Where the Rubber Meets the Road. Industry 4.0 Among SMEs in the Automotive Sector

Arcidiacono, Francesco; Ancarani, Alessandro; Mauro, Carmela Di; Schupp, Florian

doi:10.1109/emr.2019.2932965

Cited by 30 publications

(30 citation statements)

References 9 publications

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“…However, such developments require a business vision and a clear system architecture to wrap up anticipate key implementation steps. While the IoT ONE database does not offer supporting information in this direction, IoT and Industry 4.0 literature offers examples of the importance of internal motivations (vision, perceived usefulness) for the achievement of higher technological readiness levels (Arcidiacono et al , 2019; Müller et al , 2018). In addition, past research has shown that firms’ innovation is a positive function of its network of new technology providers (Rothaermel and Hess, 2007).…”

Section: Discussionmentioning

confidence: 99%

Internet of things adoption: a typology of projects

Ancarani

Mauro

Legenvre³

et al. 2019

IJOPM

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Purpose The internet of things (IoT) is one of the pillars of Industry 4.0. Prior OM research has conceptualized IoT, and analyzed potential applications and risks and challenges associated with its adoption. However, little empirical evidence exists on the main types of IoT projects undertaken by organizations and on their impacts. The purpose of this paper is to close this gap by searching for a taxonomy of IoT projects that may be associated to different IoT readiness levels. The dynamic capability lens is used as the theoretical background for the analysis. Design/methodology/approach A database of secondary IoT case studies is used to identify an IoT project taxonomy through two-step cluster analysis. The taxonomy obtained allows classifying projects into homogenous groups by technological novelty, IoT capabilities and functional areas of application. ANOVA is then used to test for the association between cluster membership and alternative operational impacts. Finally, the analysis of selected case studies from the database allows throwing light on the nature of the projects typical of each cluster. Findings Five clusters of projects have been identified and positioned along varying degrees of capabilities, novelty and scope. The taxonomy is consistent with a three layer IoT technological readiness model. In turn, the three IoT readiness levels correspond to three managerial capabilities: monitoring, control and optimization. Combining cluster results with detailed case analysis suggests that IoT technological readiness can be interpreted as a dynamic capability enabling knowledge creation that can support competitive advantage. Originality/value This is a first attempt to describe projects firms undertake when adopting IoT. Building on cluster analysis, the study suggests that different IoT readiness levels are needed to reach different impacts.

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Section: Discussionmentioning

confidence: 99%

Internet of things adoption: a typology of projects

Ancarani

Mauro

Legenvre³

et al. 2019

IJOPM

Self Cite

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“…The expert confirmed the appropriateness of conceptualizing three SM stages and contributed to contextualize the relevant technologies to the automotive supply chain. In particular, since automotive suppliers often approach SM with an initial focus on automation, which is considered a low-complexity means to respond to cost pressures in the industry (Arcidiacono et al. , 2019; Dalenogare et al.…”

Section: Methodsmentioning

confidence: 99%

“…, 2013). Despite the strategic importance of SM, a fragmented adoption process is observed, and many firms fail to advance in SM (Arcidiacono et al. , 2019; Lin et al.…”

Section: Introductionmentioning

confidence: 99%

“…SM lies at the core of the Industry 4.0 revolution (Frank et al, 2019) and represents a building block of adaptable systems, which automatically adjust processes to allow for multiple types of products and changing conditions (Kagermann et al, 2013). Despite the strategic importance of SM, a fragmented adoption process is observed, and many firms fail to advance in SM (Arcidiacono et al, 2019;Lin et al, 2018;Raj et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

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The role of absorptive capacity in the adoption of Smart Manufacturing

Arcidiacono

Ancarani

Mauro

et al. 2022

IJOPM

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PurposeSmart Manufacturing (SM) lies at the core of Industry 4.0. Operations management research has identified several factors influencing firms’ ability to adopt SM. However, a clear understanding of capabilities needed to progress in SM is still missing. This paper aims to investigate how absorptive capacity (AC) allows firms to advance in SM and explore how managerial antecedents support the capacity to absorb SM-related knowledge at different stages of SM adoption.Design/methodology/approachThis study adopts an exploratory approach through multiple case studies. Twelve firms, operating as part of the automotive supply chain and exhibiting different stages of SM adoption, constitute the sample.FindingsThe results suggest that advancement in SM requires firms to progressively reinforce their AC. Firms’ ability to acquire and assimilate SM knowledge is supported by managerial antecedents encompassing integrative capacities to bridge old and SM technologies, managerial cognition through the clear alignment of SM technologies with strategic goals and knowledge development capabilities through practices oriented to provide senior managers with SM competences.Originality/valueThe findings contribute to SM research by suggesting that AC is a crucial dynamic capability for SM adoption. The results also provide evidence-grounded recommendations to firms engaged in the digital transformation on the managerial capabilities needed to support AC and to progress from lower to higher stages of SM.

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“…As regards the first -i.e., implementation process characteristicsthe literature has explored drivers and barriers (e.g., Chatzoglou and Michailidou, 2019;Yeh and Chen, 2018;Ghombakhloo, In Press); initial disadvantages of small and medium enterprises (e.g., Horváth and Szabó, 2019;Moeuf et al, 2020;Arcidiacono et al, 2019) and developing countries (e.g., Kamble et al, 2018;Raj et al, 2020); best-practice implementation processes (e.g., Mellor et al, 2014;Svan et al, 2017;Zangiacomi et al, 2020;Tortorella et al, 2020;Veile et al, 2020); ideal maturity stages (e.g., Bibby and Dehe, 2018;Pacchini et al, 2019); and governance modes in specific geographical and institutional contexts (e.g., Reynolds and Yilmaz, 2018;Sung, 2018;Kummitha and Crutzen, 2019;Fukuda, 2020).…”

Section: Industry 40: Concept and Research Issuesmentioning

confidence: 99%

The future of manufacturing: A Delphi-based scenario analysis on Industry 4.0

Culot

Orzes

Sartor

et al. 2020

Technological Forecasting and Social Change

174

103

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Where the Rubber Meets the Road. Industry 4.0 Among SMEs in the Automotive Sector

Cited by 30 publications

References 9 publications

Internet of things adoption: a typology of projects

Internet of things adoption: a typology of projects

The role of absorptive capacity in the adoption of Smart Manufacturing

The future of manufacturing: A Delphi-based scenario analysis on Industry 4.0

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