The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Smart factory performance and Industry 4.0
Open accessAbstract
Existing literature on the Industry 4.0 concept does not empirically verify if, how, and for which types of firms, a greater openness to enabling technologies of Industry 4.0 provides further opportunities. This study analyzes the causal relationship between this degree of openness and performance, with an empirical analysis based on a sample representing local manufacturing units. Performance is measured by the extent of opportunities businesses obtain. The degree of openness is investigated using two indicators: breadth, or the number of technologies used; and depth, or the number of value chain stages involved. The regression models demonstrate that: (1) breadth and (2) depth of Industry 4.0 allow greater opportunities, and (3) micro-level local units achieve best performances. Verifying the opportunities for companies with Industry 4.0 is extremely relevant, as investments in Industry 4.0 are high in terms of costs, the acquisition of new skills, and the risks of obsolescence to enable better strategic decisions. This work also provides a scope for future analyses of this topic conducted on panel data. Despite the limited application of Industry 4.0, this study's results can encourage managers and policy-makers to implement a wider range of enabling technologies in the various stages of the value chain.
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Lasi, H., Fettke, P., Kemper, H.-G., Feld, T., & Hoffmann, M. (2014) · Business & Information Systems Engineering · doi:10.1007/s12599-014-0334-4 · Cited by 4,276
Industry 4.0: A survey on technologies, applications and open research issues
Lu, Y. (2017) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2017.04.005 · Cited by 3,012
The long tail: why the future of business is selling less of more
(2007) · Choice Reviews Online · doi:10.5860/choice.44-2783 · Cited by 2,869
How ‘big data’ can make big impact: findings from a systematic review and a longitudinal case study
Fosso Wamba, S., Akter, S., Edwards, A., Chopin, G., & Gnanzou, D. (2015) · International Journal of Production Economics · doi:10.1016/j.ijpe.2014.12.031 · Cited by 1,905
Wang, G., Gunasekaran, A., Ngai, E. W. T., & Papadopoulos, T. (2016) · International Journal of Production Economics · doi:10.1016/j.ijpe.2016.03.014 · Cited by 1,545
Fortune favors the prepared: How SMEs approach business model innovations in Industry 4.0
Müller, J. M., Buliga, O., & Voigt, K.-I. (2018) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2017.12.019 · Cited by 1,262
Cited by
Digital twin paradigm: A systematic literature review
Semeraro, C., Lezoche, M., Panetto, H., & Dassisti, M. (2021) · Computers in Industry · doi:10.1016/j.compind.2021.103469 · Cited by 986
Sima, V., Gheorghe, I. G., Subić, J., & Nancu, D. (2020) · Sustainability · doi:10.3390/su12104035 · Cited by 786
Digital Twin as a Service (DTaaS) in Industry 4.0: An Architecture Reference Model
Aheleroff, S., Xu, X., Zhong, R. Y., & Lu, Y. (2021) · Advanced Engineering Informatics · doi:10.1016/j.aei.2020.101225 · Cited by 658
Zizic, M. C., Mladineo, M., Gjeldum, N., & Celent, L. (2022) · Energies · doi:10.3390/en15145221 · Cited by 420
Impact of artificial intelligence on employees working in industry 4.0 led organizations
Malik, N., Tripathi, S. N., Kar, A. K., & Gupta, S. (2022) · International Journal of Manpower · doi:10.1108/ijm-03-2021-0173 · Cited by 416
Metadata
- Authors
- Büchi, G., Cugno, M., & Castagnoli, R.
- Venue
- Technological Forecasting and Social Change
- Publisher
- Elsevier BV
- Published
- 2020-01
- Volume
- 150
- Pages
- 119790
- ISSN
- 0040-1625 (ISSN-L)
- OpenAlex
- W2987040097
- Cited by
- 645
- References
- 6
- FWCI
- 59.65
- Type
- journal article
- Language
- en
- Open access
- hybrid
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