Journal



Frequency: Quarterly

ISSN (Online): 2789-5181

ISSN (Print) : 2789-732X




Journal of Enterprise and Business Intelligence

Sustainable Manufacturing for Industry 4.0 Technologies: Perspective of the Future



Journal of Enterprise and Business Intelligence

Received On : 10 May 2023

Revised On : 10 July 2023

Accepted On : 25 August 2023

Published On : 05 April 2024

Volume 04, Issue 02

Pages : 061-072




DOI
https://doi.org/10.53759/5181/JEBI202404007
Article Views

Abstract

Throughout history, manufacturing has consistently been at the forefront of technical progress, seeing the evolution from steam engines through cyber-physical systems, electricity, IoT, microprocessors, AI, automation, computers, and now. In the context of promoting growth of economy and generating lasting value in industries, sustainable manufacturing comprises the three essential components of manufacturing, namely processes, products, and systems. In order for manufacturing to be deemed sustainable, it is essential that these three components, when examined individually, illustrate the advantages in terms of environmental, economic, and social aspects. The primary objective of sustainable manufacturing is to produce things of superior quality while minimizing resource consumption and ensuring the safety of customers, employees, and local communities. This article explores the future direction of research in the domains of Industry 4.0 and sustainable manufacturing technology. Upon reviewing the extant literature, six key areas emerge as important subjects for further inquiry. These focal points are elucidated, along with the identified gaps in knowledge that need more exploration. Relevant papers for this research were identified using keywords such as "Sustainability," "Industry 4.0," "sustainable manufacturing," "manufacturing sustainability," or "smart manufacturing."


Keywords

Smart manufacturing, Industry 4.0, Manufacturing Sustainability, Industrial Manufacturing.


