IDEAS home Printed from https://ideas.repec.org/a/eee/proeco/v289y2025ics0925527325002750.html

Enhancing digital manufacturing efficiency and dominance relation driven big Data analytics

Author

Listed:
  • Jin, Zhuo
  • Zhou, Zhixiang
  • Wu, Huaqing

Abstract

The integration of AI technologies based on large language models (AI-LLM) with big data analytics has revolutionized digital manufacturing and enabled real-time decision-making in operations and supply chain management (OSCM). However, traditional data envelopment analysis (DEA) models face prohibitive computational complexity in large-scale environments, hindering their adoption for such AI-LLM-driven optimization tasks as demand forecasting, inventory control, and energy efficiency enhancement. To bridge this gap, we propose a dominance–relation-driven DEA framework tailored for big data environments contexts in digital manufacturing. Our approach leverages spatial relationship characteristics and grouping algorithms to reduce computational complexity by 10–30 times, as validated through numerical simulations on industrial datasets. A case study on the Chaohu Lake watershed further demonstrates its practical value in LLM-enhanced environmental monitoring and sustainable supply chain design. This research presents a scalable solution for optimizing efficiency in digital manufacturing, addressing critical challenges in predictive analytics and resource allocation.

Suggested Citation

  • Jin, Zhuo & Zhou, Zhixiang & Wu, Huaqing, 2025. "Enhancing digital manufacturing efficiency and dominance relation driven big Data analytics," International Journal of Production Economics, Elsevier, vol. 289(C).
    • Handle: RePEc:eee:proeco:v:289:y:2025:i:c:s0925527325002750
    DOI: 10.1016/j.ijpe.2025.109790
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0925527325002750
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijpe.2025.109790?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Tao Jie, 2020. "Parallel processing of the Build Hull algorithm to address the large-scale DEA problem," Annals of Operations Research, Springer, vol. 295(1), pages 453-481, December.
    2. Erik Brynjolfsson & Danielle Li & Lindsey Raymond, 2025. "Generative AI at Work," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 140(2), pages 889-942.
    3. Alexandre Dolgui & Dmitry Ivanov & Boris Sokolov, 2020. "Reconfigurable supply chain: the X-network," International Journal of Production Research, Taylor & Francis Journals, vol. 58(13), pages 4138-4163, July.
    4. Zhu, Joe, 2003. "Imprecise data envelopment analysis (IDEA): A review and improvement with an application," European Journal of Operational Research, Elsevier, vol. 144(3), pages 513-529, February.
    5. Khezrimotlagh, Dariush & Zhu, Joe & Cook, Wade D. & Toloo, Mehdi, 2019. "Data envelopment analysis and big data," European Journal of Operational Research, Elsevier, vol. 274(3), pages 1047-1054.
    6. Kerstens, Kristiaan & O’Donnell, Christopher & Van de Woestyne, Ignace, 2019. "Metatechnology frontier and convexity: A restatement," European Journal of Operational Research, Elsevier, vol. 275(2), pages 780-792.
    7. Chao Zhang & Jingjing Li & Guanghui Zhou & Qian Huang & Min Zhang & Yifan Zhi & Zhibo Wei, 2024. "A multi-level modelling and fidelity evaluation method of digital twins for creating smart production equipment in Industry 4.0," International Journal of Production Research, Taylor & Francis Journals, vol. 62(10), pages 3671-3689, May.
    8. Chen, Ya & Tsionas, Mike G. & Zelenyuk, Valentin, 2021. "LASSO+DEA for small and big wide data," Omega, Elsevier, vol. 102(C).
    9. T Kuosmanen, 2009. "Data envelopment analysis with missing data," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(12), pages 1767-1774, December.
    10. Shanshan Li & Yong He & Stefan Minner, 2021. "Dynamic compensation and contingent sourcing strategies for supply disruption," International Journal of Production Research, Taylor & Francis Journals, vol. 59(5), pages 1511-1533, March.
