Scalability Planning for Cloud-Based Manufacturing Systems

Dazhong Wu, David W. Rosen, Dirk Schaefer

Research output: Contribution to journalArticle

30 Citations (Scopus)

Abstract

Cloud-based 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 in-house manufacturing capacity
to third-party 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 cost-effective manufacturing scalability in practice by combining inhouse
manufacturing and crowdsourcing in a CBM setting.
Original languageEnglish
Pages (from-to)041007-1 - 041007-13
Number of pages13
JournalJournal of Manufacturing Science and Engineering
Volume137
Issue number4
Early online date8 Jul 2015
DOIs
Publication statusPublished - Aug 2015

Keywords

  • Cloud-based manufacturing
  • scalability
  • simulation
  • stochastic Petri nets

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