Designing a Model for Assessing the Effect of Cloud Manufacturing on Supply Chain Agility

Document Type : Original Article

Authors

1 Student of Industrial Management, Faculty of Management and Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran

2 Professor, Department of Industrial Management, Islamic Azad University, Science and Research Branch, Islamic Azad University, Tehran, Iran

3 Associate Professor, Department of Industrial Management, Islamic Azad University, Shahri Branch, Islamic Azad University, Tehran, Iran

4 Assistant Professor, Department of Industrial Management, Islamic Azad University, Karaj Branch, Iran

Abstract

Cloud computing comes from a new paradigm in order to empower future manufacturing companies to be responsive, customizable and adaptable, and flexible in a word of agility. The main purpose of this research is to analyze the effect of cloud manufacturing on the agility and chaos of the supply chain, as well as to identify the factors affecting cloud manufacturing in the level of supply chain agility. In this study, the relationship between supply chain agility (with regard to empowerment and capabilities) And cloud computing in order to quantify the potential impact of ICT solutions on supply chain agility. The research type in this study is descriptive modeling. This is an applied and developmental study. Using a field study, interviews and a questionnaire with experts and experts were conducted to find the dimensions and components of the conceptual model of the research and their significance based on the fuzzy model (in the company Isako). The fuzzy Delphi method is used to refine the conceptual model and a mathematical model is presented based on ANFIS. The result of this research based on the model of organizational excellence (empowerment and results) approach is to identify the empowering components derived from the use of manufacturing technologies as well as components that are effective in the form of organizational agility (outcomes).
The result of this study could facilitate the investment decision on cloud manufacturing technologies and could provide a better understanding of how to use the effectiveness of cloud computing to develop the agility of the supply chain. Due to the increasing expansion of cloud manufacturing technologies as well as rapid changes in the business environment, and the quantitative and qualitative change in customer demand and the need to effectively meet customer demands, this research can be effective in structuring and intelligently selecting agility components from organizations.

