was trained on more than 23k vials (about 300 images) and tested on 5k more (about 75 images), whereas 60 training images and 52 testing images were used for the second one.

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The purpose of this paper is to propose a multiobjective optimization approach for solving the manufacturing cell formation problem, explicitly considering the performance of this said manufacturing system. Cells are formed so as to simultaneously minimize three conflicting objectives, namely, the level of the work-in-process, the intercell moves and the total machinery investment. A genetic algorithm performs a search in the design space, in order to approximate to the Pareto optimal set. The values of the objectives for each candidate solution in a population are assigned by running a discrete-event simulation, in which the model is automatically generated according to the number of machines and their distribution among cells implied by a particular solution. The potential of this approach is evaluated via its application to an illustrative example, and a case from the relevant literature. The obtained results are analyzed and reviewed. Therefore, it is concluded that this approach is capable of generating a set of alternative manufacturing cell configurations considering the optimization of multiple performance measures, greatly improving the decision making process involved in planning and designing cellular systems. (C) 2010 Elsevier Ltd. All rights reserved.

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Purpose - The purpose of this paper is to examine whether the level of logistics information systems (LIS) adoption in manufacturing companies is influenced by organizational profile variables, such as the company`s size, the nature of its operations and their subsectors. Design/methodology/approach - A review of the mainstream literature on US was carried out to identify the factors influencing the adoption of such information systems and also some research gaps. The empirical study`s strategy is based on a survey research in Brazilian manufacturing firms from the capital goods industry. Data collected were analyzed through Kruskall-Wallis and Mann Whitney`s non-parametric tests. Findings - The analysis indicates that characteristics such as the size of companies and the nature of their operations influence the levels of LIS adoption, whilst comparisons regarding the subsectors appeared to be of little influence. Originality/value - This is the first known study to examine the influence of organizational profiles such as size, nature of operations and subsector on the level of US adoption in manufacturing companies. Moreover, it is unique in portraying the Brazilian scenario on this topic and addressing the adoption of seven types of LIS in a single study.

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In this paper it is presented the theoretical background, the architecture (using the ""4+1"" model), and the use of the library for execution of adaptive devices, AdapLib. This library was created seeking to be accurate to the adaptive devices theory, and to allow its easy extension considering the specific details of solutions that employ this kind of device. As an example, it is presented a case study in which the library was used to create a proof of concept to monitor and diagnose problems in an online news portal.

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introducing a pharmaceutical product on the market involves several stages of research. The scale-up stage comprises the integration of previous phases of development and their integration. This phase is extremely important since many process limitations which do not appear on the small scale become significant on the transposition to a large one. Since scientific literature presents only a few reports about the characterization of emulsified systems involving their scaling-up, this research work aimed at evaluating physical properties of non-ionic and anionic emulsions during their manufacturing phases: laboratory stage and scale-up. Prototype non-ionic (glyceryl monostearate) and anionic (potassium cetyl phosphate) emulsified systems had the physical properties by the determination of the droplet size (D[4,3 1, mu m) and rheology profile. Transposition occurred from a batch of 500-50,000 g. Semi-industrial manufacturing involved distinct conditions: intensity of agitation and homogenization. Comparing the non-ionic and anionic systems, it was observed that anionic emulsifiers generated systems with smaller droplet size and higher viscosity in laboratory scale. Besides that, for the concentrations tested, augmentation of the glyceryl monostearate emulsifier content provided formulations with better physical characteristics. For systems with potassium cetyl phosphate, droplet size increased with the elevation of the emulsifier concentration, suggesting inadequate stability. The scale-up provoked more significant alterations on the rheological profile and droplet size on the anionic systems than the non-ionic. (C) 2008 Elsevier B.V. All rights reserved.

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We introduced a spectral clustering algorithm based on the bipartite graph model for the Manufacturing Cell Formation problem in [Oliveira S, Ribeiro JFF, Seok SC. A spectral clustering algorithm for manufacturing cell formation. Computers and Industrial Engineering. 2007 [submitted for publication]]. It constructs two similarity matrices; one for parts and one for machines. The algorithm executes a spectral clustering algorithm on each separately to find families of parts and cells of machines. The similarity measure in the approach utilized limited information between parts and between machines. This paper reviews several well-known similarity measures which have been used for Group Technology. Computational clustering results are compared by various performance measures. (C) 2008 The Society of Manufacturing Engineers. Published by Elsevier Ltd. All rights reserved.