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
2024

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.
2024
2022
De Giovanni, Pietro; Belvedere, Valeria; Grando, Alberto
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11565/4053796
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