Review and Prospect of Data Mechanism Fusion in Value Chain Collaboration

doi:10.3901/JME.2025.13.001

Abstract

Abstract: Value chain collaboration is an important component of Industry 5.0 and a key to realizing the production concept of human-oriented, sustainable, and flexible in Industry 5.0. Data and mechanism, as important technical supports for the integration of the new generation of information technology and value chain, have become key elements in value chain collaboration management. However, there are still problems such as data silos and business barriers in current value chain collaboration, which hinder information sharing and value co-creation among enterprises on the value chain and become a huge challenge for the current transformation of manufacturing to Industry 5.0. In order to provide theoretical support for the application of data mechanism integration in value chain collaboration, this paper summarizes the research progress of value chain collaboration theory and data mechanism integration method, and explores the engineering application of data mechanism integration in five parts, including product design collaboration, production scheduling collaboration, manufacturing assembly collaboration, equipment operation and maintenance collaboration, and production service collaboration. Finally, the challenges faced by data mechanism integration in value chain collaboration are discussed, and the future development direction is looked into. It is hoped that relevant work can provide reference and inspiration for scholars to further carry out theoretical and technical research and engineering application of data mechanism integration in value chain collaboration.

Key words: value chain, collaborative management, data mechanism fusion, industry 5.0

CLC Number:

TH122

LU Chengyu, HONG Zhaoxi, ZHANG Zhifeng, GU Daqiang, NIE Jie, FENG Yixiong, TAN Jianrong. Review and Prospect of Data Mechanism Fusion in Value Chain Collaboration[J]. Journal of Mechanical Engineering, 2025, 61(13): 1-19.

References

[1] ORDIERES-MERÉ J，GUTIERREZ M，VILLALBA-DÍEZ J. Toward the industry 5.0 paradigm： Increasing value creation through the robust integration of humans and machines[J]. Computers in Industry，2023，150：103947.
[2] LENG J，SHA W，WANG B，et al. Industry 5.0： prospect and retrospect[J]. Journal of Manufacturing Systems，2022，65：279-295.
[3] XU X，LU Y，VOGEL-HEUSER B，et al. Industry 4.0 and industry 5.0—Inception，conception and perception[J]. Journal of Manufacturing Systems，2021，61：530-535.
[4] LENG J，ZHU X，HUANG Z，et al. Unlocking the power of industrial artificial intelligence towards industry 5.0，insights，pathways，and challenges[J]. Journal of Manufacturing Systems，2024，73：349-363.
[5] YAO X，MA N，ZHANG J，et al. Enhancing wisdom manufacturing as industrial metaverse for industry and society 5.0[J]. Journal of Intelligent Manufacturing，2024，35(1)：235-255.
[6] 胡炳涛，冯毅雄，密尚华，等. 面向核电装备的全生命周期价值链协同模式研究[J]. 机械工程学报，2022，58(13)：213-227. HU Bingtao，FENG Yixiong，MI Shanghua，et al. Research on the collaborative model of the whole life cycle value chain for nuclear power equipment[J]. Journal of Mechanical Engineering，2022，58(13)：213-227.
[7] 李智，董思睿. 科技金融、技术转移与制造业价值链攀升[J]. 科技进步与对策，2024：1-11. LI Zhi，DONG Sirui. Science and technology finance，technology transfer and the rise of the manufacturing value chain[J]. Science & Technology Progress and Policy，2024：1-11.
[8] 陈衍泰，吕祖庆，胡旭辉，等. 大数据分析能力、动态能力与制造企业商业模式创新——来自资源编排视角[J]. 科技进步与对策，2024，41(24)：107-117. CHEN Yantai，LÜ Zuqing，HU Xuhui，et al. Big data analytics capability，dynamic capability，and business model innovation in manufacturing enterprises，A resource orchestration perspective[J]. Science & Technology Progress and Policy，2024，41(24)：107-117.
[9] KARMAKER C L，BARI A B M M，ANAM Md Z，et al. Industry 5.0 challenges for post-pandemic supply chain sustainability in an emerging economy[J]. International Journal of Production Economics，2023，258：108806.
[10] DWIVEDI A，AGRAWAL D，JHA A，et al. Studying the interactions among Industry 5.0 and circular supply chain，Towards attaining sustainable development[J]. Computers & Industrial Engineering，2023，176：108927.
