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Alessandro Ancarani
Researcher at University of Catania
Publications - 70
Citations - 1765
Alessandro Ancarani is an academic researcher from University of Catania. The author has contributed to research in topics: Reshoring & Supply chain. The author has an hindex of 17, co-authored 64 publications receiving 1380 citations.
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Motivations of manufacturing reshoring: an interpretative framework
Luciano Fratocchi,Alessandro Ancarani,Paolo Barbieri,Carmela Di Mauro,Guido Nassimbeni,Marco Sartor,Matteo Vignoli,Andrea Zanoni +7 more
TL;DR: In this article, an interpretative framework for the analysis of reshoring motivations is proposed, which can be applied to analyze motivations for reshoring, as they emerge from extant literature and from new evidence collected.
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Prior to reshoring: A duration analysis of foreign manufacturing ventures
TL;DR: In this paper, the authors investigated the determinants of the duration of manufacturing offshore experiences by US and European firms prior to reshoring, and found that the likelihood of termination of offshore manufacturing and the return to the home country may be accelerated by technology-based industries, small firms, shrinking cost differentials and the psychic distance between home and host country, the organizational archetypes, and quality related motivations.
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Backshoring strategy and the adoption of Industry 4.0: Evidence from Europe
TL;DR: In this paper, an analysis of the competitive priorities that may lead backshoring companies to adopt new technologies is developed and tested using secondary data from 495 relocation initiatives to Europe.
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Towards quality e‐service in the public sector:: The evolution of web sites in the local public service sector
TL;DR: In this paper, the authors defined and tested an approach for the evaluation of the quality of e-service provided in the local public service sector (LPS) using an approach based on the relationship between benefits for customers and web site technological complexity, assumed to be represented by the level of organisational changes adopted by firms.
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A Neural Networks Approach for Deriving Irrigation Reservoir Operating Rules
TL;DR: In this paper, a neural networks approach is applied to the derivation of the operating rules of an irrigation supply reservoir operating rules are determined as a two-step process: first, a dynamic programming technique, which determines the optimal releases by minimizing the sum of squared deficits, assumed as objective function, subject to various constraints is applied, and the resulting releases from the reservoir are expressed as a function of significant variables by neural networks.