    11. Du, Gang & Li, Wendi, 2022. "Does innovative city building promote green logistics efficiency? Evidence from a quasi-natural experiment with 285 cities," Energy Economics, Elsevier, vol. 114(C).
    12. Charles, Vincent & Aparicio, Juan & Zhu, Joe, 2019. "The curse of dimensionality of decision-making units: A simple approach to increase the discriminatory power of data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 279(3), pages 929-940.
    13. M C A Silva Portela & E Thanassoulis & G Simpson, 2004. "Negative data in DEA: a directional distance approach applied to bank branches," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(10), pages 1111-1121, October.
    14. Lee, Chia-Yen & Cai, Jia-Ying, 2020. "LASSO variable selection in data envelopment analysis with small datasets," Omega, Elsevier, vol. 91(C).
    15. William Cooper & Zhimin Huang & Vedran Lelas & Susan Li & Ole Olesen, 1998. "Chance Constrained Programming Formulations for Stochastic Characterizations of Efficiency and Dominance in DEA," Journal of Productivity Analysis, Springer, vol. 9(1), pages 53-79, January.
    16. Léopold Simar & Valentin Zelenyuk, 2018. "Central Limit Theorems for Aggregate Efficiency," Operations Research, INFORMS, vol. 66(1), pages 137-149, January.
    17. Du, Juan & Chen, Yao & Huo, Jiazhen, 2015. "DEA for non-homogenous parallel networks," Omega, Elsevier, vol. 56(C), pages 122-132.
    18. Ilya Jackson & Dmitry Ivanov & Alexandre Dolgui & Jafar Namdar, 2024. "Generative artificial intelligence in supply chain and operations management: a capability-based framework for analysis and implementation," International Journal of Production Research, Taylor & Francis Journals, vol. 62(17), pages 6120-6145, September.
    19. Ahti Salo & Antti Punkka, 2011. "Ranking Intervals and Dominance Relations for Ratio-Based Efficiency Analysis," Management Science, INFORMS, vol. 57(1), pages 200-214, January.
    20. Dobos, Imre & Vörösmarty, Gyöngyi, 2019. "Inventory-related costs in green supplier selection problems with Data Envelopment Analysis (DEA)," International Journal of Production Economics, Elsevier, vol. 209(C), pages 374-380.
    21. Jie Wu & Zhixiang Zhou, 2015. "A mixed-objective integer DEA model," Annals of Operations Research, Springer, vol. 228(1), pages 81-95, May.
    22. Kneip, Alois & Simar, Léopold & Wilson, Paul W., 2015. "When Bias Kills The Variance: Central Limit Theorems For Dea And Fdh Efficiency Scores," Econometric Theory, Cambridge University Press, vol. 31(2), pages 394-422, April.
    23. Rolf Färe & Valentin Zelenyuk, 2021. "On aggregation of multi-factor productivity indexes," Journal of Productivity Analysis, Springer, vol. 55(2), pages 107-133, April.
    24. Hong, Jae-Dong & Mwakalonge, Judith L., 2020. "Biofuel logistics network scheme design with combined data envelopment analysis approach," Energy, Elsevier, vol. 209(C).
    25. Yang, Feng & Wu, Desheng Dash & Liang, Liang & O'Neill, Liam, 2011. "Competition strategy and efficiency evaluation for decision making units with fixed-sum outputs," European Journal of Operational Research, Elsevier, vol. 212(3), pages 560-569, August.
    26. Zhu, Weiwei & Yu, Yu & Sun, Panpan, 2018. "Data envelopment analysis cross-like efficiency model for non-homogeneous decision-making units: The case of United States companies’ low-carbon investment to attain corporate sustainability," European Journal of Operational Research, Elsevier, vol. 269(1), pages 99-110.
    27. Du, Juan & Liang, Liang & Chen, Yao & Bi, Gong-bing, 2010. "DEA-based production planning," Omega, Elsevier, vol. 38(1-2), pages 105-112, February.