[11] PORTER M E. Technology and competitive advantage[J]. Journal of Business Strategy，1985，5(3)：60-78.
[12] HINES P. Integrated materials management，the value chain redefined[J]. The International Journal of Logistics Management，1993，4(1)：13-22.
[13] KANO L，TSANG E W K，YEUNG H W. Global value chains，a review of the multi-disciplinary literature[J]. Journal of International Business Studies，2020，51(4)：577-622.
[14] EPEDE M B，WANG D. Global value chain linkages，an integrative review of the opportunities and challenges for SMEs in developing countries[J]. International Business Review，2022，31(5)：101993.
[15] ANTRÀS P，CHOR D. Global value chains[J]. Handbook of International Economics，2022，5：297-376.
[16] OLSON E L. Perspective，the green innovation value chain，a tool for evaluating the diffusion prospects of green products[J]. Journal of Product Innovation Management，2013，30(4)：782-793.
[17] HUILING L，DAN L. Value chain reconstruction and sustainable development of green manufacturing industry[J]. Sustainable Computing，Informatics and Systems，2020，28：100418.
[18] HASAN M M，NEKMAHMUD M，YAJUAN L，et al. Green business value chain，A systematic review[J]. Sustainable Production and Consumption，2019，20：326-339.
[19] COUTO J，TIAGO T，GIL A，et al. It’s hard to be green，reverse green value chain[J]. Environmental Research，2016，149：302-313.
[20] VERMESAN O，FRIESS P，GUILLEMIN P，et al. IoT digital value chain connecting research，innovation and deployment[M]. Nord Jylland：River Publishers，2022.
[21] OLIVEIRA L，FLEURY A，FLEURY M T. Digital power，value chain upgrading in an age of digitization[J]. International Business Review，2021，30(6)：101850.
[22] MIAO Z. Digital economy value chain，concept，model structure，and mechanism[J]. Applied Economics，2021，53(37)：4342-4357.
[23] DI S，YANG N，DING Y，et al. Research on regional collaborative innovation model of manufacturing resources based on value chain[C]//2021 IEEE 5th Information Technology：Networking：Electronic and Automation Control Conference (ITNEC). Xi’an，China，IEEE，2021：1475-1479.
[24] ANDRES B，POLER R，SANCHIS R. A data model for collaborative manufacturing environments[J]. Computers in Industry，2021，126：103398.
[25] GUO Y，YU J，ALLAOUI H，et al. Lateral collaboration with cost-sharing in sustainable supply chain optimisation：a combinatorial framework[J]. Transportation Research Part E，Logistics and Transportation Review，2022，157：102593.
[26] KAUR H，SINGH S P，GARZA-REYES J A，et al. Sustainable stochastic production and procurement problem for resilient supply chain[J]. Computers & Industrial Engineering，2020，139：105560.
[27] UM K H，KIM S M. The effects of supply chain collaboration on performance and transaction cost advantage：the moderation and nonlinear effects of governance mechanisms[J]. International Journal of Production Economics，2019，217：97-111.
[28] LI J，ZENG X，LIU C，et al. A parallel Lagrange algorithm for order acceptance and scheduling in cluster supply chains[J]. Knowledge-Based Systems，2018，143：271-283.
[29] GUALANDRIS J，LONGONI A，LUZZINI D，et al. The association between supply chain structure and transparency：a large‐scale empirical study[J]. Journal of Operations Management，2021，67(7)：803-827.
[30] HLIOUI R，GHARBI A，HAJJI A. Joint supplier selection，production and replenishment of an unreliable manufacturing-oriented supply chain[J]. International Journal of Production Economics，2017，187：53-67.
[31] 余洋，曾强，孙毓方，等. 基于第三方云平台的汽车服务价值链多链协同模型[J]. 计算机集成制造系统，2023，29(1)：1-20. YU Yang，ZENG Qiang，SUN Yufang，et al. Multi-chain collaborative model of automotive service value chain based on third-party cloud platform[J]. Computer Integrated Manufacturing Systems，2023，29(1)：1-20.
[32] LIU X，DENG Q，GONG G，et al. Service-oriented collaboration framework based on cloud platform and critical factors identification[J]. Journal of Manufacturing Systems，2021，61：183-195.
[33] LINDSEY HALL K K，QI J，RICHEY R G，et al. Collaboration，feedback，and performance：supply chain insights from service-dominant logic[J]. Journal of Business Research，2022，146：385-397.
