Leveraging the Capabilities of Industry 4.0 for Improving Energy Efficiency in Smart Factories
Open accessAbstract
Factories use many manufacturing processes that consume a lot of energy and highly contribute to greenhouse gas emissions. The introduction of the concept of Industrial Internet in USA and Industry 4.0 in Europe offers many opportunities to reduce energy consumption in these factories. Introducing and utilizing smart techniques for the applications pertinent to manufacturing processes within the Industry 4.0 domain can offer many benefits for reducing energy consumption in smart factories. This paper investigates and discusses these opportunities and benefits. This paper also discusses the roles of Industry 4.0 technologies in enabling these opportunities. Consequently, introducing these capabilities will help significantly reduce both production costs and greenhouse gas emissions. This paper then provides a benefit analysis that shows the advantages of such leverage. In addition, this paper offers an enabling architecture and its components that include a cyber-physical system manufacturing services' layer, a fog manufacturing services' layer, a cloud manufacturing services' layer, and a blockchain-based service-oriented middleware to support such opportunities.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Blockchain can improve data reliability by creating trust among involved entities, securing shared data and providing data timestamping.
“Blockchain can improve data reliability by creating trust among the involved entities [96], securing the shared data and providing data timestamping [19].”
From A Review on the Application of Blockchain to the Next Generation of Cybersecure Industry 4.0 Smart Factories (2019) · cites this paper as [19]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Fog computing and its role in the internet of things
Bonomi, F., Milito, R., Zhu, J., & Addepalli, S. (2012) · Proceedings of the first edition of the MCC workshop on Mobile cloud computing · doi:10.1145/2342509.2342513 · Cited by 6,244 · see citing works
A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems
Lee, J., Bagheri, B., & Kao, H.-A. (2015) · Manufacturing Letters · doi:10.1016/j.mfglet.2014.12.001 · Cited by 5,113 · see citing works
Lasi, H., Fettke, P., Kemper, H.-G., Feld, T., & Hoffmann, M. (2014) · Business & Information Systems Engineering · doi:10.1007/s12599-014-0334-4 · Cited by 4,276 · see citing works
Farhangi, H. (2010) · IEEE Power and Energy Magazine · doi:10.1109/mpe.2009.934876 · Cited by 2,967 · see citing works
Decentralizing Privacy: Using Blockchain to Protect Personal Data
Zyskind, G., Nathan, O., & Pentland, A. '. (2015) · 2015 IEEE Security and Privacy Workshops · doi:10.1109/spw.2015.27 · Cited by 2,617 · see citing works
Reconfigurable Manufacturing Systems
Koren, Y., Heisel, U., Jovane, F., Moriwaki, T., Pritschow, G., Ulsoy, A. G., & Brussel, H. V. (1999) · CIRP Annals · doi:10.1016/s0007-8506(07)63232-6 · Cited by 2,077 · see citing works
Manufacturing engineering and technology
(1990) · Choice Reviews Online · doi:10.5860/choice.27-5145 · Cited by 1,602 · see citing works
About The Importance of Autonomy and Digital Twins for the Future of Manufacturing
Rosen, R., von Wichert, G., Lo, G., & Bettenhausen, K. D. (2015) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2015.06.141 · Cited by 1,474 · cited 1× · see citing works
“the manufacturing field [52].”
