Computers in Industry 118 (2020) 103222 Contents lists available at ScienceDirect Computers in Industry jou rn al hom ep age: www.elsevier.com/locate/compind Estimating Industry 4.0 impact on job proﬁles and skills using text mining S. Fareri a,∗ , G. Fantoni b , F. Chiarello c , E. Coli d , A. Binda e a University of Modena e Reggio Emilia, Giacomo Brodolini Foundation, Marco Biagi Foundation, Italy b Department of Civil and Industrial Engineering, University of Pisa, Italy c Department of Energy Systems, Territory and Construction Engineering, University of Pisa, Italy d Department of Information Engineering, University of Pisa, Italy e Whirlpool Corporation, Italy a r t i c l e i n f o Article history: Received 24 October 2019 Received in revised form 6 February 2020 Accepted 4 March 2020 Keywords: Industry 4.0 Job proﬁles Skills Text Mining Data Mining Case-Study Whirlpool Skill Literature Map Human Resource Management Named Entity Recognition Natural Language Processing Job description Technology 4.0 O*NET a b s t r a c t Industry 4.0 is introducing rapid and epochal changes and challenges. Among these, the issue of skills and job proﬁles is assuming a critical role. In fact, the literature highlights not only the necessary integration of existing skills in professional proﬁles, but also the inevitable creation of new ones to properly manage the digitalisation trends. Although, the state of the art mostly focuses on building models to assess the digital maturity of companies, considering instead the impact on the labor market as a hazy issue. Moreover, the literature tends to offer qualitative approaches to the topic, making the results uncertain; on the other side, quantitative ones tend to be mainly applied on structured databases, while the supply and demand of competences (ﬁndable in CVs, vacancies or ﬁrm’s job proﬁles) are less treated. The goal of the present research is developing a measure for quantifying the readiness of employees belonging to a big ﬁrm with respect to the Industry 4.0 paradigm. To reach the goal, a data-driven approach based on text mining techniques is applied to a case study. In particular the present methodology makes use of a previously developed enriched dictionary of technologies and methods 4.0 (Chiarello et al., 2018). The source is used to analyze job proﬁles’ descriptions belonging to Whirlpool, a multinational company with a structured database of jobs and skills. The process allows the identiﬁcation of technologies, techniques and related skills contained in job descriptions. Starting from these, the Industry 4.0 impact on each job proﬁle is measured. Finally, the metadata of the job proﬁles are analyzed to evaluate to which extent the skills of proﬁles 4.0-ready and non-4.0-ready differ. In the end, the work provides a framework for estimating the Industry 4.0 readiness of enterprises’ human capital which demonstrates to be fast, adaptable and reusable. © 2020 Elsevier B.V. All rights reserved. 1. Introduction Today there is an urgent need to understand which is the impact that technology is having on the workforce. This urgency is given by the paradigm of Industry 4.0: a sociotechnical revolution, hav- ing repercussions on Human Resources as well as on Technological Resources. Industry 4.0 is a systemic transformation in manufactur- ing and economy which is also inﬂuencing society, governance and human identity (Sung, 2018). The new paradigm cannot be merely considered a technological tsunami, but it is made by heteroge- neous elements and may have heterogeneous consequences also from managerial and governmental point of view (Arnold et al., ∗ Corresponding author. E-mail address: silvia.fareri@gmail.com (S. Fareri). 2016; Porter and Heppelmann, 2014). In particular, this impact is affecting multiple groups of stakeholders: companies that have invested in digital innovation in the last 5 years (since the advent of Industry 4.0) are now in the need for an alignment of their internal competencies to maximise the return on investments; labor force feels threatened by robots and Artiﬁcial Intelligence which are suc- ceeding in many new tasks; governments are trying to look to the future of sectors that characterize modern economy; universities are reshaping their offer almost every year. The understanding of changes taking place is increasingly cru- cial for the whole society since a more detailed knowledge of skills requirements helps in designing training programs giving the opportunity to upskill and reskill (European Centre for the Development of Vocational Training (Cedefop, 2019). Despite the great effort of these stakeholders, there exists a lack of tools able to detect the impact that technology is having on speciﬁc https://doi.org/10.1016/j.compind.2020.103222 0166-3615/© 2020 Elsevier B.V. All rights reserved.