  1. J. P. Edwards, B. Kuhn-Sherlock, B. T. Dela Rue, and C. R. Eastwood, “Short communication: Technologies and milking practices that reduce hours of work and increase flexibility through milking efficiency in pasture-based dairy farm systems,” Journal of Dairy Science, vol. 103, no. 8, pp. 7172–7179, Aug. 2020, doi: 10.3168/jds.2019-17941.
  2. W. U. Mulk, “An Overview of Conventional Techniques and Recent Advancements in CO2 Capture Technologies,” Aspects in Mining & Mineral Science, vol. 11, no. 3, May 2023, doi: 10.31031/amms.2023.11.000761.
  3. P. Haack and A. Rasche, “The Legitimacy of Sustainability Standards: A Paradox Perspective,” Organization Theory, vol. 2, no. 4, p. 263178772110494, Oct. 2021, doi: 10.1177/26317877211049493.
  4. Z. Liang and M. Yu, “Exchange Rate Movements and Exporter Profitability: Empirical Evidence from Chinese Manufacturing Sectors,” China Economic Journal, vol. 7, no. 2, pp. 214–220, May 2014, doi: 10.1080/17538963.2014.928971.
  5. Ashadi, J. Priyana, Basikin, A. Triastuti, and N. H. P. S. Putro, Teacher Education and Professional Development In Industry 4.0. CRC Press, 2020.
  6. T. Aagaard, H. Lund, and C. Juhl, “Optimizing literature search in systematic reviews – are MEDLINE, EMBASE and CENTRAL enough for identifying effect studies within the area of musculoskeletal disorders?,” BMC Medical Research Methodology, vol. 16, no. 1, Nov. 2016, doi: 10.1186/s12874-016-0264-6.
  7. Md. G. Mortoja, T. Yigitcanlar, and S. Mayere, “What is the most suitable methodological approach to demarcate peri-urban areas? A systematic review of the literature,” Land Use Policy, vol. 95, p. 104601, Jun. 2020, doi: 10.1016/j.landusepol.2020.104601.
  8. H. Ruoslahti and A. Trent, “Organizational Learning in the Academic Literature – Systematic Literature Review,” Information & Security: An International Journal, vol. 46, no. 1, pp. 65–78, 2020, doi: 10.11610/isij.4605.
  9. S. Bruce, “Open Access for Scholars Left Behind: The Issue of Limited Access to Data and Scholarship,” International Information & Library Review, vol. 50, no. 3, pp. 236–243, Jul. 2018, doi: 10.1080/10572317.2018.1491708.
  10. G. Drogaris, “Learning from major accidents involving dangerous substances,” Safety Science, vol. 16, no. 2, pp. 89–113, Apr. 1993, doi: 10.1016/0925-7535(93)90008-2.
  11. T. Nad, “Systematisches Lean Management,” Zeitschrift für wirtschaftlichen Fabrikbetrieb, vol. 105, no. 4, pp. 299–302, Apr. 2010, doi: 10.3139/104.110299.
  12. M. Zairul and Z. Zaremohzzabieh, “Thematic Trends in Industry 4.0 Revolution Potential towards Sustainability in the Construction Industry,” Sustainability, vol. 15, no. 9, p. 7720, May 2023, doi: 10.3390/su15097720.
  13. F. Galati and B. Bigliardi, “Industry 4.0: Emerging themes and future research avenues using a text mining approach,” Computers in Industry, vol. 109, pp. 100–113, Aug. 2019, doi: 10.1016/j.compind.2019.04.018.
  14. G. Xu, G. Hou, and J. Zhang, “Digital Sustainable Entrepreneurship: A Digital Capability Perspective through Digital Innovation Orientation for Social and Environmental Value Creation,” Sustainability, vol. 14, no. 18, p. 11222, Sep. 2022, doi: 10.3390/su141811222.
  15. A. Sanders, C. Elangeswaran, and J. Wulfsberg, “Industry 4.0 implies lean manufacturing: Research activities in industry 4.0 function as enablers for lean manufacturing,” Journal of Industrial Engineering and Management, vol. 9, no. 3, p. 811, Sep. 2016, doi: 10.3926/jiem.1940.
  16. J. T. Blake, “On the use of Operational Research for managing platelet inventory and ordering,” Transfusion, vol. 49, no. 3, pp. 396–401, Feb. 2009, doi: 10.1111/j.1537-2995.2008.02061.x.
  17. A. Bagula, O. Ajayi, and H. Maluleke, “Cyber Physical Systems Dependability Using CPS-IOT Monitoring,” Sensors, vol. 21, no. 8, p. 2761, Apr. 2021, doi: 10.3390/s21082761.
