Document Type : Research Paper

Authors

1 Department of Industrial Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran.

2 Department of Management of Information Technology, Shahid Beheshti University, Tehran, Iran.

3 Department of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran.

Abstract

The purpose of this paper is to identify and classify the main factors implementing the Cloud Manufacturing Systems (CMS) in the internet service providers company by Fuzzy Cognitive Map (FCM) methodology. Through expert opinions, 20 main factors were identified and classified based on the importance and then a FCM approach was applied for obtaining the relationship between the factors, and all of the impact factors are outputs of expert opinion. The outcomes of the study highlighted those three factors, including customer scoring, R&D, and method development were the most important factors impressing the implementation CMSs. Present practices for implementation CMS are and the relationship between the main factors also impressing CMS by employing the FCM approach in the Iranian internet service provider company. The model obtained in this study guides the managers to identify and classify the important factors of the cloud manufacturing and finally implement it successfully.

Keywords

Main Subjects

  1. Akbaripour, H., Houshmand, M., Van Woensel, T., & Mutlu, N. (2018). Cloud manufacturing service selection optimization and scheduling with transportation considerations: mixed-integer programming models. The international journal of advanced manufacturing technology95(1), 43-70.
  2. Xu, X. (2012). From cloud computing to cloud manufacturing. Robotics and computer-integrated manufacturing28(1), 75-86.
  3. Carlucci, D., Renna, P., Materi, S., & Schiuma, G. (2020). Intelligent decision-making model based on minority game for resource allocation in cloud manufacturing. Management decision, 58(11), 2305-2325.
  4. Wang, X. V., & Xu, X. W. (2013). An interoperable solution for cloud manufacturing. Robotics and computer-integrated manufacturing29(4), 232-247.
  5. Mourtzis, D., & Vlachou, E. (2016). Cloud-based cyber-physical systems and quality of services. The TQM journal, 28(5), 704-733.
  6. Meier, M., Seidelmann, J., & Mezgár, I. (2010). ManuCloud: the next-generation manufacturing as a service environment. ERCIM news, (83), 33-34.
  7. Rauschecker, U., Meier, M., Muckenhirn, R., Yip, A. L. K., Jagadeesan, A. P., & Corney, J. (2011). Cloud-based manufacturing-as-a-service environment for customized products. eChallenges e-2011 Conference Proceedings, IIMC International Information Management Corporation, ITA. https://strathprints.strath.ac.uk/38573/
  8. Wang, X. V., & Wang, L. (2014). From cloud manufacturing to cloud remanufacturing: a cloud-based approach for WEEE recovery. Manufacturing letters2(4), 91-95.
  9. Kosko, B. (1996). Fuzzy engineering. Prentice-Hall, Inc.
  10. Wang, X. V., Wang, L., Mohammed, A., & Givehchi, M. (2017). Ubiquitous manufacturing system based on Cloud: A robotics application. Robotics and computer-integrated manufacturing45, 116-125.
  11. Wu, D., Rosen, D. W., Wang, L., & Schaefer, D. (2015). Cloud-based design and manufacturing: a new paradigm in digital manufacturing and design innovation. Computer-aided design59, 1-14.
  12. Li, W., Cai, Y. L., & Lu, W. F. (2013). A streaming technology of 3D design and manufacturing visualization information sharing for cloud-based collaborative systems. Cloud manufacturing(pp. 137-163). Springer, London.
  13. Zhang, Z., Li, X., Liu, Y., & Xie, Y. (2014). Distributed resource environment: a cloud-based design knowledge service paradigm. Cloud-based design and manufacturing (CBDM)(pp. 63-87). Springer, Cham.
  14. Wang, X. V., Givehchi, M., & Wang, L. (2017). Manufacturing system on the cloud: a case study on cloud-based process planning. Procedia Cirp63, 39-45.
  15. Chai, X., Zhang, Z., Li, T., Zhang, Y., & Hou, B. (2011, June). High-performance cloud simulation platform advanced research of cloud simulation platform. Proceedings of the 2011 grand challenges on modeling and simulation conference(pp. 181-186). Hague Netherlands: Society for Modeling & Simulation International.
  16. Gerhardter, A., & Ortner, W. (2013). Flexibility and improved resource utilization through cloud-based ERP systems: critical success factors of SaaS solutions in SME. Innovation and future of enterprise information systems(pp. 171-182). Springer, Berlin, Heidelberg.
  17. Yang, N., Li, D., & Tong, Y. (2012). A cloud computing-based ERP system under the cloud manufacturing environment. International journal of digital content technology and its applications6(23), 126.
  18. Cegielski, C. G., Jones‐Farmer, L. A., Wu, Y., & Hazen, B. T. (2012). Adoption of cloud computing technologies in supply chains: an organizational information processing theory approach. The international journal of logistics management, 23(2), 184-211.
  19. Leukel, J., Kirn, S., & Schlegel, T. (2011). Supply chain as a service: a cloud perspective on supply chain systems. IEEE systems journal5(1), 16-27.
  20. Wu, Y. U. N., Cegielski, C. G., Hazen, B. T., & Hall, D. J. (2013). Cloud computing in support of supply chain information system infrastructure: understanding when to go to the cloud. Journal of supply chain management49(3), 25-41.
  21. Oliveira, T., Thomas, M., & Espadanal, M. (2014). Assessing the determinants of cloud computing adoption: An analysis of the manufacturing and services sectors. Information & management51(5), 497-510.
