{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T11:19:51Z","timestamp":1778757591886,"version":"3.51.4"},"reference-count":23,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T00:00:00Z","timestamp":1762473600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T00:00:00Z","timestamp":1762473600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Syst Assur Eng Manag"],"published-print":{"date-parts":[[2026,4]]},"DOI":"10.1007\/s13198-025-03045-3","type":"journal-article","created":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T08:39:33Z","timestamp":1762504773000},"page":"1393-1410","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Harnessing big data analytics for industry 4.0: a hybrid architecture of revolutionary technologies and predictive implementation in manufacturing"],"prefix":"10.1007","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4439-1317","authenticated-orcid":false,"given":"Md. Abu","family":"Salman","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-2512-1604","authenticated-orcid":false,"given":"Krishnapada","family":"Mondal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-2139-8045","authenticated-orcid":false,"given":"Md Shajahan","family":"Kobir","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-4841-5346","authenticated-orcid":false,"given":"Mst. Khurshida Khatun","family":"Khusi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-9363-184X","authenticated-orcid":false,"given":"Md","family":"Rifat","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-7603-2216","authenticated-orcid":false,"given":"Shahnewaz Ibne","family":"Ghani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,11,7]]},"reference":[{"key":"3045_CR1","doi-asserted-by":"publisher","first-page":"71634","DOI":"10.1109\/ACCESS.2023.3295694","volume":"11","author":"W Chen","year":"2023","unstructured":"Chen W, Milosevic Z, Rabhi F, Berry A (2023) Real-time analytics: concepts, architectures, and ML\/AI considerations. IEEE Access 11:71634\u201371657. https:\/\/doi.org\/10.1109\/ACCESS.2023.3295694","journal-title":"IEEE Access"},{"key":"3045_CR2","volume-title":"Industry 40 challenges and solutions for the digital transformation and use of exponential technologies","author":"AG Deloitte","year":"2015","unstructured":"Deloitte AG (2015) Industry 40 challenges and solutions for the digital transformation and use of exponential technologies. McKinsey Global Institute"},{"key":"3045_CR3","doi-asserted-by":"publisher","first-page":"298","DOI":"10.1016\/j.aej.2023.12.065","volume":"88","author":"S Elkateb","year":"2024","unstructured":"Elkateb S, M\u00e9twalli A, Shendy A, Abu-Elanien A (2024) Machine learning and IoT-based predictive maintenance approach for industrial applications. Alex Eng J 88:298\u2013309. https:\/\/doi.org\/10.1016\/j.aej.2023.12.065","journal-title":"Alex Eng J"},{"key":"3045_CR4","unstructured":"Evans D (2011) The internet of things: how the next evolution of the internet is changing everything. Cisco Internet Business Solutions Group (IBSG)"},{"key":"3045_CR5","doi-asserted-by":"publisher","DOI":"10.1063\/5.0001031","volume":"2227","author":"QY Farizal","year":"2020","unstructured":"Farizal QY, Cornelis CI, Dachyar M (2020) Fast moving product demand forecasting model with multi linear regression. AIP Conf Proc 2227:030007. https:\/\/doi.org\/10.1063\/5.0001031","journal-title":"AIP Conf Proc"},{"issue":"2","key":"3045_CR6","doi-asserted-by":"publisher","first-page":"1814","DOI":"10.47852\/AIA42021814","volume":"4","author":"S Goswami","year":"2024","unstructured":"Goswami S, Mondal S, Sarkar S, Gupta K, Sahoo SK, Halder R (2024) Artificial intelligence enabled supply chain management: unlocking new opportunities and challenges. Artif Intell Appl 4(2):1814. https:\/\/doi.org\/10.47852\/AIA42021814","journal-title":"Artif Intell Appl"},{"key":"3045_CR7","volume-title":"Operations management: sustainability and supply chain management","author":"J Heizer","year":"2017","unstructured":"Heizer J, Render B, Munson C (2017) Operations management: sustainability and supply chain management, 12th edn. Pearson, New York","edition":"12"},{"key":"3045_CR8","unstructured":"Kagermann H, Wahlster W, Helbig J (2013) Securing the future of German manufacturing industry: recommendations for implementing the strategic initiative Industrie 4.0. Final report of the Industrie 4.0 working group. Acatech\u2014National Academy of Science and Engineering, Munich, p 678"},{"key":"3045_CR9","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-64653-4_9","volume-title":"Guide to security in SDN and NFV. Computer communications and networks","author":"R Khondoker","year":"2017","unstructured":"Khondoker R, Larbig P, Scheuermann D, Weber F, Bayarou K (2017) Addressing industry 40 security by software-defined networking. In: Zhu S, Scott-Hayward S, Jacquin L, Hill R (eds) Guide to security in SDN and NFV. Computer communications and networks. Springer, Cham. https:\/\/doi.org\/10.1007\/978-3-319-64653-4_9"},{"key":"3045_CR10","doi-asserted-by":"publisher","first-page":"122","DOI":"10.30630\/joiv.1.4.40","volume":"1","author":"MS Kumar","year":"2017","unstructured":"Kumar MS, Jayagopal P (2017) Analysis of network function virtualization and software defined virtualization. Int J Inform vis 1:122\u2013126. https:\/\/doi.org\/10.30630\/joiv.1.4.40","journal-title":"Int J Inform vis"},{"key":"3045_CR11","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.procir.2014.02.001","volume":"16","author":"J Lee","year":"2014","unstructured":"Lee J, Kao HA, Yang S (2014) Service innovation and smart analytics for