[34] WANG C，HU Q. Knowledge sharing in supply chain networks：effects of collaborative innovation activities and capability on innovation performance[J]. Technovation，2020，94：102010.
[35] KARNIADAKIS G E，KEVREKIDIS I G，LU L，et al. Physics-informed machine learning[J]. Nature Reviews Physics，2021，3(6)：422-440.
[36] 马费成，王文慧，孙玉姣，等. 数字产业化与产业数字化协同发展中的数据价值实现[J]. 信息资源管理学报，2024：1-12. MA Feicheng，WANG Wenhui，SUN Yujiao，et al. Realization of data value in the coordinated development of digital industrialization and industrial digitalization[J]. Journal of Information Resource Management，2024：1-12.
[37] WANG K，WANG P，XU C. Toward efficient automated feature engineering[C]//2023 IEEE 39th International Conference on Data Engineering (ICDE). 2023：1625-1637.
[38] 汪俊亮，高鹏捷，张洁，等. 制造大数据分析综述：内涵、方法、应用和趋势[J]. 机械工程学报，2023，59(12)：1-16. WANG Junliang，GAO Pengjie，ZHANG Jie，et al. A review of big data analysis in manufacturing：connotation，methods，applications，and trends[J]. Journal of Mechanical Engineering，2023，59(12)：1-16.
[39] XIE X，BENNETT J，SAHA S，et al. Mechanistic data-driven prediction of as-built mechanical properties in metal additive manufacturing[J]. Npj Computational Materials，2021，7(1)：1-12.
[40] RUEDIGER-FLORE P，GLATT M，HUSSONG M，et al. CAD-based data augmentation and transfer learning empowers part classification in manufacturing[J]. The International Journal of Advanced Manufacturing Technology，2023，125(11)：5605-5618.
[41] YAO H，GAO Y，LIU Y. FEA-Net，A physics-guided data-driven model for efficient mechanical response prediction[J]. Computer Methods in Applied Mechanics and Engineering，2020，363：112892.
[42] CENEDESE M，AXÅS J，YANG H，et al. Data-driven nonlinear model reduction to spectral submanifolds in mechanical systems[J]. Philosophical Transactions of the Royal Society A，Mathematical，Physical and Engineering Sciences，2022，380(2229)：20210194.
[43] YAN J，OUYANG T. Advanced wind power prediction based on data-driven error correction[J]. Energy Conversion and Management，2019，180：302-311.
[44] SAFARI S，LONDOÑO MONSALVE J M. Data-driven structural identification of nonlinear assemblies，Structures with bolted joints[J]. Mechanical Systems and Signal Processing，2023，195：110296.
[45] AKHARE D，LUO T，WANG J X. Physics-integrated neural differentiable (PiNDiff) model for composites manufacturing[J]. Computer Methods in Applied Mechanics and Engineering，2023，406：115902.
[46] REINBOLD P A K，KAGEORGE L M，SCHATZ M F，et al. Robust learning from noisy，incomplete，high-dimensional experimental data via physically constrained symbolic regression[J]. Nature Communications，2021，12(1)：3219.
[47] LI X，HU Y，LI M，et al. Fault diagnostics between different type of components：a transfer learning approach[J]. Applied Soft Computing，2020，86：105950.
[48] WANG P，QI J，XU X，et al. Machining quality prediction of multi-feature parts using integrated multi-source domain dynamic adaptive transfer learning[J]. Robotics and Computer-Integrated Manufacturing，2024，90：102815.
[49] WU X，CHEN H，WU G，et al. Knowledge engineering with big data[J]. IEEE Intelligent Systems，2015，30：46-55.
[50] RAZ A，KENLEY C R，DELAURENTIS D. A system-of-systems perspective for information fusion system design and evaluation[J]. Information Fusion，2017，35：148-165.
[51] ZHANG H，QIN S，LI R，et al. Progressive modelling of feature-centred product family development[J]. International Journal of Production Research，2019，58：3701-3723.
[52] XU Y，LANDON Y，SEGONDS S，et al. A decision support model in mass customization[J]. Computers & Industrial Engineering，2017，114：11-21.
[53] HE B，WANG J，HUANG S，et al. Low-carbon product design for product life cycle[J]. Journal of Engineering Design，2015，26(10-12)：321-339.
