Many manufacturing firms today are considering whether to adopt one or more technologies associated with the Industry 4.0 vision. Yet, neither academic nor practitioner literature provide much guidance about these technology choices. This article aims at understanding how Industry 4.0 (I4.0) technologies can impact operational performance and at discovering possible synergies among I4.0 technologies. More specifically, the research seeks to analyze the full network of relationships between I4.0 technologies and operational performance, assess the impact of single technologies on performance, construct portfolios of technologies depending on the firms' operational targets, and identify possible operational improvements to achieve by adjusting a portfolio of I4.0 technologies. To pursue these research goals, we use a Bayesian network and machine-learning algorithms to drive firms' investment decisions based on I4.0 technologies. The analysis has been conducted on a sample of 289 Italian manufacturing firms and companies that provide services directly related to manufacturing activities. Our findings provide managerial insights and intuitions to improve operational performance by either adopting single I4.0 technologies or creating an ad hoc portfolio of technologies.

The Selection of Industry 4.0 Technologies Through Bayesian Networks: An Operational Perspective / De Giovanni, Pietro; Belvedere, Valeria; Grando, Alberto. - In: IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT. - ISSN 0018-9391. - (In corso di stampa), pp. 1-16. [10.1109/TEM.2022.3200868]

The Selection of Industry 4.0 Technologies Through Bayesian Networks: An Operational Perspective

Pietro De Giovanni;
In corso di stampa

Abstract

Many manufacturing firms today are considering whether to adopt one or more technologies associated with the Industry 4.0 vision. Yet, neither academic nor practitioner literature provide much guidance about these technology choices. This article aims at understanding how Industry 4.0 (I4.0) technologies can impact operational performance and at discovering possible synergies among I4.0 technologies. More specifically, the research seeks to analyze the full network of relationships between I4.0 technologies and operational performance, assess the impact of single technologies on performance, construct portfolios of technologies depending on the firms' operational targets, and identify possible operational improvements to achieve by adjusting a portfolio of I4.0 technologies. To pursue these research goals, we use a Bayesian network and machine-learning algorithms to drive firms' investment decisions based on I4.0 technologies. The analysis has been conducted on a sample of 289 Italian manufacturing firms and companies that provide services directly related to manufacturing activities. Our findings provide managerial insights and intuitions to improve operational performance by either adopting single I4.0 technologies or creating an ad hoc portfolio of technologies.
Supply chains, Cloud computing, Portfolios, Fourth Industrial Revolution, Big Data, Peer-to-peer computing, Companies, Bayesian networks, Industry 4.0 technologies, machine learning, operational performance, portfolio of technologies
The Selection of Industry 4.0 Technologies Through Bayesian Networks: An Operational Perspective / De Giovanni, Pietro; Belvedere, Valeria; Grando, Alberto. - In: IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT. - ISSN 0018-9391. - (In corso di stampa), pp. 1-16. [10.1109/TEM.2022.3200868]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11385/223538
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