industry 4.0 and big data environment. Procedia CIRP 16:3\u20138. https:\/\/doi.org\/10.1016\/j.procir.2014.02.001","journal-title":"Procedia CIRP"},{"key":"3045_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jii.2017.04.005","volume":"6","author":"Y Lu","year":"2017","unstructured":"Lu Y (2017) Industry 4.0: a survey on technologies, applications and open research issues. J Ind Inf Integr 6:1\u201310. https:\/\/doi.org\/10.1016\/j.jii.2017.04.005","journal-title":"J Ind Inf Integr"},{"key":"3045_CR13","unstructured":"Manyika J, Chui M, Brown B, Bughin J, Dobbs R, Roxburgh C, Byers A (2011) Big data: the next frontier for innovation, competition, and productivity. McKinsey Global Institute"},{"key":"3045_CR14","doi-asserted-by":"publisher","first-page":"1343","DOI":"10.1016\/j.jclepro.2018.11.025","volume":"210","author":"S Ren","year":"2019","unstructured":"Ren S, Zhang Y, Liu Y, Sakao T, Huisingh D, Almeida CMVB (2019) A comprehensive review of big data analytics throughout product lifecycle to support sustainable smart manufacturing: a framework, challenges and future research directions. J Clean Prod 210:1343\u20131365. https:\/\/doi.org\/10.1016\/j.jclepro.2018.11.025","journal-title":"J Clean Prod"},{"key":"3045_CR15","unstructured":"Robinson S, Chai W, Stedman C (2021) Big data analytics. TechTarget. https:\/\/www.techtarget.com\/searchbusinessanalytics\/definition\/big-data-analytics"},{"key":"3045_CR16","doi-asserted-by":"publisher","unstructured":"Sabharwal A, (2011) Russell S Norvig P Artificial intelligence: a modern approach, third edition. Artif Intell 175:935\u2013937. https:\/\/doi.org\/10.1016\/j.artint.2011.01.005","DOI":"10.1016\/j.artint.2011.01.005"},{"key":"3045_CR17","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-14544-6_4","volume-title":"Data management, analytics and innovation. Advances in intelligent systems and computing","author":"A Sharma","year":"2020","unstructured":"Sharma A, Pandey H (2020) Big data and analytics in industry 4.0. In: Sharma N, Chakrabarti A, Balas VE (eds) Data management, analytics and innovation. Advances in intelligent systems and computing. Springer. https:\/\/doi.org\/10.1007\/978-3-030-14544-6_4"},{"key":"3045_CR18","unstructured":"Shewale R (2024) Big data statistics for 2024. DemandSage. https:\/\/www.demandsage.com\/big-data-statistics\/"},{"key":"3045_CR19","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1016\/j.comnet.2015.12.017","volume":"101","author":"S Wang","year":"2016","unstructured":"Wang S, Wan J, Zhang D, Li D, Zhang C (2016) Towards smart factory for industry 4.0: a self-organized multi-agent system with big data based feedback and coordination. Comput Netw 101:158\u2013168. https:\/\/doi.org\/10.1016\/j.comnet.2015.12.017","journal-title":"Comput Netw"},{"issue":"1\u20132","key":"3045_CR20","doi-asserted-by":"publisher","first-page":"604","DOI":"10.1080\/00207543.2017.1401239","volume":"56","author":"X Wang","year":"2018","unstructured":"Wang X, Ong SK, Nee AYC (2018) A comprehensive survey of ubiquitous manufacturing research. Int J Prod Res 56(1\u20132):604\u2013628. https:\/\/doi.org\/10.1080\/00207543.2017.1401239","journal-title":"Int J Prod Res"},{"key":"3045_CR21","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1080\/21693277.2016.1192517","volume":"4","author":"T Wuest","year":"2016","unstructured":"Wuest T, Weimer D, Irgens C, Thoben KD (2016) Machine learning in manufacturing: advantages, challenges, and applications. Prod Manuf Res 4:23\u201345. https:\/\/doi.org\/10.1080\/21693277.2016.1192517","journal-title":"Prod Manuf Res"},{"key":"3045_CR22","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1016\/j.ymssp.2018.05.050","volume":"115","author":"R Zhao","year":"2019","unstructured":"Zhao R, Yan R, Chen Z, Mao K, Wang P, Gao R (2019) Deep learning and its applications to machine health monitoring. Mech Syst Signal Process 115:213\u2013237. https:\/\/doi.org\/10.1016\/j.ymssp.2018.05.050","journal-title":"Mech Syst Signal Process"},{"key":"3045_CR23","doi-asserted-by":"publisher","unstructured":"Zhou K, Liu T, Zhou L (2015) Industry 4.0: towards future industrial opportunities and challenges. In: 2015 12th international conference on fuzzy systems and knowledge discovery (FSKD). IEEE, Zhangjiajie, pp 2147\u20132152. https:\/\/doi.org\/10.1109\/FSKD.2015.7382284","DOI":"10.1109\/FSKD.2015.7382284"}],"container-title":["International Journal of System Assurance Engineering and Management"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13198-025-03045-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13198-025-03045-3","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13198-025-03045-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T10:34:51Z","timestamp":1778754891000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13198-025-03045-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,7]]},"references-count":23,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2026,4]]}},"alternative-id":["3045"],"URL":"https:\/\/doi.org\/10.1007\/s13198-025-03045-3","relation":{},"ISSN":["0975-6809","0976-4348"],"issn-type":[{"value":"0975-6809","type":"print"},{"value":"0976-4348","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,7]]},"assertion":[{"value":"23 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 October 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 November 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors confirm that there are no conflicts of interest or competing interests related to the publication of this manuscript.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}