Abstract
The position of customer order decoupling point (CODP) has been one of the hot topics in the field of mass customization. With the constant changing in customer requirements, an uncertain production environment arises from the interaction of many variable factors including variety, quantity, time, etc. This results in the need of a better CODP adjustment model and strategy in a customized manufacturing systems with multiple decoupling points. Driven by dynamic environments and customer requirements, the paper presents a multi-CODP positioning adjustment system to adapt to dynamic environments and proposes activation models and an algorithm to implement it. After the adjustment of CODP, the system will diagnose whether or not there are inconsistencies in the old and new knowledge based on an expert system. If there are inconsistencies, it will regain consistency by adjusting the rules to ensure the scientificity of the dynamic multi-CODP positioning adjustment model. Finally, combined with the examples of turbine production in the machinery factory, the multi-CODP positioning adjustment model has been validated.
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References
Hedenstierna, P., Ng, H.C.: Dynamic implications of customer order decoupling point positioning. J. Manuf. Technol. Manag. 22(8), 1032–1042 (2011)
Verdouw, C.N., Beulens, A.J.M., Bouwmeester, D.: Modeling demand-driven chain networks using multiple CODPs. In: Proceedings APMS’2006 Lean Business Systems and Beyond, Poland, Wroclaw, pp. 313–318 (2006)
Wang,Yu: Supply chain model based on multi-CODP in mass dynamic customization. In: International Conference on Information Management, Innovation Management and Industrial Engineering, pp. 252–255 (2008)
Wang, Y., Ling, P.: Production scheduling system based on multi-CODP and simulation in a mass customization context. J. Manag. Sci. (in Chinese). 21(4), 17–25 (2008)
Banerjee, A., Mukhopadhyay, S.K., Sarkar, B.: An intrinsic decoupling in managing supply chain syndrome. In: International Conference of Flexible Automation and Intelligent Manufacturing, pp. 418–425 (2010)
Sun, X.Y.: Positioning multiple decoupling points in a supply network. Int. J. Prod. Econ. 113(2), 943–956 (2008)
Kundu, S., McKay, A.: Selection of decoupling points in supply chains using a knowledge-based approach. Proceedings of the Institution of Mechanical Engineers. Part B: J. Eng. Manuf. 22(2), 1529–1549 (2005)
Banerjee, A., Sarkar, B., Mukhopadhyay, S.K.: Multiple decoupling point paradigms in a global supply chain syndrome: a relational analysis. Int. J. Prod. Res. 52(4), 1–15 (2011)
Wang, Y.: MC in customer order decoupling point positioning of expert systems. Comput. Integr. Manuf. Syst. 17(5), 924–934 (2011)
Risdiyono, P.K.: Design by customer:concept and applications. J. Intell. Manuf. 24, 295–311 (2013)
Fogliatto, F.S., da Silveira, G.J., Borenstein, D.: The mass customization decade: an updated review of the literature. Int. J. Prod. Econ. 138, 14–25 (2012)
EevaJärvenpää, Luostarinen, P.: Dynamic operation environment—towards intelligent adaptive production systems. In: 2011 IEEE International Symposium on Assembly and Manufacturing (ISAM), pp. 1–5 (2011)
Tien, J.M.: The next industrial revolution: Integrated services and goods. J. Syst. Sci. Syst. Eng. 21(3), 257–296 (2012)
Buffington, J.: Comparison of mass customization and generative customization in mass markets. Ind. Manag. Data Syst. 111(2), 41–62 (2011)
Jian, C.: Adapt System Operation and Management. Mechanical Industry Press, Beijing (2012)
Acknowledgments
The paper is supported by the project of National Natural Science Foundation, China (No. 71302153);the project of China Postdoctoral Science Foundation, China (No. 2014T70838); the Project of Natural Sciences of Guangdong Province, China (No. 2014A030313608); Technology R&D Program of Guangzhou, China (No. 201607010012).
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Wang, Y., Chen, Y. Multi-CODP adjustment model and algorithm driven by customer requirements in dynamic environments. Cluster Comput 19, 2119–2131 (2016). https://doi.org/10.1007/s10586-016-0661-y
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DOI: https://doi.org/10.1007/s10586-016-0661-y