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Author:

Ghadimi, Pezhman (Ghadimi, Pezhman.) | Donnelly, Oisin (Donnelly, Oisin.) | Sar, Kubra (Sar, Kubra.) | Wang, Chao (Wang, Chao.) (Scholars:王超) | Azadnia, Amir Hossein (Azadnia, Amir Hossein.)

Indexed by:

SSCI EI Scopus

Abstract:

Industry 4.0 is anticipated to revolutionize the manufacturing sector through a digital transformation. With this transformation, many benefits are expected, such as the automation and decentralization of production processes. Nevertheless, enterprises face considerable risks upon successful implementation of Industry 4.0. The uncertainties regarding these risks are currently hindering enterprises' implementation of Industry 4.0. Although several studies have investigated the adoption of Industry 4.0-related technologies, far too little attention has been devoted to identifying and analyzing the risk factors associated with the adoption of these technologies in manufacturing, especially in Irish industry. Therefore, this study contributes to the existing knowledge by proposing a systematic approach to identifying and ranking these risk factors along with recommending policies to mitigate the highest risks. Fourteen risk factors are identified, and the opinions of 12 industry experts across the Irish manufacturing sector are used to rank these risk factors using an adjusted best-worst method. The lack of standards and lack of methodological approaches was the highest-ranking risk factor, with the risk to capital investment, the lack of talent, the uncertainty in economic benefits and the potential delay to the manufacturing process ranking in the top 5. Policy recommendations to mitigate the highest-ranking risks are proposed based on an analysis of the Irish government's current Industry 4.0 policy. Governments should aim to assist industries in establishing comprehensive standards to increase the rate of successful Industry 4.0 implementation.

Keyword:

0 Risk factor Irish industry Manufacturing Industry 4 Best-worst method

Author Community:

  • [ 1 ] [Ghadimi, Pezhman]Univ Coll Dublin, Sch Mech & Mat Engn, Lab Adv Mfg Simulat & Robot, Dublin 4, Ireland
  • [ 2 ] [Donnelly, Oisin]Univ Coll Dublin, Sch Mech & Mat Engn, Lab Adv Mfg Simulat & Robot, Dublin 4, Ireland
  • [ 3 ] [Sar, Kubra]Univ Coll Dublin, Sch Mech & Mat Engn, Lab Adv Mfg Simulat & Robot, Dublin 4, Ireland
  • [ 4 ] [Wang, Chao]Beijing Univ Technol, Coll Econ & Management, Res Base Beijing Modern Mfg Dev, Beijing, Peoples R China
  • [ 5 ] [Azadnia, Amir Hossein]Letterkenny Inst Technol, Dept Business Studies, Letterkenny, Ireland

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Source :

TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE

ISSN: 0040-1625

Year: 2021

Volume: 175

ESI Discipline: SOCIAL SCIENCES, GENERAL;

ESI HC Threshold:53

JCR Journal Grade:1

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 1

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