Abstract
In the Internet era, the industrial Internet platform has become a bridge between customers and enterprises. In addition, with the continuous improvement of customers’ living standards, customers not only meet the basic needs for the functionality of products, but also yearn to participate in the production process of products and interactive experience activities with enterprises while pursuing high quality. In view of the difficulties in identifying and capturing the dynamically changing needs of customer- product interaction whole life cycle under the new environment of industrial Internet, and the problem of large amount and redundancy of demand data, this paper develops a set of models and methods covering the identification, definition, acquisition and parsing of customer needs, which provides theoretical support and decision-making basis for enterprises to accurately capture and predict customer needs, excavates potential market customers of enterprises, and realizes value-added of the value chain. Moreover, the methodology of personalized requirements for CILC proposed in this paper is helpful to the transformation of enterprise manufacturing model from mass manufacturing to mass customization and even to mass personalization. It can reduce enterprise production costs, and improve customer satisfaction and service experience.
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References
Alhayani, B. S. A., & Ilhan, H. (2021). Visual sensor intelligent module based image transmission in industrial manufacturing for monitoring and manipulation problems. Journal of Intelligent Manufacturing, 32(2), 597–610. https://doi.org/10.1007/s10845-020-01590-1
Cai, R., Lin, Y., Li, H., Zhu, Y., Tang, X., Weng, Y., et al. (2021). Wowtao: A personalized pottery-making system. Computers in Industry, 124, 103325. https://doi.org/10.1016/j.compind.2020.103325
Chen, P., Liu, M., Chen, C., & Shang, X. (2019). A battery management strategy in microgrid for personalized customer requirements. Energy, 189, 116245. https://doi.org/10.1016/j.energy.2019.116245
Cheng, X., Yuan, M., Xu, L., Zhang, T., Jia, Y., Cheng, C., & Chen, W. (2016). Big data assisted customer analysis and advertising architecture for real estate. In 16th International Symposium on Communications and Information Technologies (ISCIT), Qingdao
Chiu, M. C., Huang, J. H., Gupta, S., & Akman, G. (2021). Developing a personalized recommendation system in a smart product service system based on unsupervised learning model. Computers in Industry, 128(10), 103421. https://doi.org/10.1016/j.compind.2021.103421
Ding, S., Wang, Z., Wu, D., & Olson, D. L. (2017). Utilizing customer satisfaction in ranking prediction for personalized cloud service selection. Decision Support Systems, 93, 1–10. https://doi.org/10.1016/j.dss.2016.09.001
Dou, R., Lin, D., Nan, G., & Lei, S. (2018). A method for product personalized design based on prospect theory improved with interval reference. Computers & Industrial Engineering, 125, 708–719. https://doi.org/10.1016/j.cie.2018.04.056
Gang, X., Wang, F. Y., Nyberg, T. R., Shang, X., Zhou, M., Zhen, S., et al. (2017). From mind to products: towards social manufacturing and service. IEEE/CAA Journal of Automatica Sinica, 5(1), 47–57. https://doi.org/10.1109/JAS.2017.7510742
Gao, M., Jin, C., Qian, W., et al. (2014). Real-time and personalized search over a microblogging system. Computer Journal, 57(9), 1281–1295. https://doi.org/10.1093/comjnl/bxt108
Gedik, B., & Ling, L. (2007). Protecting location privacy with personalized k-anonymity: architecture and algorithms. IEEE Transactions on Mobile Computing, 7(1), 1–18. https://doi.org/10.1109/TMC.2007.1062
Grewal, D., Kroschke, M., Mende, M., Roggeveen, A. L., & Scott, M. L. (2020). Frontline cyborgs at your service: how human enhancement technologies affect customer experiences in retail, sales, and service settings. Journal of Interactive Marketing. https://doi.org/10.1016/j.intmar.2020.03.001
Hsu, G., Kovács, B., & Koak, Z. (2019). Experientially diverse customers and organizational adaptation in changing demand landscapes: A study of US cannabis markets, 2014–2016. Strategic Management Journal, 40(13), 2214–2241. https://doi.org/10.1002/smj.3078
Huang, E. Y., & Lin, C. Y. (2005). Customer-oriented financial service personalization. Industrial Management & Data Systems, 105(1/2), 26–44. https://doi.org/10.1108/02635570510575171