Smart manufacturing: Past research, present findings, and future directions
Kang, H. S., Lee, J. Y., Choi, S., Kim, H. J., Park, J. H., Son, J. Y., Kim, B. H., & Noh, S. D. (2016) · International Journal of Precision Engineering and Manufacturing-Green Technology · doi:10.1007/s40684-016-0015-5 · Cited by 1,364 · see citing works
Industry 4.0: Towards future industrial opportunities and challenges
Zhou, K., Liu, T., & Zhou, L. (2015) · International Conference on Fuzzy Systems and Knowledge Discovery (FSKD) · doi:10.1109/fskd.2015.7382284 · Cited by 1,257 · see citing works
Towards energy and resource efficient manufacturing: A processes and systems approach
Duflou, J. R., Sutherland, J. W., Dornfeld, D. A., Herrmann, C. A., Jeswiet, J., Kara, S., Hauschild, M. Z., & Kellens, K. (2012) · CIRP Annals · doi:10.1016/j.cirp.2012.05.002 · Cited by 1,168 · see citing works
A Categorical Framework of Manufacturing for Industry 4.0 and Beyond
Qin, J., Liu, Y., & Grosvenor, R. I. (2016) · Procedia CIRP · doi:10.1016/j.procir.2016.08.005 · Cited by 1,043 · see citing works
Katipamula, S., & Brambley, M. R. (2005) · Science and Technology for the Built Environment · doi:10.1080/10789669.2005.10391123 · Cited by 942 · see citing works
Sustainable manufacturing: trends and research challenges
Garetti, M., & Taisch, M. (2011) · Production Planning & Control · doi:10.1080/09537287.2011.591619 · Cited by 942 · see citing works
Shrouf, F., Ordieres-Meré, J. B., & Miragliotta, G. (2014) · doi:10.1109/ieem.2014.7058728 · Cited by 915 · see citing works
CCIoT-CMfg: Cloud Computing and Internet of Things-Based Cloud Manufacturing Service System
Tao, F., Cheng, Y., Xu, L. D., Li, Z., & Li, B. H. (2014) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2014.2306383 · Cited by 724 · see citing works
The Industry 4.0 revolution and the future of Manufacturing Execution Systems (MES)
Almada-Lobo, F. (2016) · Journal of Innovation Management · doi:10.24840/2183-0606_003.004_0003 · Cited by 622 · see citing works
Operational methods for minimization of energy consumption of manufacturing equipment
Mouzon, G., Yildirim, M. B., & Twomey, J. M. (2007) · International Journal of Production Research · doi:10.1080/00207540701450013 · Cited by 599 · see citing works
Industrial Internet: A Survey on the Enabling Technologies, Applications, and Challenges
Li, J., Yu, F. R., Deng, G., Luo, C., Ming, Z., & Yan, Q. (2017) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2017.2691349 · Cited by 472 · see citing works
Shrouf, F., & Miragliotta, G. (2015) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2015.03.055 · Cited by 385 · see citing works
Li, W., & Kara, S. (2011) · Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture · doi:10.1177/2041297511398541 · Cited by 335 · see citing works
Bruzzone, A. A. G., Anghinolfi, D., Paolucci, M., et al. (2012) · CIRP Annals · doi:10.1016/j.cirp.2012.03.084 · Cited by 329 · see citing works
Thollander, P., Danestig, M., & Rohdin, P. (2007) · Energy Policy · doi:10.1016/j.enpol.2007.06.013 · Cited by 317 · see citing works
Industry 4.0 as a part of smart cities
Lom, M., Přibyl, O., & Svítek, M. (2016) · doi:10.1109/scsp.2016.7501015 · Cited by 309 · see citing works
Energy oriented simulation of manufacturing systems – Concept and application
Herrmann, C. A., Thiede, S., Kara, S., & Hesselbach, J. (2011) · CIRP Annals · doi:10.1016/j.cirp.2011.03.127 · Cited by 294 · see citing works
Missaoui, R., Joumaa, H., Ploix, S., & Bacha, S. (2014) · Energy and Buildings · doi:10.1016/j.enbuild.2013.12.018 · Cited by 274 · see citing works
A framework for modelling energy consumption within manufacturing systems
Seow, Y., & Rahimifard, S. (2011) · CIRP journal of manufacturing science and technology · doi:10.1016/j.cirpj.2011.03.007 · Cited by 239 · see citing works
The Handbook of Logistics and Distribution Management: Understanding the Supply Chain
Rushton, A., Croucher, P., & Baker, P. (2014) · Cited by 237 · see citing works
Energy Efficiency of Manufacturing Processes: A Critical Review
Fysikopoulos, A., Papacharalampopoulos, A., Pastras, G., et al. (2013) · Procedia CIRP · doi:10.1016/j.procir.2013.06.044 · Cited by 188 · see citing works