Keywords


پیله‌وری، نازنین (1388) تبیین و ارائه الگوی ارزیابی چابکی در زنجیره‌های تأمین مبتنی بر سیستم‌های خبره، رساله دکتری دانشگاه آزاد واحد علوم و تحقیقات.
حسامی زند، حسام ؛ رجب‌زاده، علی؛ طلوعی، عباس (1388) بررسی مؤلفه‌های تأثیرگذار بر زنجیره تأمین (pscm) و طراحی مدل مفهومی مدیریت زنجیره تأمین چابک، فصلنامه پژوهشنامه بازرگانی، شماره 51، تابستان 1388، 123-161
اتابکی، آزاده (1395) شبیه‌سازی مدل تأثیر تولید ابری بر چابکی زنجیره تأمین، رساله کارشناسی ارشد، دانشگاه آزاد اسلامی واحد علوم و تحقیقات.
شادبخت، علیرضا (1393) تبیین و رتبه‌بندی ارزش‌های کسب‌وکاری رایانش ابری بر اساس دیدگاه خبرگان فناوری اطلاعات استان مازندران، دانشگاه آزاد اسلامی واحد تهران مرکز.
Lei Ren, Lin Zhang, Fei Tao, Chun Zhao, Xudong Chai & Xinpei Zhao (2015)Cloud manufacturing: from concept to practice, Enterprise Information Systems, 9:2, 186-209,DOI: 10.1080/ 17517575. 2013.839055.
Wu He & Lida Xu (2015) A state-of-the-art survey of cloud manufacturing,International Journal of Computer Integrated Manufacturing, 28:3, 239-250, DOI:10.1080/0951192X.2013.874595.
Biqing Huang & Chenghai Li & Chao Yin & Xinpei Zhao(2013), Cloud manufacturing service platform for small- and medium-sized enterprises, Int J Adv Manuf Technol (2013) 65:1261–1272,DOI 10.1007/s00170-012-4255-4.
Mingyang Wu1 & Tingyu Huo1 & Jianghua Ge(2015), Cutting process-based optimization model of machining feature for cloud manufacturing, Int J Adv Manuf Technol DOI 10.1007/s00170-015-7800-0.5.
Weidong Li; Jörn Mehnen (2015), Cloud Manufacturing Distributed Computing Technologies for Global and Sustainable Manufacturing, Springer Series in Advanced Manufacturing, DOI 10.1007/978-1-4471-4935-4 Springer London Heidelberg New York Dordrecht.
Dazhong Wu, Matthew John Greer, David W. Rosen, Dirk Schaefer, ReviewCloud manufacturing: Strategic vision and state-of-the-art(2013), The George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, 813 Ferst Drive, NW, Atlanta, GA 30332-0405, United Statesa.
Dazhong Wua, David W. Rosena, Lihui Wangb, Dirk Schaefera(2014), Cloud-Based Manufacturing: Old Wine in New Bottles?, ScienceDirec
Javad Jassbi, Giovanni di Orio, Diogo Barata, Jos´e Barata(2016), The Impact of Cloud Manufacturing on Supply Chain Agility, https://www. researchgate.net/ publication/265140299, DOI: 10.13140/2.1.4075.7121.
H. Sharifi and Z. Zhang, “A methodology for achieving agility in manufacturing organisations: An introduction,” International Journal ofProduction Economics, vol. 62, no. 1–2, pp. 7–22, May 1999.
F. Tao, L. Zhang, V. C. Venkatesh, Y. Luo, and Y. Cheng, “Cloud manufacturing: a computing and service-oriented manufacturing model,”Proceedings of the Institution of Mechanical Engineers, Part B: Journalof Engineering Manufacture, vol. 225, no. 10, pp. 1969–1976, Oct. 2011.
X. Xu, “From cloud computing to cloud manufacturing,” Robotics and Computer-Integrated Manufacturing, vol. 28, no. 1, pp. 75–86, Feb.2012.
F. Tao, Y. LaiLi, L. Xu, and L. Zhang, “FC-PACO-RM: a parallel method for service composition optimal-selection in cloud manufacturing system,”IEEE Transactions on Industrial Informatics, vol. 9, no. 4, pp.2023–2033, 2013.
W. Zhang and Y. Xu, “Implementation of agile supply chain information integration system in manufacturing industry based on service-oriented architecture and web service,” Advanced Materials Research, vol. 219-220, p. 1145–1148, May 2011.
J. Jassbi, S.M. Seyedhosseini, and N. Pilevari, “An adaptive neuro fuzzy inference system for supply chain agility evaluation,” International Journal of Industrial Engineering & Production Research, vol. 20, no. 4,pp. 187–196, Mar. 2010.
J. Jassbi, F. Mohamadnejad, and H. Nasrollahzadeh, “A fuzzy DEMATEL
framework for modeling cause and effect relationships of strategy map,” Expert Systems with Applications, vol. 38, no. 5, pp. 5967–5973,May 2011.
C.-W. Hsu, T.-C. Kuo, S.-H. Chen, and A. H. Hu, “Using DEMATELto develop a carbon management model of supplier selection in green supply chain management,” Journal of Cleaner Production, vol. 56, pp.164–172, Oct. 2013.
Gunasekaran, A. 1998. Agile manufacturing: enablers and implementation framework. International Journal of production economics 36(5): 1223-1274.
Jang, R. 1993. ANFIS: Adaptive Network-based Fuzzy Inference System. IEEE Transactions on systems man and Cybernetics.
Lee H, So KC (2000), The value of information sharing in two-level Supply Chain Management Science 46 (5): 626-643 Supply Chain Management and advanced planning: concepts, models, Stadtler, Hartmut (2005).
Pilevari, N., Seyed Hosseini, S.M.& Jassbi, J. 2008. Fuzzy Logic Supply Chain Agility Assessment Methodology IEEM Industrial Engineering, Singapore.
Power, D; Sohal, A(2005): critical success factor in agile supply chain management, Journal of Physical Distribution & Logistic Management, vol. 31, Nov 4, P. 247-205.
Sharifi, H; Zhang, Z. (1999): A methodology for Achieving agility in manufacturing organization, international journal of production economics, 69(1999), 7-22.
Yusuf. Y, Gunasekaron (2003) Agile Supply Chain Capabilities European Journal of Operation Search, Elsevier.
Zhang Z & Sharifi H (2000) A methodology for achieving agility in manufacturing organization. International Journal of operation and production, 20(4): 496-512.
Toloie. A., Zandehessami, H., "Process based agile supply chain model according to BPR", contemporary Engineering sciences, 2(3): 117-138, 220q.
Zhang, Q., Vonderembrse, M.A., Lim, J., 2002a. Value chain flexibility: a dichotomy of competence and capability. International Journal of Production Research 40 (3), 561-583.
Dazhong Wua,David W. Rosena, Lihui Wangb, Dirk Schaefer(2015), Cloud-based design and manufacturing: A new paradigm in digital manufacturing and design innovation. journal homepage: www. elsevier.com/locate/cad
Chituc, C., and F. Restivo. 2009. “Challenges and Trends in Distributed Manufacturing Systems: Are Wise Engineering Systems the Ultimate Answer?.” Second International Symposium on Engineering Systems, MIT, Cambridge, MA, June 15–17
Foster, R. S., A. Gupta, and S. Deshpande. 2002. “Evolution of the High-End Computing Market in the USA.” International Journal of Technology Management 24 (2): 274–295.
Foster, I., Y. Zhao, I. Raicu, and S. Lu. 2008. “Cloud Computing and Grid Computing 360-degree compared?” In Proceedings of Grid Computing Environments Workshop, Austin, TX, 1–10. Piscataway, NJ: IEEE Society Press.
Yusuf, Y. Y., M. Sarhadi, and A. Gunasekaran. 1999. “Agile Manufacturing: The Drivers, Concepts and Attributed.” International Journal of Production Economics 62 (1–2): 33–43.
Zhang, L., H. Guo, F. Tao, Y. L. Luo, and N. Si. 2010. “Flexible Management of Resource Service Composition in Cloud Manufacturing.” Proceedings of IEEE International Conference on Industrial Engineering & Engineering Management, 2278–2282, Macao, December 7–10.
Flammia, G. 2001. “Application Service Providers: Challenges and Opportunities.” IEEE Intelligent Systems and Their Applications 16 (1): 22–23.
He, W., and L. Xu. 2013. “Integration of Distributed Enterprise Applications: A Survey.” IEEE Transactions on Industry Informatics. doi:10.1109/ TII.2012.2189221.
Spicer, P., and H. J. Carlo. 2007. “Integrating Reconfiguration Cost Into the Design of Multi-Period Scalable Reconfigurable Manufacturing Systems.” Journal of Manufacturing Science and Engineering 129: 202.
Panetto, H., and A. Molina. 2008. “Enterprise Integration and Interoperability in Manufacturing Systems: Trends and Issues.” Computers in Industry 59 (7): 641–646.