Special Section Articles

Scalability Planning for Cloud-Based Manufacturing Systems

[+] Author and Article Information
Dazhong Wu

The G.W. Woodruff School
of Mechanical Engineering,
Georgia Institute of Technology,
Atlanta, GA 30332
e-mail: dwu42@gatech.edu

David W. Rosen

The G.W. Woodruff School
of Mechanical Engineering,
Georgia Institute of Technology,
Atlanta, GA 30332
e-mail: david.rosen@me.gatech.edu

Dirk Schaefer

The G.W. Woodruff School
of Mechanical Engineering,
Georgia Institute of Technology,
Atlanta, GA 30332
e-mail: dirk.schaefer@me.gatech.edu

1Corresponding author.

Contributed by the Manufacturing Engineering Division of ASME for publication in the JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING. Manuscript received October 8, 2014; final manuscript received March 25, 2015; published online July 8, 2015. Assoc. Editor: Lihui Wang.

J. Manuf. Sci. Eng 137(4), 040911 (Aug 01, 2015) (13 pages) Paper No: MANU-14-1511; doi: 10.1115/1.4030266 History: Received October 08, 2014; Revised March 25, 2015; Online July 08, 2015

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 in-house manufacturing and crowdsourcing in a CBM setting.

Copyright © 2015 by ASME
Your Session has timed out. Please sign back in to continue.