  18. J. Park, “Smart Factory and Cyber-Physical Systems: Analysis of CPS Case Study,” Regional Industry Review, vol. 44, no. 1, pp. 161–181, Feb. 2021, doi: 10.33932/rir.44.1.7.
  19. Z. Liu, D. Sun, M. Zhao, H. Zhao, S. Wang, and X. Liao, “Cyber‐physical description and CPS‐based pinning approach of mixed traffic,” IET Intelligent Transport Systems, vol. 16, no. 3, pp. 344–362, Dec. 2021, doi: 10.1049/itr2.12147.
  20. G. Şişman, “Implementing Lean Six Sigma methodology to reduce the logistics cost: a case study in Turkey,” International Journal of Lean Six Sigma, vol. 14, no. 3, pp. 610–629, Aug. 2022, doi: 10.1108/ijlss-02-2022-0054.
  21. J. Park, M. Gofman, F. Wu, and Y.-H. Choi, “Challenges of wireless sensor networks for Internet of thing applications,” International Journal of Distributed Sensor Networks, vol. 12, no. 8, p. 155014771666550, Aug. 2016, doi: 10.1177/1550147716665506.
  22. S. Nalluri, S. Ramasubbareddy, and G. Kannayaram, “Cloud Application Security Based on Enhanced MD5 Algorithm,” Journal of Computational and Theoretical Nanoscience, vol. 16, no. 5, pp. 2022–2027, May 2019, doi: 10.1166/jctn.2019.7843.
  23. M. Pech and D. Vaněček, “Methods of Lean Production to Improve Quality in Manufacturing,” Quality Innovation Prosperity, vol. 22, no. 2, p. 01, Jul. 2018, doi: 10.12776/qip.v22i2.1096.
  24. Q. Deng and K.-D. Thoben, “A Systematic Procedure for Utilization of Product Usage Information in Product Development,” Information, vol. 13, no. 6, p. 267, May 2022, doi: 10.3390/info13060267.
  25. M. H. Mohamaddiah, A. Abdullah, S. Subramaniam, and M. Hussin, “A Survey on Resource Allocation and Monitoring in Cloud Computing,” International Journal of Machine Learning and Computing, pp. 31–38, Feb. 2014, doi: 10.7763/ijmlc.2014.v4.382.
  26. C. Mathieu, C. Puech, and H. Yahia, “Average efficiency of data structures for binary image processing,” Information Processing Letters, vol. 26, no. 2, pp. 89–93, Oct. 1987, doi: 10.1016/0020-0190(87)90043-3.
  27. A. Shalihin, “Peningkatan Kualitas Layanan Sertifikasi Halal Menggunakan Value Stream Mapping (VSM),” Engineering and Technology International Journal, vol. 4, no. 01, pp. 45–51, Mar. 2022, doi: 10.55642/eatij.v4i01.186.
  28. T. Hayashi and Y. Ohsawa, “Matrix-Based Method for Inferring Elements in Data Attributes Using a Vector Space Model,” Information, vol. 10, no. 3, p. 107, Mar. 2019, doi: 10.3390/info10030107.
  29. P. Basu and P. K. Dan, “Pivoting Lean Manufacturing through Industry 4.0 in the Indian Context,” Asian Journal of Engineering and Applied Technology, vol. 8, no. 2, pp. 1–7, May 2019, doi: 10.51983/ajeat-2019.8.2.1147.
  30. H. Maria Aslam and D. A. Siddiqui, “The Era of Industry 4.0 Technologies and Sustainable Organizational Performance of Pakistan’s Garment Industry: The Role of Lean Manufacturing and Green Supply Chain Management Practices with the Moderating Effect of Sustainability Culture,” SSRN Electronic Journal, 2023, Published, doi: 10.2139/ssrn.4431689.
  31. M. Fathi and M. Ghobakhloo, “Enabling Mass Customization and Manufacturing Sustainability in Industry 4.0 Context: A Novel Heuristic Algorithm for in-Plant Material Supply Optimization,” Sustainability, vol. 12, no. 16, p. 6669, Aug. 2020, doi: 10.3390/su12166669.
  32. W. Luo, “Evaluating Tourist Destination Performance: Expanding the Sustainability Concept,” Sustainability, vol. 10, no. 2, p. 516, Feb. 2018, doi: 10.3390/su10020516.
  33. A. J. R. Torres and F. Mahmoodi, “Outsourcing decision in manufacturing supply chains considering production failure and operating costs,” International Journal of Integrated Supply Management, vol. 4, no. 2, p. 141, 2008, doi: 10.1504/ijism.2008.016615.
  34. P. Danese, P. Romano, and A. Vinelli, “Exploring New Supply Chain Strategies in the Pharmaceutical Industry,” Supply Chain Forum: An International Journal, vol. 5, no. 1, pp. 12–23, Jan. 2004, doi: 10.1080/16258312.2004.11517123.