[54] KULAK O，CEBI S，KAHRAMAN C. Applications of axiomatic design principles：a literature review[J]. Expert Systems with Applications，2010，37(9)：6705-6717.
[55] WANG H，ZHANG X，TANG C，et al. A semantic model for axiomatic systems design[J]. Proceedings of the Institution of Mechanical Engineers，Part C，Journal of Mechanical Engineering Science，2018，232：2159-2184.
[56] MABROK M，EFATMANESHNIK M，RYAN M. Integrating nonfunctional requirements into axiomatic design methodology[J]. IEEE Systems Journal，2017，11：2204-2214.
[57] ILEVBARE I M，PROBERT D，PHAAL R. A review of TRIZ，and its benefits and challenges in practice[J]. Technovation，2013，33(2)：30-37.
[58] AL-DWAIRI A，AL-ARAIDAH O，HAMASHA S. An integrated QFD and TRIZ methodology for innovative product design[J]. Designs，2023，7(6)：132.
[59] KASLOW D，ANDERSON L，ASUNDI S，et al. Developing a cubesat model-based system engineering (MBSE) reference model - interim status[C]//2015 IEEE Aerospace Conference. 2015：1-16.
[60] RASHID M，ANWAR M W，KHAN A. Toward the tools selection in model based system engineering for embedded systems—a systematic literature review [J]. Journal of Systems and Software，2015，106：150-163.
[61] BRAHMI R，HAMMADI M，AIFAOUI N，et al. CAD-MBSE interoperability for the checking of design requirements[C]//202118th International Multi- Conference on Systems，Signals & Devices (SSD). 2021：1446-1451.
[62] CHEN X，JIA S，XIANG Y. A review：knowledge reasoning over knowledge graph[J]. Expert systems with applications，2020，141：112948.
[63] WANG Q，MAO Z，WANG B，et al. Knowledge graph embedding：a survey of approaches and applications[J]. IEEE Transactions on Knowledge and Data Engineering，2017，29：2724-2743.
[64] HAO J，ZHAO L，MILISAVLJEVIC-SYED J，et al. Integrating and navigating engineering design decision-related knowledge using decision knowledge graph[J]. Advanced Engineering Informatics，2021，50：101366.
[65] KO H，WITHERELL P，LU Y，et al. Machine learning and knowledge graph based design rule construction for additive manufacturing[J]. Additive Manufacturing，2021，37：101620.
[66] KAWAMURA K，ARIFIN H H，ONG H K R，et al. Model-Based systems engineering (MBSE) application in nuclear power plants (NPP)[J]. INCOSE International Symposium，2024，34(1)：864-880.
[67] WANG L C，CHEN C C，LIU J L，et al. Framework and deployment of a cloud-based advanced planning and scheduling system[J]. Robotics and Computer-Integrated Manufacturing，2021，70：102088.
[68] DOLGUI A，IVANOV D，SETHI S P，et al. Scheduling in production，supply chain and industry 4.0 systems by optimal control：fundamentals，state-of-the-art and applications[J]. International Journal of Production Research，2019，57(2)：411-432.
[69] 王艳，蒋天伦. 融合决策树的分布式多工厂协同生产调度方法[J]. 系统仿真学报，2019，31(11)：2181-2197. WANG Yan，JIANG Tianlun. Distributed multi-factory collaborative production scheduling method based on decision tree integration[J]. Journal of System Simulation，2019，31(11)：2181-2197.
[70] ZHANG J，DING G，ZOU Y，et al. Review of job shop scheduling research and its new perspectives under Industry 4.0[J]. Journal of Intelligent Manufacturing，2019，30(4)：1809-1830.
[71] FANG Y，PENG C，LOU P，et al. Digital-twin-based job shop scheduling toward smart manufacturing[J]. IEEE Transactions on Industrial Informatics，2019，15：6425-6435.
[72] HAM A. Transfer-robot task scheduling in flexible job shop[J]. Journal of Intelligent Manufacturing，2020，31：1783-1793.
[73] SONG W，CHEN X，LI Q，et al. Flexible job-shop scheduling via graph neural network and deep reinforcement learning[J]. IEEE Transactions on Industrial Informatics，2023，19：1600-1610.
[74] GRASSI A，GUIZZI G，SANTILLO L，et al. Assessing the performances of a novel decentralised scheduling approach in Industry 4.0 and cloud manufacturing contexts[J]. International Journal of Production Research，2020，59：6034-6053.
