REFERENCES

1. Ghobakhloo, M.; Fathi, M. Corporate survival in industry 4.0 era: the enabling role of lean-digitized manufacturing. J. Manuf. Technol. Manag. 2020, 31, 1-30.

2. Gündüz, M. A.; Demir, S.; Paksoy, T. Road-mapping for smart and lean tools with a sustainability perspective: a new model proposal. J. Transp. Logist. 2024, 9, 83-96.

3. Satoglu, S.; Ustundag, A.; Cevikcan, E.; Durmusoglu, M. B. Lean production systems for Industry 4.0. In Industry 4.0: managing the digital transformation. Cham: Springer International Publishing; 2018. pp. 43-59.

4. Rossini, M.; Cifone, F. D.; Kassem, B.; Costa, F.; Portioli-Staudacher, A. Being lean: how to shape digital transformation in the manufacturing sector. J. Manuf. Technol. Manag. 2021, 32, 239-59.

5. Guan, R.; Wang, H.; Zheng, R.; Shi, X.; Wang, K. Evaluating the impact of digital technologies on energy efficiency: evidence from Chinese publicly listed companies. Energy. Policy. 2025, 207, 114847.

6. Kim, J.; Kim, J.; Sovacool, B. K.; et al. Reviewing the socio-technical dynamics of AI, data centers and digitalization on energy and the environment. Renew. Sustain. Energy. Rev. 2026, 234, 116861.

7. Sarıışık, G.; Göncü, B.; Öğütlü, A. S. Harnessing industry 4.0 technologies to reduce SMEs’ carbon footprint: a machine learning-based assessment. J. Environ. Manage. 2026, 398, 128459.

8. Arana-Landín, G.; Uriarte-Gallastegi, N.; Landeta-Manzano, B.; Laskurain-Iturbe, I. The contribution of lean management - industry 4.0 technologies to improving energy efficiency. Energies 2023, 16, 2124.

9. Zhu, M.; Liang, C.; Lee, P. K. C.; Yeung, A. C. L.; Zhou, H. Does digital technologies deployment promote environmental performance? Evidence from China. IEEE. Trans. Eng. Manage. 2026, 73, 712-25.

10. Khan, M. I.; Yasmeen, T.; Khan, M.; et al. Integrating industry 4.0 for enhanced sustainability: pathways and prospects. Sustain. Prod. Consum. 2025, 54, 149-89.

11. Bonello, A.; Refalo, P.; Francalanza, E.; Gauci, M. Integrating environmental life cycle assessment in assistive technology selection for inclusive workstations: a novel multi-objective optimisation approach in industry 5.0. Clean. Eng. Technol. 2026, 32, 101245.

12. Almeida, R. P.; Ayala, N. F.; Benitez, G. B.; Kliemann Neto, F. J.; Frank, A. G. How to assess investments in Industry 4.0 technologies? A multiple-criteria framework for economic, financial, and sociotechnical factors. Prod. Plan. Control. 2023, 34, 1583-602.

13. Javaid, M.; Khan, S.; Haleem, A.; Rab, S. Adoption of modern technologies for implementing industry 4.0: an integrated MCDM approach. Benchmarking 2023, 30, 3753-90.

14. Vohra, K.; Sinha, A. K.; Anand, A. Development of Industry 4.0 based technology selection index using multi criteria decision making. RAIRO. Oper. Res. 2024, 58, 5185-209.

15. Maretto, L.; Faccio, M.; Battini, D. Industry 4.0 technology implementation in manufacturing: a selection method and real case applications. Int. J. Prod. Res. 2025, 63, 3142-74.

16. Santos, A. D. M.; Sant’anna, ÂMDO.; Barbosa, A. S.; Becker, A. M.; Ayala, N. F. Multi-criteria decision-making model for sustainability functions integrated Industry 4.0 technologies within small and medium enterprises in emerging countries. Int. J. Product. Perform. Manag. 2025, 74, 1614-43.

17. Kamble, S.; Gunasekaran, A.; Dhone, N. C. Industry 4.0 and lean manufacturing practices for sustainable organisational performance in Indian manufacturing companies. Int. J. Prod. Res. 2020, 58, 1319-37.

18. Varela, L.; Araújo, A.; Ávila, P.; Castro, H.; Putnik, G. Evaluation of the relation between lean manufacturing, Industry 4.0, and sustainability. Sustainability 2019, 11, 1439.

19. Saad, S. M.; Bahadori, R.; Bhovar, C.; Zhang, H. Industry 4.0 and lean manufacturing - a systematic review of the state-of-the-art literature and key recommendations for future research. Int. J. Lean. Six. Sigma. 2024, 15, 997-1024.

20. Grigorescu, A.; Lincaru, C.; Pirciog, C. S. The impact of cloud computing on mass and energy flows: greenhouse gas emissions in the IT and communications sectors at the European level (2014-2021). Processes 2025, 13, 1808.

21. Pirson, T.; Bol, D. Assessing the embodied carbon footprint of IoT edge devices with a bottom-up life-cycle approach. J. Clean. Prod. 2021, 322, 128966.

22. Gütmen, S.; Roy, S. K.; Weber, G. An overview of weighted goal programming: a multi-objective transportation problem with some fresh viewpoints. Cent. Eur. J. Oper. Res. 2024, 32, 557-68.

23. Karakutuk, S. S.; Ornek, M. A. A goal programming approach to lean production system implementation. J. Oper. Res. Soc. 2023, 74, 403-16.

24. Aouni, B.; Colapinto, C.; La Torre, D. Financial portfolio management through the goal programming model: current state-of-the-art. Eur. J. Oper. Res. 2014, 234, 536-45.

