ABSTRACT. Empirical evidence on deep learning-enabled smart process planning has been scarcely documented in the literature. Using and replicating data from Deloitte, KSM, PwC, SME, Statista, and Tractica, we performed analyses and made estimates regarding top challenges to implementing smart manufacturing solutions (%) and business organizations’ reasons for adopting artificial intelligence (%). Data were analyzed using structural equation modeling.
JEL codes: E24; J21; J54; J64
Keywords: smart process planning; cyber-physical system-based manufacturing
How to cite: Valaskova, Katarina, Odile Throne, Pavol Kral, and Lucia Michalkova (2020). “Deep Learning-enabled Smart Process Planning in Cyber-Physical System-based Manufacturing,” Journal of Self-Governance and Management Economics 8(1): 121–127. doi:10.22381/JSME8120205
Received 9 January 2020 • Received in revised form 16 March 2020
Accepted 17 March 2020 • Available online 28 March 2020