{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T05:38:42Z","timestamp":1775194722086,"version":"3.50.1"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T00:00:00Z","timestamp":1750204800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T00:00:00Z","timestamp":1750204800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Discov Artif Intell"],"DOI":"10.1007\/s44163-025-00363-0","type":"journal-article","created":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T09:14:19Z","timestamp":1750238059000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Harnessing AI for smart manufacturing: insights from Industry 4.0"],"prefix":"10.1007","volume":"5","author":[{"given":"Daniel G.","family":"Lindberg","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,6,18]]},"reference":[{"key":"363_CR1","unstructured":"Gregolinska E, Khanam R, Lefort F, Parthasarathy P. Capturing the true value of Industry 4.0. McKinsey and Company. 2022. https:\/\/www.mckinsey.com\/capabilities\/operations\/our-insights\/capturing-the-true-value-of-industry-four-point-zero."},{"key":"363_CR2","unstructured":"Bresnahan T. Artificial intelligence technologies and aggregate growth prospects. In: Prospects for Economic Growth in the United States (G. Zodrow and J. Diamond, eds). Cambridge: Cambridge University Press; 2018."},{"key":"363_CR3","doi-asserted-by":"publisher","DOI":"10.7208\/chicago\/9780226833125.001.0001","volume-title":"Power and prediction: the disruptive economics of artificial intelligence","author":"A Agrawal","year":"2024","unstructured":"Agrawal A, Gans J, Goldfarb A. Power and prediction: the disruptive economics of artificial intelligence. Boston: Harvard Business Review Press; 2024."},{"issue":"1","key":"363_CR4","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1016\/0304-4076(94)01598-T","volume":"65","author":"TF Bresnahan","year":"1995","unstructured":"Bresnahan TF, Trajtenberg M. General purpose technologies \u2018engines of growth\u2019? J Econom. 1995;65(1):83\u2013108. https:\/\/doi.org\/10.1016\/0304-4076(94)01598-T.","journal-title":"J Econom"},{"key":"363_CR5","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/j.procir.2014.03.115","volume":"17","author":"L Monostori","year":"2014","unstructured":"Monostori L. Cyber-physical production systems: roots, expectations, an R&D challenges. Procedia CIRP. 2014;17:9\u201313. https:\/\/doi.org\/10.1016\/j.procir.2014.03.115.","journal-title":"Procedia CIRP"},{"issue":"8","key":"363_CR6","doi-asserted-by":"publisher","first-page":"2941","DOI":"10.1080\/00207543.2018.1444806","volume":"56","author":"Li Da Xu","year":"2018","unstructured":"Da Xu Li, Xu Eric L, Li Ling. Industry 4.0: State of the art and future trends. Int J Prod Res. 2018;56(8):2941\u201362. https:\/\/doi.org\/10.1080\/00207543.2018.1444806.","journal-title":"Int J Prod Res"},{"key":"363_CR7","doi-asserted-by":"publisher","first-page":"312","DOI":"10.1016\/j.jmsy.2020.06.010","volume":"56","author":"Y Lu","year":"2020","unstructured":"Lu Y, Xun Xu, Wang L. Smart manufacturing process and system automation \u2013 a critical review of the standards and envisioned scenarios. J Manuf Syst. 2020;56:312\u201325. https:\/\/doi.org\/10.1016\/j.jmsy.2020.06.010.","journal-title":"J Manuf Syst"},{"issue":"10","key":"363_CR8","doi-asserted-by":"publisher","first-page":"1291","DOI":"10.1287\/mnsc.40.10.1291","volume":"40","author":"K Srinivasan","year":"1994","unstructured":"Srinivasan K, Kekre S, Mukhopadhyay T. Impact of electronic data interchange technology on JIT shipments. Manage Sci. 1994;40(10):1291\u2013304. https:\/\/doi.org\/10.1287\/mnsc.40.10.1291.","journal-title":"Manage Sci"},{"key":"363_CR9","unstructured":"Lindse, A. How AI and ML are transforming procurement. 