With the changes in the world economic environment, the vigorous development of network information and sensor technology, the combination of traditional manufacturing and advanced technology has produced tremendous changes. After decades of development, intelligent manufacturing has gradually become popular among people, and has achieved amazing results in a large number of enterprise practices. The main purpose of this paper is to study the optimization of equipment maintenance and spare parts management in automobile intelligent manufacturing. This article is mainly based on the WBS work decomposition method, combined with the product life cycle theory, ABC classification method, demand forecasting method, inventory management method, etc., described and introduced in detail and properly. Experiments show that C-type parts (1/3 of the parts are wire harness connectors) only account for 5% of the entire sales. Therefore, in the case of limited resources, C-type spare parts can be managed in a differentiated manner according to needs, such as a small amount of stock, or by referring to past experience to order C-type spare parts with less than 5 quantities.

Chenpeng Pang. Management Optimization of Equipment Maintenance and Spare Parts for Automobile Intelligent Manufacturing Enterprises. International Journal of Frontiers in Engineering Technology (2022), Vol. 4, Issue 5: 33-41. https://doi.org/10.25236/IJFET.2022.040507.

[1] Dahane M., Sahnoun M., Bettayeb B., et al. Impact of spare parts remanufacturing on the operation and maintenance performance of offshore wind turbines: a multi-agent approach. Journal of Intelligent Manufacturing, 2017, 28(7):1531-1549.

[2] Israel E F., Albrecht A., Frazzon E M., et al. Operational Supply Chain Planning Method for Integrating Spare Parts Supply Chains and Intelligent Maintenance Systems. IFAC-PapersOnLine, 2017, 50(1):12428-12433.

[3] Regal T., Pereira C E. Ontology for Conceptual Modelling of Intelligent Maintenance Systems and Spare Parts Supply Chain Integration. IFAC-PapersOnLine, 2018, 51(11):1511-1516.

[4] A, Andrew, S, et al. Development of an intelligent decision making tool for maintenance planning using fuzzy logic and dynamic scheduling. International Journal of Information Technology, 2020, 12(1):27-36.

[5] Rasay H., Naderkhani F., Azizi F. Opportunistic maintenance integrated model for a two-stage manufacturing process. The International Journal of Advanced Manufacturing Technology, 2022, 119(11-12):8173-8191.

[6] Leiber D., Eickholt D., Vuong A T., et al. Simulation-based layout optimization for multi-station assembly lines. Journal of Intelligent Manufacturing, 2021, 33(2):537-554.

[7] Hammadi S., He Rr Ou B. Energetic equipment maintenance logistics: Towards a lean approach. Journal of Engineering and Applied Sciences, 2018, 13(11):4188-4192.

[8] Mourtzis D., Angelopoulos J., Boli N. Maintenance assistance application of Engineering to Order manufacturing equipment: A Product Service System (PSS) approach - ScienceDirect. IFAC-PapersOnLine, 2018, 51(11):217-222.

[9] Wakiru J M., Pintelon L., Muchiri P N., et al. A simulation-based optimization approach evaluating maintenance and spare parts demand interaction effects. International Journal of Production Economics, 2019, 208(FEB.):329-342.

[10] Sharma P., Kulkarni M S., Yadav V. A simulation based optimization approach for spare parts forecasting and selective maintenance. Reliability Engineering & System Safety, 2017, 168(dec.):274-289.

[11] Dmitriev O N., Novikov S V. Economic Optimization of the Modular Structure of Complex Objects. Russian Engineering Research, 2019, 39(6):503-506.

[12] Bernabei G., F Costantino, Palagi L., et al. An Integer Black-Box Optimization Model for Repairable Spare Parts Management. International Journal of Information Systems and Supply Chain Management, 2021, 14(2):46-68.