[75] 桑艳伟. 铝合金大型构件产线多目标生产调度模型及优化方法研究[D]. 长沙：中南大学，2023. SANG Yanwei. Research on multi-objective production scheduling model and optimization method for large aluminum alloy components production line[D]. Changsha：Central South University，2023.
[76] WANG W，XIAO J，GAO C，et al. A review of production scheduling rules and algorithms for intelligent manufacturing[C]//International Conference on Artificial Intelligence and Industrial Design (AIID 2022)，Vol. 12612. SPIE，2023：278-283.
[77] ROSSIT D，TOHMÉ F，FRUTOS M. Production planning and scheduling in cyber-physical production systems：a review[J]. International Journal of Computer Integrated Manufacturing，2019，32：385-395.
[78] CASTRO P，GROSSMANN I，ZHANG Q. Expanding scope and computational challenges in process scheduling[J]. Computers & Chemical Engineering，2018，114：14-42.
[79] LI L，HAI L，LI G，et al. Integrated production planning and scheduling system design[J]. 20178th IEEE International Conference on Software Engineering and Service Science (ICSESS)，2017：731-734.
[80] JIANG Z，YUAN S，MA J，et al. The evolution of production scheduling from industry 3.0 through industry 4.0[J]. International Journal of Production Research，2021，60：3534-3554.
[81] LI W，ZHU C，YANG L，et al. Subtask scheduling for distributed robots in cloud manufacturing[J]. IEEE Systems Journal，2017，11：941-950.
[82] HE Z，GUO Z，WANG J. Integrated scheduling of production and distribution operations in a global MTO supply chain[J]. Enterprise Information Systems，2019，13：490-514.
[83] FRAZZON E，ALBRECHT A，PIRES M C，et al. Hybrid approach for the integrated scheduling of production and transport processes along supply chains[J]. International Journal of Production Research，2018，56：2019-2035.
[84] HAO J，CAO L，JIANG D. Integrated production-distribution scheduling problem with multiple independent manufacturers[J]. Mathematical Problems in Engineering，2015，2015(1)：1-5.
[85] ZHANG R，LV Q，LI J，et al. A reinforcement learning method for human-robot collaboration in assembly tasks[J]. Robotics and Computer-Integrated Manufacturing，2022，73：102227.
[86] CUI L，WANG L，XIANG G，et al. A Method based on correlation analysis of the assembly process and neural network for precision prediction of the inertial platform[C]//Proceedings of the 20215th International Conference on Cloud and Big Data Computing. New York，NY，USA，Association for Computing Machinery，2021：57-62.
[87] SHOVAL S，EFATMANESHNIK M，RYAN M. Assembly sequence planning for processes with heterogeneous reliabilities[J]. International Journal of Production Research，2017，55：2806-2828.
[88] MUELLER R，HOERAUF L，BASHIR A. Assembly process prediction with digital assistance systems to ensure synchrony between digital and physical product state using recurrent neural networks[C]//202025th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)，Vol. 1. 2020：513-517.
[89] ZANCHETTIN A，CASALINO A，PIRODDI L，et al. Prediction of human activity patterns for human–robot collaborative assembly tasks[J]. IEEE Transactions on Industrial Informatics，2019，15：3934-3942.
[90] MU X，WANG Y，BO Y，et al. A new assembly precision prediction method of aeroengine high-pressure rotor system considering manufacturing error and deformation of parts[J]. Journal of Manufacturing Systems，2021，61:112-124.
[91] ZHANG Y，ZHANG L. Intelligent fault detection of reciprocating compressor using a novel discrete state space[J]. Mechanical Systems and Signal Processing，2022，169：108583.
[92] CAO P，SHEN X，DUAN G，et al. Quality-integrated diagnostic platform for aerospace complex product assembly processes[J]. Computers & Industrial Engineering，2024，189：109796.
[93] 顾世民，刘金锋，钱天龙，等. 基于几何特征的船舶大尺度构件快速建模及装配干涉检测方法[J]. 中国机械工程，2024：1-17. GU Shimin，LIU Jinfeng，QIAN Tianlong，et al. Rapid modeling and assembly interference detection method for large-scale ship components based on geometric features[J]. China Mechanical Engineering，2024：1-17.