    28. Josef Svoboda & Stefan Minner, 2022. "Tailoring inventory classification to industry applications: the benefits of understandable machine learning," International Journal of Production Research, Taylor & Francis Journals, vol. 60(1), pages 388-401, January.
    29. Chu, Junfei & Rui, Yuting & Khezrimotlagh, Dariush & Zhu, Joe, 2024. "A general computational framework and a hybrid algorithm for large-scale data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 316(2), pages 639-650.
    30. Kulkarni, Onkar & Dahan, Mathieu & Montreuil, Benoit, 2022. "Resilient Hyperconnected Parcel Delivery Network Design Under Disruption Risks," International Journal of Production Economics, Elsevier, vol. 251(C).
    31. Kai Ding & Felix T.S. Chan & Xudong Zhang & Guanghui Zhou & Fuqiang Zhang, 2019. "Defining a Digital Twin-based Cyber-Physical Production System for autonomous manufacturing in smart shop floors," International Journal of Production Research, Taylor & Francis Journals, vol. 57(20), pages 6315-6334, October.
    32. Adler, Nicole & Yazhemsky, Ekaterina, 2010. "Improving discrimination in data envelopment analysis: PCA-DEA or variable reduction," European Journal of Operational Research, Elsevier, vol. 202(1), pages 273-284, April.
    33. Frank, Alejandro Germán & Dalenogare, Lucas Santos & Ayala, Néstor Fabián, 2019. "Industry 4.0 technologies: Implementation patterns in manufacturing companies," International Journal of Production Economics, Elsevier, vol. 210(C), pages 15-26.
    34. Itay Goldstein & Chester S. Spatt & Mao Ye, 2021. "Big Data in Finance," NBER Working Papers 28615, National Bureau of Economic Research, Inc.
    35. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    36. Liu, Zhenyuan & Han, Shuihua & Li, Chao & Gupta, Shivam & Sivarajah, Uthayasankar, 2022. "Leveraging customer engagement to improve the operational efficiency of social commerce start-ups," Journal of Business Research, Elsevier, vol. 140(C), pages 572-582.
    37. Itay Goldstein & Chester S Spatt & Mao Ye, 2021. "Big Data in Finance [Institutional order handling and broker-affiliated trading venues]," The Review of Financial Studies, Society for Financial Studies, vol. 34(7), pages 3213-3225.
    38. Sajjad Rahmanzadeh & Mir Saman Pishvaee & Kannan Govindan, 2023. "Emergence of open supply chain management: the role of open innovation in the future smart industry using digital twin network," Annals of Operations Research, Springer, vol. 329(1), pages 979-1007, October.
    39. Samuel Fosso Wamba & Cameron Guthrie & Maciel M. Queiroz & Stefan Minner, 2024. "ChatGPT and generative artificial intelligence: an exploratory study of key benefits and challenges in operations and supply chain management," International Journal of Production Research, Taylor & Francis Journals, vol. 62(16), pages 5676-5696, August.
    40. Bustinza, Oscar F. & Molina, Luis M. & Vendrell-Herrero, Ferran & Opazo-Basaez, Marco, 2024. "AI-enabled smart manufacturing boosts ecosystem value capture: The importance of servitization pathways within digital-intensive industries," International Journal of Production Economics, Elsevier, vol. 277(C).
    41. Feng, Jianghong & Ning, Yu & Wang, Zhaohua & Li, Guo & Xiu Xu, Su, 2024. "ChatGPT-enabled two-stage auctions for electric vehicle battery recycling," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 183(C).
    42. Wamba, Samuel Fosso & Dubey, Rameshwar & Gunasekaran, Angappa & Akter, Shahriar, 2020. "The performance effects of big data analytics and supply chain ambidexterity: The moderating effect of environmental dynamism," International Journal of Production Economics, Elsevier, vol. 222(C).
    43. Richard Barr & Matthew Durchholz, 1997. "Parallel and hierarchical decomposition approaches for solving large-scale Data Envelopment Analysis models," Annals of Operations Research, Springer, vol. 73(0), pages 339-372, October.