2020. https:\/\/business.amazon.com\/en\/discover-more\/blog\/how-ai-and-ml-are-transforming-procurement."},{"key":"363_CR10","doi-asserted-by":"crossref","unstructured":"Varian HR. Artificial intelligence, economics, and industrial organization, in The Economics of Artificial Intelligence: An Agenda (A. Agrawal, J. Gans, and A. Goldfarb, eds.). Chicago: University of Chicago Press; 2019.","DOI":"10.7208\/chicago\/9780226613475.003.0016"},{"key":"363_CR11","first-page":"7","volume-title":"Industry 4.0 Managing the digital transformation. springer series in Advanced Manufacturing","author":"Baris Bayram","year":"2018","unstructured":"Bayram Baris, \u0130nce G\u00f6khan. Advances in robotics in the era of Industry 4.0. In: Industry 4.0 Managing the digital transformation. springer series in Advanced Manufacturing. Cham: Springer; 2018. p. 7\u20138."},{"key":"363_CR12","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1007\/978-981-15-6186-3","volume-title":"Edge AI Convergence of Edge Computing and Artificial Intelligence","author":"Xiaofei Wang","year":"2020","unstructured":"Wang Xiaofei, Han Yiwen, Leung Victor C.M., Niyato Dusit, Yan Xueqiang, Chen Xu. Edge AI Convergence of Edge Computing and Artificial Intelligence. Singapore: Springer; 2020. p. 8\u20139."},{"issue":"5","key":"363_CR13","doi-asserted-by":"publisher","first-page":"1117","DOI":"10.1108\/JMTM-09-2018-0283","volume":"31","author":"L Bosman","year":"2020","unstructured":"Bosman L, Hartman N, Sutherland J. How manufacturing firm characteristics can influence decision making for investing in Industry 4.0 technologies. J Manuf Technol Manage. 2020;31(5):1117\u201341. https:\/\/doi.org\/10.1108\/JMTM-09-2018-0283.","journal-title":"J Manuf Technol Manage"},{"key":"363_CR14","unstructured":"U.S. Census Bureau. SUSB Annual Data Tables by Establishment Industry. 2020. https:\/\/www.census.gov\/data\/tables\/2020\/econ\/susb\/2020-susb-annual.html."},{"issue":"3","key":"363_CR15","doi-asserted-by":"publisher","first-page":"458","DOI":"10.1108\/JMTM-05-2023-0190","volume":"35","author":"F Arcidiacono","year":"2024","unstructured":"Arcidiacono F, Schupp F. Investigating the impact of smart manufacturing on firms\u2019 operational and financial performance. J Manuf Technol Manag. 2024;35(3):458\u201379. https:\/\/doi.org\/10.1108\/JMTM-05-2023-0190.","journal-title":"J Manuf Technol Manag"},{"key":"363_CR16","doi-asserted-by":"publisher","DOI":"10.1108\/JMTM-07-2022-0269","author":"LS Angreani","year":"2024","unstructured":"Angreani LS, Vijaya A, Wicaksono H. Enhancing strategy for Industry 4.0 implementation through maturity models and standard reference architectures alignment. J Manuf Technol Manage. 2024. https:\/\/doi.org\/10.1108\/JMTM-07-2022-0269.","journal-title":"J Manuf Technol Manage"},{"key":"363_CR17","doi-asserted-by":"publisher","first-page":"969","DOI":"10.1111\/joes.12455","volume":"36","author":"L Abrardi","year":"2022","unstructured":"Abrardi L, Cambini C, Rondi L. Artificial intelligence, firms and consumer behavior: a survey. J EcoN Surv. 2022;36:969\u201391. https:\/\/doi.org\/10.1111\/joes.12455.","journal-title":"J EcoN Surv"},{"key":"363_CR18","first-page":"7","volume-title":"The economics of artificial intelligence: An Agenda","author":"Matt Taddy","year":"2019","unstructured":"Taddy Matt. The technological elements of artificial intelligence. In: Agrawal A, Gans J, Goldfarb A, editors. The economics of artificial intelligence: An Agenda. NY: Chicago University of Chicago Press; 2019. p. 7\u20139."