[94] VERNA E，GENTA G，GALETTO M，et al. Defect prediction for assembled products，a novel model based on the structural complexity paradigm[J]. The International Journal of Advanced Manufacturing Technology，2021，120：3405-3426.
[95] BURGGRÄF P，WAGNER J，HEINBACH B，et al. Machine learning-based prediction of missing components for assembly – a case study at an engineer-to-order manufacturer[J]. IEEE Access，2021，9：105926-105938.
[96] YI Y，ZHANG A，LIU X，et al. Digital twin-driven assembly accuracy prediction method for high performance precision assembly of complex products[J]. Advanced Engineering Informatics，2024，61：102495.
[97] LIU P，QIAN L，ZHAO X，et al. Joint knowledge graph and large language model for fault diagnosis and its application in aviation assembly[J]. IEEE Transactions on Industrial Informatics，2024，20(6)：8160-8169.
[98] SILVESTRI L，FORCINA A，INTRONA V，et al. Maintenance transformation through Industry 4.0 technologies，A systematic literature review[J]. Computers in Industry，2020，123：103335.
[99] RAI A，KIM J M. A novel health indicator based on information theory features for assessing rotating machinery performance degradation[J]. IEEE Transactions on Instrumentation and Measurement，2020，69：6982-6994.
[100] TANG D，GONG M，TIAN L，et al. Health indicator construction of high-speed rotating bearings in aerospace CMG based on physics-inspired machine-learning approach[J]. IEEE Transactions on Instrumentation and Measurement，2022，71：1-11.
[101] GUO J，WANG Z，LI H，et al. A hybrid prognosis scheme for rolling bearings based on a novel health indicator and nonlinear Wiener process[J]. Reliability Engineering & System Safety，2024，245：110014.
[102] MARTINOVA L，KOZAK N，KOVALEV I，et al. Creation of CNC system’s components for monitoring machine tool health[J]. The International Journal of Advanced Manufacturing Technology，2021，117：2341-2348.
[103] ZONTA T，DA COSTA C A，DA ROSA RIGHI R，et al. Predictive maintenance in the industry 4.0：a systematic literature review[J]. Computers & Industrial Engineering，2020，150：106889.
[104] AIVALIOTIS P，GEORGOULIAS K，CHRYSSOLOURIS G. The use of digital twin for predictive maintenance in manufacturing[J]. International Journal of Computer Integrated Manufacturing，2019，32：1067-1080.
[105] DUAN C，DENG C，WANG B. Multi-phase sequential preventive maintenance scheduling for deteriorating repairable systems[J]. Journal of Intelligent Manufacturing，2019，30：1779-1793.
[106] MENA R，VIVEROS P，ZIO E，et al. An optimization framework for opportunistic planning of preventive maintenance activities[J]. Reliability Engineering & System Safety，2021，215：107801.
[107] 陈楠，蔡跃洲. 工业大数据的属性特征、价值创造及开发模式[J]. 北京交通大学学报(社会科学版)，2023，22(3)，25-36. CHEN Nan，CAI Yuezhou. Attribute characteristics，value creation and development model of industrial big data[J]. Journal of Beijing Jiaotong University (Social Sciences Edition)，2023，22(3)：25-36.
[108] SIEW C Y，CHANG M M L，ONG S K，et al. Human-oriented maintenance and disassembly in sustainable manufacturing[J]. Computers & Industrial Engineering，2020，150：106903.
[109] TAO F，BI L，ZUO Y，et al. Partial/parallel disassembly sequence planning for complex products[J]. Journal of Manufacturing Science and Engineering，2017，140(1)：011016.
[110] FENG Y，ZHOU M，TIAN G，et al. Target disassembly sequencing and scheme evaluation for CNC machine tools using improved multiobjective ant colony algorithm and fuzzy integral[J]. IEEE Transactions on Systems，Man，and Cybernetics，Systems，2019，49(12)：2438-2451.
[111] IMEN B，MONCEF H，MOEZ T，et al. Generation of disassembly plans and quality assessment based on CAD data[J]. International Journal of Computer Integrated Manufacturing，2020，33：1300-1320.
[112] ANNARELLI A，BATTISTELLA C，NONINO F. Product service system：a conceptual framework from a systematic review[J]. Journal of Cleaner Production，2016，139：1011-1032.
