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Smart Logistics and Data-driven Decision-Making Processes in Cyber-Physical Manufacturing Systems

Smart Logistics and Data-driven Decision-Making Processes in Cyber-Physical Manufacturing Systems Despite the relevance of the relationship between smart logistics and data-driven decision-making processes, only limited research has been conducted on this topic. Using and replicating data from AMG World, The Boston Consulting Group, Deloitte, KSM, MIT Sloan Management Review, PwC, and SME, I performed analyses and made estimates regarding how manufacturers are addressing smart manufacturing (%), adoption level of artificial intelligence in organizations (%), and manufacturing modernization priorities (%). The results of a study based on data collected from 4,200 respondents provide support for my research model. Using the structural equation modeling and employing the probability sampling technique, I gathered and analyzed data through a self-administrated questionnaire. JEL Codes: E24; J21; J54; J64 Keywords: smart logistics; big data; cyber-physical manufacturing system http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Economics, Management, and Financial Markets Addleton Academic Publishers

Smart Logistics and Data-driven Decision-Making Processes in Cyber-Physical Manufacturing Systems

Gavin Sheares
Economics, Management, and Financial Markets , Volume 15 (1): 7 – Jan 1, 2020
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Publisher
Addleton Academic Publishers
Copyright
© 2009 Addleton Academic Publishers
ISSN
1842-3191
eISSN
1938-212X
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Abstract

Despite the relevance of the relationship between smart logistics and data-driven decision-making processes, only limited research has been conducted on this topic. Using and replicating data from AMG World, The Boston Consulting Group, Deloitte, KSM, MIT Sloan Management Review, PwC, and SME, I performed analyses and made estimates regarding how manufacturers are addressing smart manufacturing (%), adoption level of artificial intelligence in organizations (%), and manufacturing modernization priorities (%). The results of a study based on data collected from 4,200 respondents provide support for my research model. Using the structural equation modeling and employing the probability sampling technique, I gathered and analyzed data through a self-administrated questionnaire. JEL Codes: E24; J21; J54; J64 Keywords: smart logistics; big data; cyber-physical manufacturing system

Journal

Economics, Management, and Financial Markets – Addleton Academic Publishers

Published: Jan 1, 2020

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