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Cognitive systems optimally modify production settings based on managing uncertainty and sensory inputs. This work uses the Internet of Things-based decision support system with cognitive automation (IoT-DSS-CA) for industrial informatics across the board, including data collection, transmission, processing, and storage. Incorporating the elements frequently neglected during digital transformation, the suggested method uses the business process management (BPM) paradigm to give a systematic approach that industrial organizations may employ to aid their path towards Industry 4.0. The proposed mechanism is thoroughly investigated and evaluated compared to an original solution using several sensing and decision-making features in industrial parameter settings determined by Simple Additive Weighting (SAW) and Analytic Hierarchy Process (AHP).<\/jats:p>","DOI":"10.3233\/idt-230636","type":"journal-article","created":{"date-parts":[[2024,7,5]],"date-time":"2024-07-05T11:47:22Z","timestamp":1720180042000},"page":"2389-2406","source":"Crossref","is-referenced-by-count":0,"title":["Digital Industry transformation strategy analysis and decision-based on intelligent decision technology"],"prefix":"10.1177","volume":"18","author":[{"given":"Yishu","family":"Liu","sequence":"first","affiliation":[{"name":"School of Economics and Management, Fuzhou University of International Studies and Trade, Fuzhou, Fujian, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jia","family":"Hou","sequence":"additional","affiliation":[{"name":"Business School, Xiamen Institute of Technology, Xiamen, Fujian, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"key":"10.3233\/IDT-230636_ref1","doi-asserted-by":"crossref","first-page":"718","DOI":"10.1016\/j.jbusres.2021.09.020","article-title":"Building skills in the context of digital transformation: How industry digital maturity drives proactive skill development","volume":"139","author":"Ostmeier","year":"2022","journal-title":"Journal of Business Research."},{"issue":"1","key":"10.3233\/IDT-230636_ref2","doi-asserted-by":"crossref","first-page":"2781","DOI":"10.1080\/1331677X.2021.1980731","article-title":"Enterprise digital transformation and production efficiency: Mechanism analysis and empirical research","volume":"35","author":"Zhang","year":"2022","journal-title":"Economic research-Ekonomska istra\u017eivanja."},{"key":"10.3233\/IDT-230636_ref3","doi-asserted-by":"crossref","first-page":"102414","DOI":"10.1016\/j.technovation.2021.102414","article-title":"Digital transformation of industrial businesses: A dynamic capability approach","volume":"113","author":"Ghosh","year":"2022","journal-title":"Technovation."},{"issue":"2","key":"10.3233\/IDT-230636_ref4","doi-asserted-by":"crossref","first-page":"100173","DOI":"10.1016\/j.jik.2022.100173","article-title":"Digital transformation, labour share, and industrial heterogeneity","volume":"7","author":"Chen","year":"2022","journal-title":"Journal of Innovation & Knowledge."},{"key":"10.3233\/IDT-230636_ref5","doi-asserted-by":"crossref","first-page":"130997","DOI":"10.1016\/j.jclepro.2022.130997","article-title":"Co-design of digital transformation and sustainable development strategies-What socio-metabolic and industrial ecology research can contribute","volume":"343","author":"Pauliuk","year":"2022","journal-title":"Journal of Cleaner Production."},{"key":"10.3233\/IDT-230636_ref6","unstructured":"Suyunovich MK, Shakhriyorovich KJ. 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