},{"key":"363_CR19","doi-asserted-by":"publisher","DOI":"10.1093\/wentk\/9780190602383.001.0001","volume-title":"Artificial Intelligence: What Everyone Needs to Know","author":"J Kaplan","year":"2016","unstructured":"Kaplan J. Artificial Intelligence: What Everyone Needs to Know. Oxford: Oxford University Press; 2016."},{"issue":"1","key":"363_CR20","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1257\/mac.20170105","volume":"13","author":"William D Nordhaus","year":"2021","unstructured":"Nordhaus William D. Are we approaching an economic singularity? Information technology and the future of economic growth. Am Econ J Macroecon. 2021;13(1):299\u2013332. https:\/\/doi.org\/10.1257\/mac.20170105.","journal-title":"Am Econ J Macroecon"},{"key":"363_CR21","unstructured":"Hu K. ChatGPT sets record for fastest-growing user base - analyst note, Reuters, 2 February, 2023. https:\/\/www.reuters.com\/technology\/chatgpt-sets-record-fastest-growing-user-base-analyst-note-2023-02-01\/."},{"issue":"151","key":"363_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.jfineco.2023.103745","volume":"2024","author":"T Babina","year":"2024","unstructured":"Babina T, Fedyk A, He AX, Hodson J. Artificial intelligence, firm growth, and product innovation. J Financ Econ. 2024;2024(151): 103745. https:\/\/doi.org\/10.1016\/j.jfineco.2023.103745.","journal-title":"J Financ Econ"},{"issue":"7","key":"363_CR23","doi-asserted-by":"publisher","DOI":"10.2139\/ssrn.3829822","volume":"51","author":"C Rammer","year":"2022","unstructured":"Rammer C, Fern\u00e1ndez GP, Czarnitzki D. Artificial intelligence and industrial innovation: Evidence from German firm-level data. Res Policy. 2022;51(7): 104555. https:\/\/doi.org\/10.2139\/ssrn.3829822.","journal-title":"Res Policy"},{"key":"363_CR24","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1111\/jems.12524","volume":"33","author":"TF Bresnahan","year":"2024","unstructured":"Bresnahan TF. What innovation paths for AI to become a GPT? J Econ Manage Strateg. 2024;33:305\u201316. https:\/\/doi.org\/10.1111\/jems.12524.","journal-title":"J Econ Manage Strateg"},{"issue":"5","key":"363_CR25","doi-asserted-by":"publisher","first-page":"S71","DOI":"10.1086\/261725","volume":"98","author":"PM Romer","year":"1990","unstructured":"Romer PM. Endogenous technological change. J Political Econ. 1990;98(5):S71\u2013102. https:\/\/doi.org\/10.1086\/261725.","journal-title":"J Political Econ"},{"issue":"6","key":"363_CR26","doi-asserted-by":"publisher","first-page":"1126","DOI":"10.1086\/261856","volume":"100","author":"EL Glaeser","year":"1992","unstructured":"Glaeser EL, Kallal HD, Scheinkman JA, Schliefer A. Growth in Cities. J Polit Econ. 1992;100(6):1126\u201352. https:\/\/doi.org\/10.1086\/261856.","journal-title":"J Polit Econ"},{"key":"363_CR27","doi-asserted-by":"publisher","first-page":"1923882","DOI":"10.1080\/23322039.2021.1923882","volume":"9","author":"I Kuswardana","year":"2021","unstructured":"Kuswardana I, Dajalal N, Aulia FT, Damayanti A. The effect of knowledge spillover on productivity: evidence from manufacturing industry in Indonesia. Cogent Econ Finance. 2021;9:1923882. https:\/\/doi.org\/10.1080\/23322039.2021.1923882.","journal-title":"Cogent Econ Finance"},{"issue":"2","key":"363_CR28","doi-asserted-by":"publisher","first-page":"51","DOI":"10.3886\/E113926V1","volume":"28","author":"DW Nickerson","year":"2014","unstructured":"Nickerson DW, Rogers T. Political campaigns and big data. J Econ Perspect. 