Wu, D., Greer, M. J., Rosen, D. W., and Schaefer, D., 2013, “Cloud Manufacturing: Strategic Vision and State-of-the-Art,” J. Manuf. Syst., 32(4), pp. 564–579. [CrossRef]
Ren, L., Zhang, L., Wang, L., Tao, F., and Chai, X., 2014, “Cloud Manufacturing: Key Characteristics and Applications,” Int. J. Comput. Integr. Manuf., pp. 1–15. [CrossRef]
Lu, Y., Xu, X., and Xu, J., 2014, “Development of a Hybrid Manufacturing Cloud,” J. Manuf. Syst., 33(4), pp. 551–566. [CrossRef]
Wu, D., Rosen, D. W., Wang, L., and Schaefer, D., 2015, “Cloud-Based Design and Manufacturing: A New Paradigm in Digital Manufacturing and Design Innovation,” Comput.-Aided Des., 59, pp. 1–14. [CrossRef]
Zhang, L., Luo, Y. L., Tao, F., Li, B. H., Ren, L., Zhang, X. S., Guo, H., Cheng, Y., Hu, A. R., and Liu, Y. K., 2014, “Cloud Manufacturing: A New Manufacturing Paradigm,” Enterp. Inf. Syst., 8(2), pp. 167–187. [CrossRef]
Wang, L., 2013, “Machine Availability Monitoring and Machining Process Planning Towards Cloud Manufacturing,” CIRP J. Manuf. Sci. Technol., 6(4), pp. 263–273. [CrossRef]
Ren, L., Zhang, L., Tao, F., Zhao, C., Chai, X., and Zhao, X., 2015, “Cloud Manufacturing: From Concept to Practice,” Enterp. Inf. Syst., 9(2), pp. 186–209. [CrossRef]
Wu, D., Rosen, D. W., Wang, L., and Schaefer, D., 2014, “Cloud-Based Manufacturing: Old Wine in New Bottles?” 47th CIRP Conference on Manufacturing Systems, Windsor, Canada, pp. 8–18. [CrossRef]
Wu, D. Z., Thames, J. L., Rosen, D. W., and Schaefer, D., 2013, “Enhancing the Product Realization Process With Cloud-Based Design and Manufacturing Systems,” ASME J. Comput. Inf. Sci. Eng., 13(4), p. 041004. [CrossRef]
2014, “Shapeways,” http://www.shapeways.com/
2014, “3DHubs,” http://www.3dhubs.com/
MFG, 2014, “MFG Overview,” http://www.mfg.com/
Manyika, J., Chui, M., Bughin, J., Dobbs, R., Bisson, P., and Marrs, A., 2013, “Disruptive Technologies: Advances That Will Transform Life, Business, and the Global Economy,” McKinsey Global Institute, San Francisco, CA, Report No. 6.
Witherell, P., Feng, S., Simpson, T. W., Saint John, D. B., Michaleris, P., Liu, Z.-K., Chen, L.-Q., and Martukanitz, R., 2014, “Toward Metamodels for Composable and Reusable Additive Manufacturing Process Models,” ASME J. Manuf. Sci. Eng., 136(6), p. 061025. [CrossRef]
Spicer, P., and Carlo, H. J., 2007, “Integrating Reconfiguration Cost Into the Design of Multi-Period Scalable Reconfigurable Manufacturing Systems,” ASME J. Manuf. Sci. Eng., 129(1), pp. 202–210. [CrossRef]
Jeong, N., and Rosen, D. W., 2014, “Microstructure Feature Recognition for Materials Using Surfacelet-Based Methods for Computer-Aided Design-Material Integration,” ASME J. Manuf. Sci. Eng., 136(6), p. 061021. [CrossRef]
Wang, W., and Koren, Y., 2012, “Scalability Planning for Reconfigurable Manufacturing Systems,” J. Manuf. Syst., 31(2), pp. 83–91. [CrossRef]
Putnik, G., Sluga, A., ElMaraghy, H., Teti, R., Koren, Y., Tolio, T., and Hon, B., 2013, “Scalability in Manufacturing Systems Design and Operation: State-of-the-Art and Future Developments Roadmap,” CIRP Ann.-Manuf. Technol., 62(2), pp. 751–774. [CrossRef]
Koren, Y., and Shpitalni, M., 2010, “Design of Reconfigurable Manufacturing Systems,” J. Manuf. Syst., 29(4), pp. 130–141. [CrossRef]
Spicer, P., Koren, Y., Shpitalni, M., and Yip-Hoi, D., 2002, “Design Principles for Machining System Configurations,” CIRP Ann.-Manuf. Technol., 51(1), pp. 275–280. [CrossRef]
Koren, Y., 2006, “General RMS Characteristics. Comparison With Dedicated and Flexible Systems,” Reconfigurable Manufacturing Systems and Transformable Factories, Springer, Berlin, pp. 27–45. [CrossRef]
Ghosh, S., 1995, “A Distributed Algorithm for Fault Simulation of Combinatorial and Asynchronous Sequential Digital Designs, Utilizing Circuit Partitioning, on Loosely-Coupled Parallel Processors,” Microelectron. Reliab., 35(6), pp. 947–967. [CrossRef]
Rys, M., 2011, “Scalable SQL,” Commun. ACM, 54(6), pp. 48–53. [CrossRef]
ElMaraghy, H. A., and Wiendahl, H.-P., 2009, “Changeability: An Introduction,” Changeable and Reconfigurable Manufacturing Systems, Springer, London, pp. 3–24. [CrossRef]