Ingrao, C., Scrucca, F., Matarazzo, A., Arcidiacono, C., & Zabaniotou, A. (2020). Freight transport in the context of industrial ecology and sustainability: Evaluation of uni- and multi-modality scenarios via life cycle assessment. The International Journal of Life Cycle Assessment, 26, 127–142. https://doi.org/10.1007/s11367-020-01831-8
Jie, X., Xing, T., & Schaar, M. (2015). Personalized Course Sequence Recommendations. IEEE Transactions on Signal Processing, 64(20), 5340–5352. https://doi.org/10.1109/TSP.2016.2595495
Kim, D. Y., Park, J. W., Baek, S., Park, K. B., Kim, H. R., Park, J. I., et al. (2020). A modular factory testbed for the rapid reconfiguration of manufacturing systems. Journal of Intelligent Manufacturing, 31(3), 661–680. https://doi.org/10.1007/s10845-019-01471-2
Lin, H., Chen, Y., & Yang, Y. (2018). Cluster analysis of automobile innovative users based on interactive innovation value. Mathematical Problems in Engineering, 2018(5320645), 1–10. https://doi.org/10.1155/2018/5320645
Liu, P., Wang, K., Yang, K., Chen, H., Zhao, A., Xue, Y., et al. (2020). An aesthetic measurement approach for evaluating product appearance design. Mathematical Problems in Engineering. https://doi.org/10.1155/2020/1791450
Liu, Z., Guo, S., Wang, L., Du, B., & Pang, S. (2019). A multi-objective service composition recommendation method for individualized customer: Hybrid MPA-GSO-DNN model. Computers & Industrial Engineering, 128, 122–134. https://doi.org/10.1016/j.cie.2018.12.042
Liu, Z., Wang, L., Li, X., & Pang, S. (2021). A multi-attribute personalized recommendation method for manufacturing service composition with combining collaborative filtering and genetic algorithm. Journal of Manufacturing Systems, 58, 348–364. https://doi.org/10.1016/j.jmsy.2020.12.019
Ming, G., Jin, C., Qian, W., & Gong, X. (2014). Real-time and personalized search over a microblogging system. Computer Journal, 57(9), 1281–1295. https://doi.org/10.1093/comjnl/bxt108
Park, K. T., Lee, J., Kim, H. J., & Sang, D. N. (2020). Digital twin-based cyber physical production system architectural framework for personalized production. The International Journal of Advanced Manufacturing Technology, 106(5), 1787–1810. https://doi.org/10.1007/s00170-019-04653-7
Peng, Q. J., Liu, Y. H., Zhang, J., & Gu, P. H. (2018). Personalization for massive product innovation using open architecture. Chinese Journal of Mechanical Engineering, 31(1), 34. https://doi.org/10.1186/s10033-018-0239-0
Qasim, I., Awan, M., Ali, S., Khan, S., Mosleh, M., Alsanad, A., et al. (2022). Affinity propagation-based hybrid personalized recommender system. Complexity. https://doi.org/10.1155/2022/6958596
Qin, Z., & Lu, Y. (2021). Self-organizing manufacturing network: A paradigm towards smart manufacturing in mass personalization. Journal of Manufacturing Systems, 60, 35–47. https://doi.org/10.1016/j.jmsy.2021.04.016
Qiwei, L. U., Wang, C., Xiong, Y., Xia, H., Huang, W., & Ong, X. G. (2017). Personalized privacy-preserving trajectory data publishing. Chinese Journal of Electronics. https://doi.org/10.1049/cje.2017.01.024
Sabri-Laghaie, K., Fathi, M., Zio, E., & Mazhar, M. (2022). A novel reliability monitoring scheme based on the monitoring of manufacturing quality error rates. Reliability Engineering & System Safety, 217, 108065. https://doi.org/10.1016/j.ress.2021.108065
Saniuk, S., & Grabowska, S. (2021). The concept of cyber-physical networks of small and medium enterprises under personalized manufacturing. Energies, 14(17), 5273. https://doi.org/10.3390/en14175273
Silvestri, L., Forcina, A., Introna, V., Santolamazza, A., & Cesarotti, V. (2020). Maintenance transformation through Industry 4.0 technologies: A systematic literature review. Computers in Industry. https://doi.org/10.1016/j.compind.2020.103335
Sun, H., Xia, X., & Liu, B. (2020). Inventory lot sizing policies for a closed-loop hybrid system over a finite product life cycle. Computers & Industrial Engineering. https://doi.org/10.1016/j.cie.2020.106340
Tan, C., Chung, H., Barton, K., Hu, S. J., & Freiheit, T. (2020). Incorporating customer personalization preferences in open product architecture design. Journal of Manufacturing Systems, 56, 72–83. https://doi.org/10.1016/j.jmsy.2020.05.006
Wang, J., Xu, C., Zhang, J., Bao, J., & Zhong, R. (2020). A collaborative architecture of the industrial internet platform for manufacturing systems. Robotics and Computer-Integrated Manufacturing. https://doi.org/10.1016/j.rcim.2019.101854