Industry 4.0 Development and Application of Intelligent Manufacturing
Cheng, G., Liu, L.-T., Qiang, X., & Liu, Y. (2016) · International Conference on Information System and Artificial Intelligence · doi:10.1109/isai.2016.0092 · Cited by 186 · see citing works
Service-oriented middleware for the Future Internet: state of the art and research directions
Issarny, V., Georgantas, N., Hachem, S., et al. (2011) · Journal of Internet Services and Applications · doi:10.1007/s13174-011-0021-3 · Cited by 166 · see citing works
Applying Blockchain in Industry 4.0 Applications
Mohamed, N., & Al‐Jaroodi, J. (2019) · IEEE Annual Computing and Communication Workshop and Conference (CCWC) · doi:10.1109/ccwc.2019.8666558 · Cited by 130 · see citing works
Self-citationManufacturing Process Selection Handbook
(2013) · Elsevier eBooks · doi:10.1016/c2011-0-07343-x · Cited by 108 · see citing works
Applications of Internet of Things in manufacturing
Yang, C., Shen, W., & Wang, X. (2016) · International Conference on Computer Supported Cooperative Work in Design (CSCWD) · doi:10.1109/cscwd.2016.7566069 · Cited by 107 · see citing works
On a Predictive Maintenance Platform for Production Systems
Efthymiou, K., Papakostas, N., Mourtzis, D., et al. (2012) · Procedia CIRP · doi:10.1016/j.procir.2012.07.039 · Cited by 105 · see citing works
Automated FDD of multiple-simultaneous faults (MSF) and the application to building chillers
Han, H., Gu, B. Q., Hong, Y., et al. (2011) · Energy and Buildings · doi:10.1016/j.enbuild.2011.06.011 · Cited by 94 · see citing works
Service-oriented middleware: A survey
Al‐Jaroodi, J., & Mohamed, N. (2011) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2011.07.013 · Cited by 88 · see citing works
Self-citationA survey on service-oriented middleware for wireless sensor networks
Mohamed, N., & Al‐Jaroodi, J. (2011) · Service Oriented Computing and Applications · doi:10.1007/s11761-011-0083-x · Cited by 75 · see citing works
Self-citationFault detection and diagnosis of automated manufacturing systems
Viswanadham, N., & Johnson, T. L. (2003) · Proceedings of the IEEE Conference on Decision & Control, including the Symposium on Adaptive Processes · doi:10.1109/cdc.1988.194747 · Cited by 75 · see citing works
Sequeira, H., Carreira, P., Goldschmidt, T., et al. (2014) · IEEE/ACM International Conference on Utility and Cloud Computing (UCC) · doi:10.1109/ucc.2014.79 · Cited by 53 · see citing works
Energy use analysis and local benchmarking of manufacturing lines
ElMaraghy, H. A., Youssef, A. M. A., Marzouk, A. M., et al. (2016) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2015.12.026 · Cited by 52 · see citing works
A service-oriented middleware framework for manufacturing industry 4.0
Al‐Jaroodi, J., Mohamed, N., & Jawhar, I. (2018) · ACM SIGBED Review · doi:10.1145/3292384.3292389 · Cited by 43 · see citing works
Self-citationIndustrial Applications of Blockchain
Al‐Jaroodi, J., & Mohamed, N. (2019) · IEEE Annual Computing and Communication Workshop and Conference (CCWC) · doi:10.1109/ccwc.2019.8666530 · Cited by 38 · see citing works
Self-citationA Framework for IoT-Based Monitoring and Diagnosis of Manufacturing Systems
Yen, I., Zhang, S., Bastani, F., & Zhang, Y. (2017) · IEEE International Symposium on Service-Oriented System Engineering (SOSE) · doi:10.1109/sose.2017.26 · Cited by 36 · see citing works
Liu, J., Jiang, P., & Leng, J. (2017) · CASE · doi:10.1109/coase.2017.8256072 · Cited by 33 · see citing works
Anomaly detection and productivity analysis for cyber-physical systems in manufacturing
Saez, M., Maturana, F., Barton, K. L., & Tilbury, D. M. (2017) · IEEE International Conference on Automation Science and Engineering (CASE) · doi:10.1109/coase.2017.8256070 · Cited by 30 · see citing works
A Survey of Expert Systems for Equipment Maintenance and Diagnostics
Scherer, W. T., & White, C. C. (1989) · Springer eBooks · doi:10.1007/978-1-4899-2471-1_16 · Cited by 26 · see citing works
Reliability of cyber physical systems with focus on building management systems
Lazarova‐Molnar, S., Shaker, H. R., & Mohamed, N. (2016) · IEEE International Performance, Computing, and Communications Conference · doi:10.1109/pccc.2016.7820666 · Cited by 24 · see citing works