  22. Subramanian, N., Abdulrahman, M. D., & Zhou, X. (2014). Integration of logistics and cloud computing service providers: cost and green benefits in the Chinese context. Transportation research part E: logistics and transportation review70, 86-98.
  23. Schrödl, H. (2012, April). Adoption of cloud computing in supply chain management solutions: a SCOR-Aligned assessment. Asia-Pacific web conference(pp. 233-244). Springer, Berlin, Heidelberg.
  24. Lu, Y., Xu, X., & Xu, J. (2014). Development of a hybrid manufacturing cloud. Journal of manufacturing systems33(4), 551-566.
  25. Wang, X. V., & Xu, X. W. (2013, June). Virtualize manufacturing capabilities in the cloud: requirements and architecture. International manufacturing science and engineering conference(Vol. 55461, p. V002T02A002). American Society of Mechanical Engineers.
  26. Susanto, A. B. (2008). Organizational readiness for change: a case study on change readiness in a manufacturing company in Indonesia. International journal of management perspective. https://docplayer.net/14230643-Organizational-readiness-for-change-a-case-study-on-change-readiness-in-a-manufacturing-company-in-indonesia.html
  27. Storkholm, M. H., Mazzocato, P., Tessma, M. K., & Savage, C. (2018). Assessing the reliability and validity of the Danish version of organizational readiness for implementing change (ORIC). Implementation science13(1), 1-7.
  28. Arazmjoo, H., & Rahmanseresht, H. (2019). A multi-dimensional meta-heuristic model for managing organizational change. Management decision, 58(3), 526-543.
  29. Nilsen, P. (2020). Making sense of implementation theories, models, and frameworks. Implementation Science 3.0(pp. 53-79). Springer, Cham.
  30. Mühlenfeld, A., Mayer, W., Maier, F., & Stumptner, M. (2008, July). Ontology-based process modeling and execution using STEP/EXPRESS. The 20th international conference on software engineering & knowledge engineering (pp. 935-940). Knowledge Systems Institute Graduate School.
  31. Kosko, B. (1986). Fuzzy cognitive maps. International journal of man-machine studies24(1), 65-75.
  32. Mourhir, A., Rachidi, T., & Karim, M. (2015, August). Employing fuzzy cognitive maps to support environmental policy development. 2015 IEEE international conference on fuzzy systems (FUZZ-IEEE)(pp. 1-8). IEEE.
  33. Papageorgiou, E. I., & Stylios, C. D. (2008). Fuzzy cognitive maps. Handbook of granular computing123, 755-775.
  34. Baykasoglu, A., Durmusoglu, Z. D., & Kaplanoglu, V. (2011). Training fuzzy cognitive maps via extended great deluge algorithm with applications. Computers in industry62(2), 187-195.
  35. Papageorgiou, E. I., Stylios, C. D., & Groumpos, P. P. (2003). An integrated two-level hierarchical system for decision making in radiation therapy based on fuzzy cognitive maps. IEEE transactions on biomedical engineering50(12), 1326-1339.
  36. Papageorgiou, E. I., & Salmeron, J. L. (2014). Methods and algorithms for fuzzy cognitive map-based modeling. Fuzzy cognitive maps for applied sciences and engineering(pp. 1-28). Springer, Berlin, Heidelberg.
  37. Papageorgiou, E. I., Stylios, C. D., & Groumpos, P. P. (2003). An integrated two-level hierarchical system for decision making in radiation therapy based on fuzzy cognitive maps. IEEE transactions on biomedical engineering50(12), 1326-1339.
  38. Papageorgiou, E. I. (2011, June). Review study on fuzzy cognitive maps and their applications during the last decade. In 2011 IEEE international conference on fuzzy systems (FUZZ-IEEE 2011)(pp. 828-835). IEEE.
  39. Schneider, M., Shnaider, E., Kandel, A., & Chew, G. (1998). Automatic construction of FCMs. Fuzzy sets and systems93(2), 161-172.
  40. Salmeron, J. L., Vidal, R., & Mena, A. (2012). Ranking fuzzy cognitive map-based scenarios with TOPSIS. Expert systems with applications39(3), 2443-2450.
  41. López, C., & Salmeron, J. L. (2014). Modeling maintenance projects risk effects on ERP performance. Computer standards & interfaces36(3), 545-553.
  42. Samarasinghe, S., & Strickert, G. (2013). Mixed-method integration and advances in fuzzy cognitive maps for computational policy simulations for natural hazard mitigation. Environmental modelling & software39, 188-200.
  43. Gausemeier, J., Fink, A., & Schlake, O. (1998). Scenario management: an approach to develop future potentials. Technological forecasting and social change59(2), 111-130.
  44. Lopez, C., & Salmeron, J. L. (2014). Dynamic risks modelling in ERP maintenance projects with FCM. Information sciences256, 25-45.
  45. Salmeron, J. L. (2009). Augmented fuzzy cognitive maps for modelling LMS critical success factors. Knowledge-based systems22(4), 275-278.
  46. Mkrtchyan, L., & Ruan, D. (2010, November). Belief degree-distributed fuzzy cognitive maps. 2010 IEEE international conference on intelligent systems and knowledge engineering(pp. 159-165). IEEE.
  47. Shokouhyar, S., Pahlevani, N., & Sadeghi, F. M. M. (2019). Scenario analysis of smart, sustainable supply chain on the basis of a fuzzy cognitive map. Management research review, 43(4), 463-496.
  48. Kontogianni, A. D., Papageorgiou, E. I., & Tourkolias, C. (2012). How do you perceive environmental change? fuzzy cognitive mapping informing stakeholder analysis for environmental policy making and non-market valuation. Applied soft computing12(12), 3725-3735.
  49. Godet, M. (2000). The art of scenarios and strategic planning: tools and pitfalls. Technological forecasting and social change65(1), 3-22.
  50. Wilkinson, A. (2009). Scenarios practices: in search of theory. Journal of futures studies13(3), 107-114.