  35. T.-Y. Eng, “The Influence of a Firm’s Cross-Functional Orientation on Supply Chain Performance,” The Journal of Supply Chain Management, vol. 41, no. 4, pp. 4–16, Nov. 2005, doi: 10.1111/j.1745-493x.2005.04104002.x.
  36. T. B. H. Tran and A. D. Vu, “From customer value co-creation behaviour to customer perceived value,” Journal of Marketing Management, pp. 1–34, Apr. 2021, doi: 10.1080/0267257x.2021.1908398.
  37. S. C. Calvert, D. D. Heikoop, G. Mecacci, and B. van Arem, “A human centric framework for the analysis of automated driving systems based on meaningful human control,” Theoretical Issues in Ergonomics Science, vol. 21, no. 4, pp. 478–506, Dec. 2019, doi: 10.1080/1463922x.2019.1697390.
  38. D. W. Parker and K. A. Russell, “Outsourcing and Inter/lntra Supply Chain Dynamics: Strategic Management Issues,” The Journal of Supply Chain Management, vol. 40, no. 4, pp. 56–68, Sep. 2004, doi: 10.1111/j.1745-493x.2004.tb00178.x.
  39. “Diagnostic Performance of Deep Learning for Angle Closre,” Case Medical Research, Jan. 2020, Published, doi: 10.31525/ct1-nct04242108.
  40. B. Schmidt, K. Gandhi, L. Wang, and D. Galar, “Context preparation for predictive analytics – a case from manufacturing industry,” Journal of Quality in Maintenance Engineering, vol. 23, no. 3, pp. 341–354, Aug. 2017, doi: 10.1108/jqme-10-2016-0050.
  41. F. Civerchia, S. Bocchino, C. Salvadori, E. Rossi, L. Maggiani, and M. Petracca, “Industrial Internet of Things monitoring solution for advanced predictive maintenance applications,” Journal of Industrial Information Integration, vol. 7, pp. 4–12, Sep. 2017, doi: 10.1016/j.jii.2017.02.003.
  42. M. Fazil Ahmad, “The Impact of Big Data Processing Framework for Artificial Intelligence within Corporate Marketing Communication,” International Journal of Engineering & Technology, vol. 7, no. 4.34, p. 384, Dec. 2018, doi: 10.14419/ijet.v7i4.34.26879.
  43. D. Christefa, H. Mawengkang, and M. Zarlis, “Data-Driven Decision Making In Large Scale Production Planning,” SinkrOn, vol. 7, no. 3, pp. 2068–2071, Aug. 2022, doi: 10.33395/sinkron.v7i3.11600.
  44. T. Sakao and A. K. Nordholm, “Requirements for a Product Lifecycle Management System Using Internet of Things and Big Data Analytics for Product-as-a-Service,” Frontiers in Sustainability, vol. 2, Aug. 2021, doi: 10.3389/frsus.2021.735550.
  45. H. Lee, K. Ryu, and Y. Cho, “A Framework of a Smart Injection Molding System Based on Real-time Data,” Procedia Manufacturing, vol. 11, pp. 1004–1011, 2017, doi: 10.1016/j.promfg.2017.07.206.
  46. T. Y. Win, H. Tianfield, and Q. Mair, “Big Data Based Security Analytics for Protecting Virtualized Infrastructures in Cloud Computing,” IEEE Transactions on Big Data, vol. 4, no. 1, pp. 11–25, Mar. 2018, doi: 10.1109/tbdata.2017.2715335.
  47. K. Zhu, Z. Zhou, B. Vogel-Heuser, and T. R. Kurfess, “Issues on Smart Sensing and Information Processing in Advanced Manufacturing,” Mechatronics, vol. 31, pp. 1–2, Oct. 2015, doi: 10.1016/j.mechatronics.2015.06.009.
  48. S. S. Kumar and Ms. V. Kirthika, “Big Data Analytics Architecture and Challenges, Issues of Big Data Analytics,” International Journal of Trend in Scientific Research and Development, vol. Volume-1, no. Issue-6, pp. 669–673, Oct. 2017, doi: 10.31142/ijtsrd4673.
  49. O. Holovashchenko, “The problem of ensuring the long-term sustainability of research infrastructures in today’s conditions,” Law and innovations, no. 3 (43), pp. 26–30, Sep. 2023, doi: 10.37772/2518-1718-2023-3(43)-4.
  50. S. Janakiraman, “DATA ACQUISITION AND MANAGEMENT,” Problems of Gathering, Treatment and Transportation of Oil and Oil Products, no. 1, p. 19, Mar. 2019, doi: 10.17122/ntj-oil-2019-1-19-28.
  51. P. Helman, “A family of NP-complete data aggregation problems,” Acta Informatica, vol. 26, no. 5, pp. 485–499, Mar. 1989, doi: 10.1007/bf00289148.
