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Designing industry 4.0 implementation from the initial background and context of companies
Author(s) -
Valérie Rocchi,
Daniel Brissaud
Publication year - 2021
Publication title -
emerald open research
Language(s) - English
Resource type - Journals
ISSN - 2631-3952
DOI - 10.35241/emeraldopenres.14399.2
Subject(s) - digital transformation , context (archaeology) , dimension (graph theory) , key (lock) , thematic analysis , computer science , transformation (genetics) , knowledge management , process management , business , qualitative research , marketing , sociology , world wide web , mathematics , paleontology , social science , biochemistry , chemistry , computer security , gene , pure mathematics , biology
Industry 4.0 is a promising concept that allows industries to meet customers’ demands with flexible and resilient processes, and highly personalised products. This concept is made up of different dimensions. For a long time, innovative digital technology has been thought of as the only dimension to succeed in digital transformation projects. Other dimensions have been identified such as organisation, strategy, and human resources as key while rolling out digital technology in factories. From these findings, researchers have designed industry 4.0 theoretical models and then built readiness models that allow for analysing the gap between the company initial situation and the theoretical model. Nevertheless, this purely deductive approach does not take into consideration a company’s background and context, and eventually favours one single digital transformation model. This article aims at analysing four actual digital transformation projects and demonstrating that the digital transformation’s success or failure depends on the combination of two variables related to a company’s background and context. This research is based on a double approach: deductive and inductive. First, a literature review has been carried out to define industry 4.0 concept and its main dimensions and digital transformation success factors, as well as barriers, have been investigated. Second, a qualitative survey has been designed to study in-depth four actual industry digital transformation projects, their genesis as well as their execution, to analyse the key variables in succeeding or failing. 46 semi-structured interviews were carried out with projects’ members; interviews have been analysed with thematic content analysis. Then, each digital transformation project has been modelled regarding the key variables and analysed with regards to succeeding or failing. Investigated projects have consolidated the models of digital transformation. Finally, nine digital transformation types have been identified.

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