[113] PIROLA F，BOUCHER X，WIESNER S，et al. Digital technologies in product-service systems，a literature review and a research agenda[J]. Computers in Industry，2020，123：103301.
[114] 张莹，陈怀超，白珊. 生产性服务业FDI对制造业服务化的影响——基于省级面板数据的分析[J]. 西安理工大学学报，2023：1-11. ZHANG Ying，CHEN Huaichao，BAI Shan. The impact of productive service FDI on the service-oriented transformation of manufacturing industry—an analysis based on provincial panel data[J]. Journal of Xi'an University of Technology，2023：1-11.
[115] LI Y，CHANG Q，BRUNDAGE M P，et al. Market demand-oriented data-driven modeling for dynamic manufacturing system control[J]. IEEE Transactions on Systems，Man，and Cybernetics，Systems，2015，45：109-121.
[116] CAO J，JIANG Z，WANG K. Customer demand prediction of service-oriented manufacturing incorporating customer satisfaction[J]. International Journal of Production Research，2016，54(5)：1303-1321.
[117] VALTAKOSKI A，JÄRVI K. Productization of knowledge-intensive services：enabling knowledge sharing and cross-unit collaboration[J]. Journal of Service Management，2016，27(3)：360-390.
[118] ALBEY E，NOROUZI A，KEMPF K G，et al. Demand modeling with forecast evolution：an application to production planning[J]. IEEE Transactions on Semiconductor Manufacturing，2015，28(3)：374-384.
[119] ALAEI S，SETAK M. Multi objective coordination of a supply chain with routing and service level consideration[J]. International Journal of Production Economics，2015，167：271-281.
[120] YAHIA W，AYADI O，MASMOUDI F. A fuzzy-based negotiation approach for collaborative planning in manufacturing supply chains[J]. Journal of Intelligent Manufacturing，2017，28：1987-2006.
[121] ALI S，NAKADE K. Coordinating a supply chain system for production，pricing and service strategies with disruptions[J]. International Journal of Advanced Operations Management，2016，8：17-37.
[122] XU Y，YU Q，ZHENG Y. Supply chain coordination of consumer electronics products considering product quality level[J]. 202039th Chinese Control Conference (CCC)，2020：7685-7690.
[123] ELORANTA V，TURUNEN T. Platforms in service-driven manufacturing，Leveraging complexity by connecting，sharing，and integrating[J]. Industrial Marketing Management，2016，55：178-186.
[124] CENAMOR J，RÖNNBERG SJÖDIN D，PARIDA V. Adopting a platform approach in servitization：leveraging the value of digitalization[J]. International Journal of Production Economics，2017，192：54-65.
[125] ZHANG Y，CHENG Y，WANG H，et al. Variable-utility-aware manufacturing service collaboration optimization toward industrial internet platforms[J]. IEEE Transactions on Systems，Man，and Cybernetics，Systems，2023，53：6007-6017.
[126] YANG H，YUAN J，LI C，et al. BrainIoT：brain-like productive services provisioning with federated learning in industrial IoT[J]. IEEE Internet of Things Journal，2021，9：2014-2024.
[127] 徐湘楚. 电动汽车聚合商响应能力评估方法及响应决策模型研究[D]. 北京：华北电力大学(北京)，2024. XU Xiangchu. Research on evaluation method and response decision model of electric vehicle aggregator response capability[D]. Beijing：North China Electric Power University (Beijing)，2024.
[128] NEUSTEIN A，MAHALLE P N，JOSHI P，et al. AI，IoT，big data and cloud computing for industry 4.0[M]. Berlin：Springer，2023.
[129] 卢延纯. 夯实数据要素驱动基础培育数据资产价格链[J]. 价格理论与实践，2023(3)：12-14，25. LU Yanchun. Consolidating the foundation of data factor-driven development and cultivating the price chain of data assets[J]. Price Theory and Practice，2023(3)：12-14，25.
[130] SHANG C，SAEIDI P，GOH C F. Evaluation of circular supply chains barriers in the era of industry 4.0 transition using an extended decision-making approach[J]. Journal of Enterprise Information Management，2022，35(4/5)：1100-1128.
[131] KAZANTSEV N，PISHCHULOV G，MEHANDJIEV N，et al. Investigating barriers to demand-driven SME collaboration in low-volume high-variability manufacturing[J]. Supply Chain Management，An International Journal，2022，27(2)：265-282.