25. Mohseny-Tonekabony, N.; Sadjadi, S. J.; Mohammadi, E.; Tamiz, M.; Jones, D. F. Robust, extended goal programming with uncertainty sets: an application to a multi-objective portfolio selection problem leveraging DEA. Ann. Oper. Res. 2025, 346, 1497-552.

26. Brandenburg, M. Low carbon supply chain configuration for a new product - a goal programming approach. Int. J. Prod. Res. 2015, 53, 6588-610.

27. Chung, K.; Chiu, R. Optimizing sustainable electronics supply chains under carbon taxation and fuzzy demand: a multi-goal programming approach. Sustainability 2026, 18, 1686.

28. Sanders, A.; Elangeswaran, C.; Wulfsberg, J. Industry 4.0 implies lean manufacturing: research activities in industry 4.0 function as enablers for lean manufacturing. J. Ind. Eng. Manag. 2016, 9, 811.

29. Pagliosa, M.; Tortorella, G.; Ferreira, J. C. E. Industry 4.0 and lean manufacturing: a systematic literature review and future research directions. J. Manuf. Technol. Manag. 2019, 32, 543-69.

30. Buer, S.; Strandhagen, J. O.; Chan, F. T. S. The link between Industry 4.0 and lean manufacturing: mapping current research and establishing a research agenda. Int. J. Prod. Res. 2018, 56, 2924-40.

31. Shah, R.; Ward, P. T. Defining and developing measures of lean production. J. Oper. Manag. 2007, 25, 785-805.

32. Pandya, D.; Kumar, G.; Singh, S. Aligning sustainability goals of industrial operations and marketing in Industry 4.0 environment for MSMEs in an emerging economy. J. Bus. Ind. Mark. 2024, 39, 581-602.

33. World Resources Institute. Greenhouse gas protocol product life cycle accounting and reporting standard. 2011. https://www.wri.org/research/greenhouse-gas-protocol-product-life-cycle-accounting-and-reporting-standard. (accessed 2026-10-15).

34. Hanafy, W. A.; Sukprasert, T.; Souza, A.; Irwin, D.; Shenoy, P. Untangling the carbon-cost tradeoffs and stampede effect challenges in cloud computing. IEEE. Trans. Comput. 2026, 75, 2715-29.

35. Cao, Z.; Wang, R.; Zhou, X.; et al. Adaptive capacity provisioning for carbon-aware data centers: a digital twin-based approach. IEEE. Trans. Sustain. Comput. 2025, 10, 730-43.

36. Zhao, D.; Zhou, J.; Li, K. CFWS: DRL-based framework for energy cost and carbon footprint optimization in cloud data centers. IEEE. Trans. Sustain. Comput. 2025, 10, 95-107.

37. Mishra, S.; Singh, S. P. Carbon management framework for sustainable manufacturing using life cycle assessment, IoT and carbon sequestration. Benchmarking 2021, 28, 1396-409.

38. Gupta, U.; Kim, Y. G.; Lee, S.; et al. Chasing carbon: the elusive environmental footprint of computing. arXiv 2020, arXiv:2011.02839. https://doi.org/10.48550/arXiv.2011.02839. (accessed 2026-10-15).

39. Olfati, M.; Krömer, P.; Fanati Rashidi, S.; Mirjalili, S.; Snášel, V. A goal programming-based algorithm for solving multi objective optimization problems. Ann. Oper. Res. 2025.

40. Huang, Z.; Yu, H.; Chu, X.; Peng, Z. A goal programming based model system for community energy plan. Energy 2017, 134, 893-901.

41. Shaw, K.; Shankar, R.; Yadav, S. S.; Thakur, L. S. Modeling a low-carbon garment supply chain. Prod. Plan. Control. 2013, 24, 851-65.

42. Kellner, F.; Lienland, B.; Utz, S. A multi‐criteria decision‐making approach for assembling optimal powertrain technology portfolios in low GHG emission environments. J. Ind. Ecol. 2021, 25, 1412-29.

43. ISO. ISO 14040:2006. Environmental management - Life cycle assessment - Principles and framework. https://www.iso.org/standard/37456.html. (accessed 2026-10-15).

44. ISO. ISO 14044:2006 - Environmental management - Life cycle assessment - Requirements and guidelines. https://www.iso.org/standard/38498.html. (accessed 2026-10-15).

45. Jones, D.; Tamiz, M. Practical goal programing, 1st ed.; Springer US; 2010.

46. ISO. ISO 13374-2:2007. Condition monitoring and diagnostics of machines - Data processing, communication and presentation. Part 2: Data processing. https://www.iso.org/standard/36645.html. (accessed 2026-10-15).

47. Kritzinger, W.; Karner, M.; Traar, G.; Henjes, J.; Sihn, W. Digital twin in manufacturing: a categorical literature review and classification. IFAC. PapersOnLine. 2018, 51, 1016-22.

48. Lu, Y.; Xu, X. Cloud-based manufacturing equipment and big data analytics to enable on-demand manufacturing services. Robot. Comput. Integr. Manuf. 2019, 57, 92-102.

49. Schmitt, J.; Bönig, J.; Borggräfe, T.; Beitinger, G.; Deuse, J. Predictive model-based quality inspection using machine learning and edge cloud computing. Adv. Eng. Inform. 2020, 45, 101101.

50. Thiede, S.; Sullivan, B.; Damgrave, R.; Lutters, E. Real-time locating systems (RTLS) in future factories: technology review, morphology and application potentials. Procedia. CIRP. 2021, 104, 671-6.

51. Chemweno, P.; Sullivan, B. P.; Bermperidis, G.; Thiede, S. Exploring the added-value of integrating real-time location systems for tracking critical maintenance tools. Procedia. CIRP. 2022, 107, 902-7.

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