    44. Wang, Ke & Huang, Wei & Wu, Jie & Liu, Ying-Nan, 2014. "Efficiency measures of the Chinese commercial banking system using an additive two-stage DEA," Omega, Elsevier, vol. 44(C), pages 5-20.
    45. Xiao, Helu & Zhou, Zhongbao & Ren, Teng & Liu, Wenbin, 2022. "Estimation of portfolio efficiency in nonconvex settings: A free disposal hull estimator with non-increasing returns to scale," Omega, Elsevier, vol. 111(C).
    46. Yang, Guoliang & Ahlgren, Per & Yang, Liying & Rousseau, Ronald & Ding, Jielan, 2016. "Using multi-level frontiers in DEA models to grade countries/territories," Journal of Informetrics, Elsevier, vol. 10(1), pages 238-253.
    47. Fosso Wamba, Samuel & Queiroz, Maciel M. & Chiappetta Jabbour, Charbel Jose & Shi, Chunming (Victor), 2023. "Are both generative AI and ChatGPT game changers for 21st-Century operations and supply chain excellence?," International Journal of Production Economics, Elsevier, vol. 265(C).
    48. Mohit Goswami & Yash Daultani & Felix T.S. Chan & Saurabh Pratap, 2022. "Assessing the impact of supplier benchmarking in manufacturing value chains: an Intelligent decision support system for original equipment manufacturers," International Journal of Production Research, Taylor & Francis Journals, vol. 60(24), pages 7411-7435, December.
    49. Loske, Dominic & Klumpp, Matthias, 2021. "Human-AI collaboration in route planning: An empirical efficiency-based analysis in retail logistics," International Journal of Production Economics, Elsevier, vol. 241(C).
    50. Nataraja, Niranjan R. & Johnson, Andrew L., 2011. "Guidelines for using variable selection techniques in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 215(3), pages 662-669, December.
    51. Itay Goldstein & Chester S Spatt & Mao Ye, 2021. "Big Data in Finance," NBER Chapters, in: Big Data: Long-Term Implications for Financial Markets and Firms, pages 3213-3225, National Bureau of Economic Research, Inc.
    52. Zelenyuk, Valentin, 2020. "Aggregation of inputs and outputs prior to Data Envelopment Analysis under big data," European Journal of Operational Research, Elsevier, vol. 282(1), pages 172-187.
    53. Rajiv D. Banker, 1993. "Maximum Likelihood, Consistency and Data Envelopment Analysis: A Statistical Foundation," Management Science, INFORMS, vol. 39(10), pages 1265-1273, October.
    54. Qi, Quansong & Xu, Zhiyong & Rani, Pratibha, 2023. "Big data analytics challenges to implementing the intelligent Industrial Internet of Things (IIoT) systems in sustainable manufacturing operations," Technological Forecasting and Social Change, Elsevier, vol. 190(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mergoni, Anna & Emrouznejad, Ali & De Witte, Kristof, 2025. "Fifty years of Data Envelopment Analysis," European Journal of Operational Research, Elsevier, vol. 326(3), pages 389-412.
    2. Wang, Mengyuan & Chen, Ya & Zelenyuk, Valentin, 2025. "DEA for big wide data based on regularization approaches: An application to energy efficiency analysis," Energy Economics, Elsevier, vol. 152(C).
    3. Valentin Zelenyuk, 2019. "Data Envelopment Analysis and Business Analytics: The Big Data Challenges and Some Solutions," CEPA Working Papers Series WP072019, School of Economics, University of Queensland, Australia.
    4. Zelenyuk, Valentin, 2020. "Aggregation of inputs and outputs prior to Data Envelopment Analysis under big data," European Journal of Operational Research, Elsevier, vol. 282(1), pages 172-187.
    5. Esteve, Miriam & Aparicio, Juan & Rodriguez-Sala, Jesus J. & Zhu, Joe, 2023. "Random Forests and the measurement of super-efficiency in the context of Free Disposal Hull," European Journal of Operational Research, Elsevier, vol. 304(2), pages 729-744.