  52. A. Ali, J. Qadir, R. ur Rasool, A. Sathiaseelan, A. Zwitter, and J. Crowcroft, “Big data for development: applications and techniques,” Big Data Analytics, vol. 1, no. 1, Jul. 2016, doi: 10.1186/s41044-016-0002-4.
  53. B. M. Savage, “Managing quality data,” Total Quality Management, vol. 7, no. 6, pp. 667–674, Dec. 1996, doi: 10.1080/09544129610540.
  54. M. Yousif, “Cloud Computing Reliability—Failure is an Option,” IEEE Cloud Computing, vol. 5, no. 3, pp. 4–5, May 2018, doi: 10.1109/mcc.2018.032591610.
  55. S. Jackson and E. Brodal, “Optimization of the Energy Consumption of a Carbon Capture and Sequestration Related Carbon Dioxide Compression Processes,” Energies, vol. 12, no. 9, p. 1603, Apr. 2019, doi: 10.3390/en12091603.
  56. H. R. Valette, F. Laloe, and J. L. Fur, “Introduction to the key issue concerning the use of sustainable development indicators,” International Journal of Sustainable Development, vol. 10, no. 1/2, p. 4, 2007, doi: 10.1504/ijsd.2007.014410.
  57. A. van Wynsberghe, “Sustainable AI: AI for sustainability and the sustainability of AI,” AI and Ethics, vol. 1, no. 3, pp. 213–218, Feb. 2021, doi: 10.1007/s43681-021-00043-6.
  58. M. Frutos-Pascual and B. G. Zapirain, “Review of the Use of AI Techniques in Serious Games: Decision Making and Machine Learning,” IEEE Transactions on Computational Intelligence and AI in Games, vol. 9, no. 2, pp. 133–152, Jun. 2017, doi: 10.1109/tciaig.2015.2512592.
  59. N. Alruwais and M. Zakariah, “Evaluating Student Knowledge Assessment Using Machine Learning Techniques,” Sustainability, vol. 15, no. 7, p. 6229, Apr. 2023, doi: 10.3390/su15076229.
  60. N. Hamdoun and K. Rguibi, “Impact of AI and Machine Learning on Financial Industry: Application on Moroccan Credit Risk Scoring,” Journal of Advanced Research in Dynamical and Control Systems, vol. 11, no. 11-SPECIAL ISSUE, pp. 1041–1048, Nov. 2019, doi: 10.5373/jardcs/v11sp11/20193134.
  61. H. Çalış and H. Fidan, “Motor Condition Monitoring Based on Time-Frequency Analysis of Stator Current Signal,” International Journal of Modeling and Optimization, vol. 5, no. 1, pp. 36–39, Feb. 2015, doi: 10.7763/ijmo.2015.v5.432.
  62. V. Torra, Y. Narukawa, J. Yin, and J. Long, “Technologies for Decision Making and AI Applications,” International Journal of Intelligent Systems, vol. 28, no. 6, pp. 523–523, Apr. 2013, doi: 10.1002/int.21590.
  63. R. Khoshkangini et al., “Early Prediction of Quality Issues in Automotive Modern Industry,” Information, vol. 11, no. 7, p. 354, Jul. 2020, doi: 10.3390/info11070354.

Acknowledgements

The author(s) received no financial support for the research, authorship, and/or publication of this article.


Funding

No funding was received to assist with the preparation of this manuscript.


Ethics declarations

Conflict of interest

The authors have no conflicts of interest to declare that are relevant to the content of this article.


Availability of data and materials

No data available for above study.


Author information

Contributions

All authors have equal contribution in the paper and all authors have read and agreed to the published version of the manuscript.


Corresponding author


Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution NoDerivs is a more restrictive license. It allows you to redistribute the material commercially or non-commercially but the user cannot make any changes whatsoever to the original, i.e. no derivatives of the original work. To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-nd/4.0/


Cite this article

Dong Diane E. Davis, “Sustainable Manufacturing for Industry 4.0 Technologies: Perspective of the Future”, Journal of Enterprise and Business Intelligence, vol.4, no.2, pp. 061-072, April 2024. doi: 10.53759/5181/JEBI202404007.


Copyright

© 2024 Dong Diane E. Davis. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


                            

Journals

Policies & Ethics

Resources

Information

Copyright © Design by AnaPub Publications