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This paper investigates the effects of automation and offshoring on the dynamics of the occupational distribution of employment with a focus on Western Europe between 2000 and 2016. I use a general equilibrium model with three regions, three types of workers, ICT capital, trade in final goods and endogenous offshoring. Fed with exogenous measures of ICT-capital prices and trade costs, the model replicates key features of the data. It matches the observed dynamics of offshoring to Eastern Europe and Asian countries. It also reproduces accurately the observed polarization of the labor market: abstract and manual labor increase while routine labor falls. A counterfactual experiment reveals that automation is the main driver of polarization. Since it is also the only factor that drives individuals to become abstract (highskill) workers, it is welfare enhancing. The effects of falling trade costs on labor polarization are smaller, but imply welfare gains.

Industry 4.0 is a term first introduced by the German government during the Hannover Messe fair in 2011 when it launched an initiative to support German industry in tackling future challenges. It refers to the 4th industrial revolution in which disruptive digital technologies, such as the Internet of Things (IoT), Internet of Everything (IoE), robotics, virtual reality (VR), and artificial intelligence (AI), are impacting industrial production. The new industrial paradigms of Industry 4.0 demand a socio-technical evolution of the human role in production systems, in which all working activities of the value chain will be performed with smart approaches. However, the automation of processes can have unpredictable effects. Nowadays, in a smart factory, the role of human operators is often only to control and supervise the automated processes. This new condition of workers brought forth a paradox: malfunctions or irregularities in the automated production process are rare but challenging. This article discusses the challenges and risks that the 4th industrial revolution is bringing to society. It introduces the concept of the Irony of Automation. This propounds that the more reliable an automated system, the less human operators have to do and, consequently, the less attention they pay to the system while it is operating. The authors go on to discuss the human-centered approach to automation, whose purpose is not necessarily to automate previously manual functions but, rather, to enhance user effectiveness and reduce errors.

International audience; Many software systems today provide support for adaptation and reconfiguration at runtime, in response to changes in their environment. Such adaptive systems are designed to run continuously and may not be shut down for reconfiguration or maintenance tasks. The variability of such systems has to be explicitly managed, together with mechanisms that control their runtime adaptation and reconfiguration. Dynamic software product lines (DSPLs) can help to achieve this. However, dealing with evolution is particularly challenging in a DSPL, as changes made at run-time can easily lead to inconsistencies. This paper describes the challenges of evolving DSPLs using an example cyber-physical system for home automation. We discuss the shortcomings of existing work and present a reference architecture to support DSPL evolution. To demonstrate its feasibility and flexibility, we implemented the proposed reference architecture for two different DSPLs: the aforementioned cyber-physical system, which uses feature models to describe its variability, and a runtime monitoring infrastructure, which is based on decision models. To assess the industrial applicability of our approach, we also implemented the reference architecture for a real-world DSPL, an automation software system for injection molding machines. Our results provide evidence on the flexibility, performance and industrial applicability of our approach.