Self-citationMaintenance an opportunity for energy saving
Darabnia, B., & Demichela, M. (2013) · Chemical engineering transactions · doi:10.3303/cet1332044 · Cited by 23 · see citing works
Demichela, M., Baldissone, G., & Darabnia, B. (2018) · Processes · doi:10.3390/pr6030025 · Cited by 20 · see citing works
Collaborative Data Analytics for Industry 4.0: Challenges, Opportunities and Models
Lazarova‐Molnar, S., Mohamed, N., & Al‐Jaroodi, J. (2018) · International Conference on Enterprise Systems (ES) · doi:10.1109/es.2018.00023 · Cited by 19 · see citing works
Self-citationEnergy Cloud: Services for Smart Buildings
Mohamed, N., Al‐Jaroodi, J., & Lazarova‐Molnar, S. (2017) · Springer eBooks · doi:10.1007/978-3-319-62238-5_5 · Cited by 18 · see citing works
Self-citationA statistical approach linking energy management to maintenance and production factors
Al-Ghanim, A. M. (2003) · Journal of Quality in Maintenance Engineering · doi:10.1108/13552510310466828 · Cited by 15 · see citing works
Fault Detection and Diagnosis of Automated Manufacturing Systems
Viswanadham, N., & Johnson, T. L. (1988) · IFAC Proceedings Volumes · doi:10.1016/s1474-6670(17)54683-2 · Cited by 12 · see citing works
Key factor identification for energy consumption analysis
Medina-Oliva, G., Voisin, A., Monnin, M., et al. (2014) · PHM Society European Conference · doi:10.36001/phme.2014.v2i1.1534 · Cited by 4 · see citing works
Flagged first, then by citations
Research works printed but not linked: 4
- P. Taylor, M. Francoeur, O. L. D'Ortigue, C. Tam, and N. Trudeau, ''World- wide trends in energy use and efficiency,'' in Proc. Paris, Int. Energy Agency (IEA), 2008, pp. 1-36.
- Annual Energy Review 2010, EIA, Washington, DC, USA, 2010, p. 38.
- T. Gutowski, J. Dahmus, and A. Thiriez, ''Electrical energy requirements for manufacturing processes,'' in Proc. 13th CIRP Int. Conf. Life Cycle Eng., Leuven, Belgium, vol. 31, 2006, pp. 623-638.
- S. Kalpakjian, K. S. VijaiSekar, and S. R. Schmid, Manufacturing Engi- neering and Technology. London, U.K.: Pearson, 2014.
Cited by
Sorted by citations
Digital twin: values, challenges and enablers from a modeling perspective
Rasheed, A., San, O., & Kvamsdal, T. (2020) · IEEE Access · doi:10.1109/access.2020.2970143 · Cited by 1,952 · see citing works
Blockchain for industry 4.0: a comprehensive review
Bodkhe, U., Tanwar, S., Parekh, K., Khanpara, P., Tyagi, S., Kumar, N., & Alazab, M. (2020) · IEEE Access · doi:10.1109/access.2020.2988579 · Cited by 766 · see citing works
Fernandez-Carames, T. M., & Fraga-Lamas, P. (2019) · IEEE Access · doi:10.1109/access.2019.2908780 · Cited by 321 · see citing works
Iliuţă, M.-E., Moisescu, M.-A., Pop, E., Ionita, A.-D., Caramihai, S.-I., & Mitulescu, T.-C. (2024) · Applied Sciences · doi:10.3390/app14135454 · Cited by 134 · see citing works
SDN–IoT empowered intelligent framework for industry 4.0 applications during COVID-19 pandemic
Rahman, A., Chakraborty, C., Anwar, A., Karim, M. R., Islam, M. J., Kundu, D., Rahman, Z., & Band, S. S. (2022) · Cluster Computing · doi:10.1007/s10586-021-03367-4 · Cited by 121 · see citing works
Langone, R., Cuzzocrea, A., & Skantzos, N. (2020) · Data & Knowledge Engineering · doi:10.1016/j.datak.2020.101850 · Cited by 81 · see citing works
- Authors
- Mohamed, N., Al-Jaroodi, J., & Lazarova-Molnar, S.
- Venue
- IEEE Access
- Published
- 2019
- Volume
- 7
- Pages
- 18008-18020
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2913662467
- Cited by
- 168
- References
- 56
- Citations by year
- 2026: 15 · 2025: 16 · 2024: 25 · 2023: 27 · 2022: 22 · 2021: 28 · 2020: 22 · 2019: 13
- FWCI
- 15.69
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- University of Southern Denmark Research Portal (University of Southern Denmark) (submittedVersion, cc-by)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Energy Efficiency and Management
- Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
- Keywords
- Industry 4.0, smart factory, energy efficiency, manufacturing energy consumption, greenhouse gas emissions, Industrial Internet of Things, cyber-physical systems, fog computing, cloud manufacturing, blockchain, service-oriented middleware, production cost reduction
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review