2014;28(2):51\u201374. https:\/\/doi.org\/10.3886\/E113926V1.","journal-title":"J Econ Perspect"},{"key":"363_CR29","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1057\/s11369-021-00224-5","volume":"56","author":"E Brynjolfsson","year":"2021","unstructured":"Brynjolfsson E, Jin W, McElheran K. The power of prediction: predictive analytics, workplace complements, and business performance. Bus Econ. 2021;56:217\u201339. https:\/\/doi.org\/10.1057\/s11369-021-00224-5.","journal-title":"Bus Econ"},{"key":"363_CR30","doi-asserted-by":"publisher","DOI":"10.1126\/science.aam9744","author":"CE Leiserson","year":"2020","unstructured":"Leiserson CE, Thompson NC, Emer JS, Zuszmaul BC, Lampson BW, Daniel S, Schardl TB. There\u2019s plenty of room at the Top What will drive computer performance after Moore\u2019s law? Science. 2020. https:\/\/doi.org\/10.1126\/science.aam9744.","journal-title":"Science"},{"issue":"9","key":"363_CR31","doi-asserted-by":"publisher","first-page":"2819","DOI":"10.1257\/aer.20191330","volume":"110","author":"CI Jones","year":"2020","unstructured":"Jones CI, Tonetti C. Nonrivalry and the economics of data. Am Econ Rev. 2020;110(9):2819\u201358. https:\/\/doi.org\/10.1257\/aer.20191330.","journal-title":"Am Econ Rev"},{"key":"363_CR32","doi-asserted-by":"publisher","DOI":"10.1177\/20539517211017308","author":"K Birch","year":"2021","unstructured":"Birch K, Cochrane DT, Ward C. Data as asset? The measurement, governance, and valuation of digital personal data by Big Tech. Big Data & Soc. 2021. https:\/\/doi.org\/10.1177\/20539517211017308.","journal-title":"Big Data & Soc"},{"key":"363_CR33","doi-asserted-by":"publisher","unstructured":"de Montjoye, Y-A, Heike Schweitzer, and Jacques Cr\u00e9mer. European Commission, Directorate-General for Competition, Competition policy for the digital era. Publications Office. 2019. https:\/\/data.europa.eu\/doi\/https:\/\/doi.org\/10.2763\/407537.","DOI":"10.2763\/407537"},{"issue":"6","key":"363_CR34","doi-asserted-by":"publisher","first-page":"2188","DOI":"10.1086\/705716","volume":"128","author":"D Acemoglu","year":"2020","unstructured":"Acemoglu D, Restrepo P. Robots and jobs: evidence from US labor markets. J Polit Econ. 2020;128(6):2188\u2013244. https:\/\/doi.org\/10.1086\/705716.","journal-title":"J Polit Econ"},{"issue":"S1","key":"363_CR35","doi-asserted-by":"publisher","first-page":"S293","DOI":"10.1086\/718327","volume":"40","author":"D Acemoglu","year":"2022","unstructured":"Acemoglu D, Autor D, Hazell J, Restrepo P. AI and jobs: evidence from online vacancies. J Labor Econ. 2022;40(S1):S293\u2013340. https:\/\/doi.org\/10.1086\/718327.","journal-title":"J Labor Econ"},{"key":"363_CR36","unstructured":"Dauth W, Findeisen S, S\u00fcdekum J, Woessner N. German Robots \u2013 The Impact of Industrial Robots on Workers. CEPR Discussion Paper. 2017; No. 12306. https:\/\/cepr.org\/publications\/dp12306."},{"key":"363_CR37","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1257\/pandp.20181019","volume":"108","author":"E Brynjolfsson","year":"2018","unstructured":"Brynjolfsson E, Mitchell T, Rock Da. What can machines learn and what does it mean for occupations and the economy? AEA Papers and Proceed. 2018;108:43\u20137.","journal-title":"AEA Papers and Proceed"},{"key":"363_CR38","unstructured":"Webb M. The impact of Artificial Intelligence on the labor market. Working Paper, Stanford University. 