Jean-Jacques Pluchart
The digital transformation of the industry involves mobilizing innovative artificial intelligence techniques. These techniques use the Internet of Things (IoT), cloud computing, data exchange, prescriptive analysis, new business models, etc. They apply advanced methods to manage data flows from heterogeneous systems. They aimto achieve greater energy efficiency, more efficient maintenance, protection against breakdowns or intrusions, etc. In principle, they enable better safety, productivity, quality and profitability of industrial systems. They operate on three levels: that of capturing operational data from suppliers and customers; that of connecting stakeholders; that of transforming data into decision-making aids and valuable actions.
AI offers continuous analysis capabilities dedicated to the collection of sensory data, fault diagnosis, flow modelling and the prescription of valid solutions. Current research focuses in particular on the integration of critical maintenance, safety and cybersecurity processes. They strive to improve the performance of systems without compromising their security. Systems engineers must choose the most suitable learning, optimization or prediction methods for the machines’ fields of application. This is particularly the case in the electrical energy sector.
The digital management of the processes of generation, transport, distribution and consumption of energy resources helps to reduce the mechanical inertia of the electricity network and to better ensure the balance of power between production and consumption. AI makes it possible to capture, store and process an increasingly large mass of data in order to make “the network smarter”. In the nuclear industry, AI makes it possible to improve predictive maintenance (by means of vibration sensors, real-time alerts), anti-collision detection and monitoring of sensitive sites.
Among the digital techniques implemented in all industrial sectors, that of digital twins is emerging as a major lever for operational optimization. The digital twin is an interconnected system, powered by data from IoT systems, supervision platforms and simulation software. By building a virtual model of real objects, this technique offers companies increased visibility into their processes, better predictive maintenance and faster development of new products, without impacting production. However, it creates cybersecurity problems, as it reveals the “trade secrets” and “industrial comparative advantages” of innovative companies. It exposes them to espionage, sabotage, manipulation of optimization parameters and/or destruction of critical data. The complexity of digital twins makes them difficult to secure, as they combine heterogeneous software from a variety of vendors, integrating different IoT sensors, AI layers, physical simulators, edge tools and, above all, cloud computing.
In the current context of software between advanced industrial states, these actions constitute major threats to their strategic resources. Thus, the digitization of industrial processes raises questions of national sovereignty that invite public and private decision-makers to extend the European directives on IT security, and in particular, and to adapt the personal data protection regulation (GDPR) to the industrial environment 4.0.
In 2016, the Turgot club chronicled one of the first works devoted to the birth of “Industry 4.0 “.
Kohler D., Weisz J-D. (2016), Ambition industrie 4.0. The challenges of the digital transformation of the German industrial model, Eds Eyrolles.
Since the 1990s, German industry has been engaged in a “cobotics” or collaborative robotics approach combining robotics, mechanics, electronics and cognitive sciences to assist the operator of a machine. Since the 2000s, it has also initiated a process of “globotics” or globalization of resources thanks to AI. The latter makes it possible to shorten value creation chains and decision-making circuits within organizations and their ecosystems, but it also accelerates the phenomenon of job relocation in laboratories, offshore factories or call centres. It also promotes the emergence of new forms of open organizational innovation based on free software, co-working and distance working, in principle more agile and less expensive, which extend from research and development (living labs, fablabs, etc.) to cooperative production (digital micro-manufacturing, do-it-yourself, maker spaces, etc.), and collaborative consumption (peer-to-peer accommodation, car sharing, etc.).