Wang, S., & Zhang, Y. (2021). A credit-based dynamical evaluation method for the smart configuration of manufacturing services under Industrial Internet of Things. Journal of Intelligent Manufacturing, 32(4), 1091–1115. https://doi.org/10.1007/s10845-020-01604-y
Xu, Y., Landon, Y., Segonds, S., & Zhang, Y. (2017). A decision support model in mass customization. Computers & Industrial Engineering, 114, 11–21. https://doi.org/10.1016/j.cie.2017.09.046
Xu, Y., Thomassey, S., & Zeng, X. (2020). Optimization of garment sizing and cutting order planning in the context of mass customization. International Journal of Advanced Manufacturing Technology, 106(3), 3485–3503. https://doi.org/10.1007/s00170-019-04866-w
Yassine, A., & Souweid, S. (2021). Time-to-Market and Product Performance Tradeoff Revisited. IEEE Transactions on Engineering Management. https://doi.org/10.1109/TEM.2021.3081987
Yilma, B. A., Panetto, H., & Naudet, Y. (2021). Systemic formalisation of Cyber-Physical-Social System (CPSS): A systematic literature review. Computers in Industry. https://doi.org/10.1016/j.compind.2021.103458
Yoon, S., Um, J., Suh, S.-H., Stroud, I., & Yoon, J.-S. (2019). Smart Factory Information Service Bus (SIBUS) for manufacturing application: Requirement, architecture and implementation. Journal of Intelligent Manufacturing, 30(1), 363–382. https://doi.org/10.1007/s10845-016-1251-9
Zhang, X., Ming, X., Liu, Z., Qu, Y., & Yin, D. (2019a). State-of-the-art review of customer to business (C2B) model. Computers & Industrial Engineering, 132, 207–222. https://doi.org/10.1016/j.cie.2019.04.039
Zhang, Y., Tang, D., Zhu, H., Li, S., & Nie, Q. (2021a). A flexible configuration method of distributed manufacturing resources in the context of social manufacturing. Computers in Industry. https://doi.org/10.1016/j.compind.2021.103511
Zhang, Y., Wang, J., & Zhang, X. (2021b). Personalized sentiment classification of customer reviews via an interactive attributes attention model. Knowledge-Based Systems. https://doi.org/10.1016/j.knosys.2021b.107135
Zhang, Y., Zhang, G., Liu, Y., & Hu, D. (2017). Research on services encapsulation and virtualization access model of machine for cloud manufacturing. Journal of Intelligent Manufacturing, 28(5), 1109–1123. https://doi.org/10.1007/s10845-015-1064-2
Zhang, Y., Zhang, P., Tao, F., Liu, Y., & Zuo, Y. (2019b). Consensus aware manufacturing service collaboration optimization under blockchain based Industrial Internet platform. Computers & Industrial Engineering, 135, 1025–1035. https://doi.org/10.1016/j.cie.2019.05.039
Zheng, P., Xu, X., Yu, S., & Liu, C. (2017). Personalized product configuration framework in an adaptable open architecture product platform. Journal of Manufacturing Systems. https://doi.org/10.1016/j.jmsy.2017.03.010
Zhou, Q., & He, L. (2019). Research on customer satisfaction evaluation method for individualized customized products. International Journal of Advanced Manufacturing Technology, 104(9–12), 3229–3238. https://doi.org/10.1007/s00170-017-1192-2
Zkab, A., Dhw, B., & Num, B. (2020). Computational model for generating interactions in conversational recommender system based on product functional requirements. Data & Knowledge Engineering. https://doi.org/10.1016/j.datak.2020.101813
Acknowledgements
The authors would like to thank Producer Service Development Innovation Center of Shanghai Jiao Tong University, Shanghai Research Center for industrial Informatics, Shanghai Key Lab of Advanced manufacturing Environment, SJTUSME-COSMOPlat Joint Research Center for New Generation Industrial Intelligent Technology, and Startup Fund for Young Faculty at SJTU for the funding support to this research.
Funding
This work was supported by the National Natural Science Foundation of China [Grant Nos 71632008, 71971139]; National Key Research and Development Program of China [Grant No 2018YFF0213701]; and National Major Science and Technology Project [Grant No 2017-I-0007-0008].
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Zhang, X., Ming, X. A smart system of Customer- product Interaction Life Cycle (CILC) in industrial Internet era for mass personalization from industrial practice survey: identification, definition, acquisition and parsing. J Intell Manuf 35, 727–756 (2024). https://doi.org/10.1007/s10845-022-02070-4
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DOI: https://doi.org/10.1007/s10845-022-02070-4