2020. https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3482150."},{"issue":"3","key":"363_CR39","doi-asserted-by":"publisher","first-page":"599","DOI":"10.1287\/isre.1110.0398","volume":"23","author":"P Tambe","year":"2012","unstructured":"Tambe P, Hitt LM. The productivity of information technology investments: new evidence from IT labor data. Inf Syst Res. 2012;23(3):599\u2013617. https:\/\/doi.org\/10.1287\/isre.1110.0398.","journal-title":"Inf Syst Res"},{"key":"363_CR40","doi-asserted-by":"publisher","DOI":"10.1186\/s41469-019-0050-0","author":"M Raj","year":"2019","unstructured":"Raj M, Seamans R. Primer on artificial intelligence and robotics. J Org Design. 2019. https:\/\/doi.org\/10.1186\/s41469-019-0050-0.","journal-title":"J Org Design"},{"key":"363_CR41","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1057\/s11369-021-00243-2","volume":"56","author":"H Varian","year":"2021","unstructured":"Varian H. Economics at Google. Bus Econ. 2021;56:195\u20139. https:\/\/doi.org\/10.1057\/s11369-021-00243-2.","journal-title":"Bus Econ"},{"key":"363_CR42","unstructured":"Chesbrough H. Why Companies Should Have Open Business Models. MIT Sloan Management Review. 2007. Sloanreview.mit.edu\/article\/why-companies-should-have-open-business-models\/."},{"key":"363_CR43","unstructured":"Center for Security and Emerging Technology. Georgetown University. 2024. https:\/\/cat.eto.tech."},{"issue":"1","key":"363_CR44","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1257\/mac.20180386","volume":"13","author":"E Brynjolfsson","year":"2021","unstructured":"Brynjolfsson E, Rock D, Syverson C. The productivity j-curve: how intangibles complement general purpose technologies. Am Econ J Macroecon. 2021;13(1):333\u201372. https:\/\/doi.org\/10.1257\/mac.20180386.","journal-title":"Am Econ J Macroecon"},{"key":"363_CR45","volume-title":"Prediction machines: the simple economics of artificial intelligence","author":"A Agrawal","year":"2018","unstructured":"Agrawal A, Gans J, Goldfarb A. Prediction machines: the simple economics of artificial intelligence. Boston: Harvard Business Review Press; 2018."},{"issue":"3","key":"363_CR46","doi-asserted-by":"publisher","first-page":"546","DOI":"10.2307\/2109500","volume":"74","author":"HY Kim","year":"1992","unstructured":"Kim HY. The translog production function and variable returns to scale. Rev Econ Stat. 1992;74(3):546\u201352. https:\/\/doi.org\/10.2307\/2109500.","journal-title":"Rev Econ Stat"}],"container-title":["Discover Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44163-025-00363-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44163-025-00363-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44163-025-00363-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T09:14:25Z","timestamp":1750238065000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44163-025-00363-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,18]]},"references-count":46,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["363"],"URL":"https:\/\/doi.org\/10.1007\/s44163-025-00363-0","relation":{},"ISSN":["2731-0809"],"issn-type":[{"value":"2731-0809","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,18]]},"assertion":[{"value":"4 December 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 June 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 June 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":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics, Consent to Participate, and Consent to Publish"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}],"article-number":"111"}}