Scalability Planning for Cloud Based Manufacturing SystemsSource: Journal of Manufacturing Science and Engineering:;2015:;volume( 137 ):;issue: 004::page 40911DOI: 10.1115/1.4030266Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: Cloudbased manufacturing (CBM) has recently been proposed as an emerging manufacturing paradigm that may potentially change the way manufacturing services are provided and accessed. In the context of CBM, companies may opt to crowdsource part of their manufacturing tasks that are beyond their existing inhouse manufacturing capacity to thirdparty CBM service providers by renting their manufacturing equipment instead of purchasing additional machines. To plan manufacturing scalability for CBM systems, it is crucial to identify potential manufacturing bottlenecks where the entire manufacturing system capacity is limited. Because of the complexity of manufacturing resource sharing behaviors, it is challenging to model and analyze the material flow of CBM systems in which sequential, concurrent, conflicting, cyclic, and mutually exclusive manufacturing processes typically occur. To address and further study this issue, we develop a stochastic Petri nets (SPNs) model to formally represent a CBM system, model and analyze the uncertainties in the complex material flow of the CBM system, evaluate manufacturing performance, and plan manufacturing scalability. We validate this approach by means of a delivery drone example that is used to demonstrate how manufacturers can indeed achieve rapid and costeffective manufacturing scalability in practice by combining inhouse manufacturing and crowdsourcing in a CBM setting.
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contributor author | Wu, Dazhong | |
contributor author | Rosen, David W. | |
contributor author | Schaefer, Dirk | |
date accessioned | 2017-05-09T01:20:26Z | |
date available | 2017-05-09T01:20:26Z | |
date issued | 2015 | |
identifier issn | 1087-1357 | |
identifier other | manu_137_04_040911.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl/handle/yetl/158706 | |
description abstract | Cloudbased manufacturing (CBM) has recently been proposed as an emerging manufacturing paradigm that may potentially change the way manufacturing services are provided and accessed. In the context of CBM, companies may opt to crowdsource part of their manufacturing tasks that are beyond their existing inhouse manufacturing capacity to thirdparty CBM service providers by renting their manufacturing equipment instead of purchasing additional machines. To plan manufacturing scalability for CBM systems, it is crucial to identify potential manufacturing bottlenecks where the entire manufacturing system capacity is limited. Because of the complexity of manufacturing resource sharing behaviors, it is challenging to model and analyze the material flow of CBM systems in which sequential, concurrent, conflicting, cyclic, and mutually exclusive manufacturing processes typically occur. To address and further study this issue, we develop a stochastic Petri nets (SPNs) model to formally represent a CBM system, model and analyze the uncertainties in the complex material flow of the CBM system, evaluate manufacturing performance, and plan manufacturing scalability. We validate this approach by means of a delivery drone example that is used to demonstrate how manufacturers can indeed achieve rapid and costeffective manufacturing scalability in practice by combining inhouse manufacturing and crowdsourcing in a CBM setting. | |
publisher | The American Society of Mechanical Engineers (ASME) | |
title | Scalability Planning for Cloud Based Manufacturing Systems | |
type | Journal Paper | |
journal volume | 137 | |
journal issue | 4 | |
journal title | Journal of Manufacturing Science and Engineering | |
identifier doi | 10.1115/1.4030266 | |
journal fristpage | 40911 | |
journal lastpage | 40911 | |
identifier eissn | 1528-8935 | |
tree | Journal of Manufacturing Science and Engineering:;2015:;volume( 137 ):;issue: 004 | |
contenttype | Fulltext |