    6. Bao Hoang Nguyen & Valentin Zelenyuk, 2021. "Aggregation of Outputs and Inputs for DEA Analysis of Hospital Efficiency: Economics, Operations Research and Data Science Perspectives," International Series in Operations Research & Management Science, in: Joe Zhu & Vincent Charles (ed.), Data-Enabled Analytics, pages 123-158, Springer.
    7. Yuanxiang Zhou & Shan Wang & Shuqi Xu & Qingyuan Zhu, 2025. "Big data in data envelopment analysis with undesirable outputs based on simulation and environmental-health matching data of Chinese industrial enterprises," Annals of Operations Research, Springer, vol. 348(1), pages 279-298, May.
    8. Yi, Tianhao & Li, Lisha & Li, Zhiyong & Zhang, Jiaxuan, 2025. "Evaluating electricity transmission and distribution efficiency using Data Envelopment Analysis Forest with feature importance," Energy, Elsevier, vol. 330(C).
    9. Dong, Hanjiang & Wang, Xiuyuan & Cui, Ziyu & Zhu, Jizhong & Li, Shenglin & Yu, Changyuan, 2025. "Machine learning-enhanced Data Envelopment Analysis via multi-objective variable selection for benchmarking combined electricity distribution performance," Energy Economics, Elsevier, vol. 143(C).
    10. Chen, Ya & Tsionas, Mike G. & Zelenyuk, Valentin, 2021. "LASSO+DEA for small and big wide data," Omega, Elsevier, vol. 102(C).
    11. Valero-Carreras, Daniel & Aparicio, Juan & Guerrero, Nadia M., 2021. "Support vector frontiers: A new approach for estimating production functions through support vector machines," Omega, Elsevier, vol. 104(C).
    12. Duras, Toni & Javed, Farrukh & Månsson, Kristofer & Sjölander, Pär & Söderberg, Magnus, 2023. "Using machine learning to select variables in data envelopment analysis: Simulations and application using electricity distribution data," Energy Economics, Elsevier, vol. 120(C).
    13. Valentin Zelenyuk, 2024. "Aggregation in efficiency and productivity analysis: a brief review with new insights and justifications for constant returns to scale," Journal of Productivity Analysis, Springer, vol. 62(3), pages 321-334, December.
    14. Dai, Sheng, 2023. "Variable selection in convex quantile regression: L1-norm or L0-norm regularization?," European Journal of Operational Research, Elsevier, vol. 305(1), pages 338-355.
    15. Ali Emrouznejad & Victor Podinovski & Vincent Charles & Chixiao Lu & Amir Moradi-Motlagh, 2025. "Rajiv Banker’s lasting impact on data envelopment analysis," Annals of Operations Research, Springer, vol. 351(2), pages 1225-1264, August.
    16. Ya Chen & Mike Tsionas & Valentin Zelenyuk, 2020. "LASSO DEA for small and big data," CEPA Working Papers Series WP022020, School of Economics, University of Queensland, Australia.
    17. Nguyen, Bao Hoang & Simar, Léopold & Zelenyuk, Valentin, 2022. "Data sharpening for improving central limit theorem approximations for data envelopment analysis–type efficiency estimators," European Journal of Operational Research, Elsevier, vol. 303(3), pages 1469-1480.
    18. Olawale Ogunrinde & Ekundayo Shittu, 2023. "Benchmarking performance of photovoltaic power plants in multiple periods," Environment Systems and Decisions, Springer, vol. 43(3), pages 489-503, September.
    19. Zhichao Wang & Bao Hoang Nguyen & Valentin Zelenyuk, 2024. "Performance analysis of hospitals in Australia and its peers: a systematic and critical review," Journal of Productivity Analysis, Springer, vol. 62(2), pages 139-173, October.
    20. Ivanov, Dmitry, 2025. "Conceptual and formal models for design, adaptation, and control of digital twins in supply chain ecosystems," Omega, Elsevier, vol. 137(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:proeco:v:289:y:2025:i:c:s0925527325002750. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ijpe .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.