Tolio, T., Ceglarek, D., ElMaraghy, H., Fischer, A., Hu, S., Laperrière, L., Newman, S. T., and Váncza, J., 2010, “SPECIES—Co-Evolution of Products, Processes and Production Systems,” CIRP Ann.-Manuf. Technol., 59(2), pp. 672–693. [CrossRef]
Chiang, S.-Y., Kuo, C.-T., and Meerkov, S. M., 2000, “DT-Bottlenecks in Serial Production Lines: Theory and Application,” IEEE Trans. Rob. Autom., 16(5), pp. 567–580. [CrossRef]
Chiang, S.-Y., Kuo, C.-T., and Meerkov, S. M., 2001, “c-Bottlenecks in Serial Production Lines: Identification and Application,” Math. Probl. Eng., 7(6), pp. 543–578. [CrossRef]
Kuo, C.-T., Lim, J.-T., and Meerkov, S. M., 1996, “Bottlenecks in Serial Production Lines: A System-Theoretic Approach,” Math. Probl. Eng., 2(3), pp. 233–276. [CrossRef]
Chiang, S.-Y., Kuo, C.-T., and Meerkov, S. M., 1998, “Bottlenecks in Markovian Production Lines: A Systems Approach,” IEEE Trans. Rob. Autom., 14(2), pp. 352–359. [CrossRef]
Law, A. M., and McComas, M. G., 1987, “Simulation of Manufacturing Systems,” Proceedings of the 19th Conference on Winter Simulation, ACM, New York, pp. 631–643. [CrossRef]
Li, J., and Meerkov, S. M., 2000, “Bottlenecks With Respect to Due-Time Performance in Pull Serial Production Lines,” Math. Probl. Eng., 5(6), pp. 479–498. [CrossRef]
Lawrence, S. R., and Buss, A. H., 1995, “Economic Analysis of Production Bottlenecks,” Math. Probl. Eng., 1(4), pp. 341–363. [CrossRef]
Li, L., Chang, Q., and Ni, J., 2009, “Data Driven Bottleneck Detection of Manufacturing Systems,” Int. J. Prod. Res., 47(18), pp. 5019–5036. [CrossRef]
Li, L., 2009, “Bottleneck Detection of Complex Manufacturing Systems Using a Data-Driven Method,” Int. J. Prod. Res., 47(24), pp. 6929–6940. [CrossRef]
Li, L., Chang, Q., Ni, J., and Biller, S., 2009, “Real Time Production Improvement Through Bottleneck Control,” Int. J. Prod. Res., 47(21), pp. 6145–6158. [CrossRef]
Reisig, W., and Rozenberg, G., 1998, Lectures on Petri Nets. I: Basic Models: Advances in Petri Nets, Springer, Berlin.
Petri, C. A., 1980, “Introduction to General Net Theory,” Net Theory and Applications, Springer, London, pp. 1–19.
David, R., and Alla, H., 1994, “Petri Nets for Modeling of Dynamic Systems: A Survey,” Automatica, 30(2), pp. 175–202. [CrossRef]
Zhou, M. C., Dicesare, F., and Desrochers, A. A., 1989, “A Top-Down Approach to Systematic Synthesis of Petri Net Models for Manufacturing Systems,” 1989 IEEE International Conference on Robotics and Automation, Vol. 1–3, pp. 534–539. [CrossRef]
Zurawski, R., and Zhou, M., 1994, “Petri Nets and Industrial Applications: A Tutorial,” IEEE Trans. Ind. Electron., 41(6), pp. 567–583. [CrossRef]
Feldmann, K., Colombo, A. W., Schnur, C., and Stockel, T., 1999, “Specification, Design, and Implementation of Logic Controllers Based on Colored Petri Net Models and the Standard IEC 1131. Part II: Design and Implementation,” IEEE Trans. Control Syst. Technol., 7(6), pp. 666–674. [CrossRef]
Bonet, P., Lladó, C. M., Puijaner, R., and Knottenbelt, W. J., 2007, “pipe v2. 5: A Petri Net Tool for Performance Modeling,” 23rd Latin American Conference on Informatics CLEI 2007, pp. 50–62.
2014, “3DRobotics,” http://3drobotics.com/
2014, “willit3dprint,” http://www.willit3dprint.com


Grahic Jump Location
Fig. 1

Cloud-based cyber-physical manufacturing systems

Grahic Jump Location
Fig. 4

Material flow of the existing manufacturing system

Grahic Jump Location
Fig. 5

(a) A snapshot of 3D printing service communities in the world [11]. (b) A snapshot of the 3D printer community in New York. (c) Search for a printer in 3D Hubs.

Grahic Jump Location
Fig. 6

Sub-SPNs for modeling material flow in building the propeller and motor

Grahic Jump Location
Fig. 7

SPN for modeling the entire manufacturing system

Grahic Jump Location
Fig. 8

System capacity versus run length

Grahic Jump Location
Fig. 9

Actual capacity versus required capacity for the original manufacturing system

Grahic Jump Location
Fig. 10

New SPN model for the new manufacturing system

Grahic Jump Location
Fig. 11

Actual capacity versus required capacity for the new CBM system



Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In