SI - RESEARCH ROADMAP

As mentioned before, the fourth industrial revolution is fuelled by many different technologies which have a multitude of applications in different areas of society, business, and people. For example, autonomous cyber-physical systems became possible due to advances in the sensor technology and to some extent in the advances in robotics, in order to link the digital to the physical world. For instance, a variety of new transportation devices are available, from small automated guided vehicles in a factory to unmanned cargo aircraft or even robots – unmanned robot-driven cargo vessels. The following are examples of technologies which are driving the fourth industrial revolution (not a complete list):

• Cyber-physical systems: mainly based on advances in sensor technology, the connection between the physical world and the digital world becomes possible, as physical signals are transformed into data.
• Digital twins: Devices can have a “digital twin”, which is a technology allowing to represent the physical object as a digital replica of itself. The digital twin will reproduce all key properties, real-time state, components of the physical object. The digital twin can be used, for instance, to run simulations and to analyse the interactions between different systems involved in a process. A digital twin may also incorporate some form of artificial intelligence and ability to learn and make improvement recommendations based on context/sensor data.
• Blockchain technology: relies on distributed ledger technology, which essentially means that many nodes or even the entire network stores all relevant data, ensuring maximum transparency. A key relevant feature of a blockchain is that many servers along one chain redundantly store all data. Data security and nonrepudiation is increased, because all chain members are able to verify the activities of their peers.
• 3D printing: with additive layer manufacturing physical objects are designed and produced in a decentral fashion by transferring the digital plans to a 3D printing unit which then creates the physical object.
• Artificial intelligence and machine learning are actually not new. They have been around for more than four decades. However only now, due to the availability of huge volumes of data it is becoming increasingly critical to develop techniques that can cope with such volumes while is extracting knowledge from data in (quasi) real time and using it to improve the execution and planning of future and in progress business processes. Furthermore, machine learning is increasingly used to improve the abilities of robots and software agents to take decisions and to interact with/learn from humans.

Smart industry will have a great impact on the types of business models organizations will employ. Many of these changes will be reflected in the way they create value, the type of value they offer to their customers, and the way they interact with other organizations. We consider the following aspects as important business developments relating to Smart industry:

• Global marketplaces: moving from the traditional supply chains to flexibly configured marketplaces which are more suited to serve a new generation of customers, but also to enable stronger and agile collaborations between organizations. New types of marketplaces can emerge which are centred around a sharing platform / economy in which transactions are done via online marketplaces rather than peer-to-peer.
• (Mass) customization: this represents the next evolutionary step in the way organizations offer their products to customers. While personalized offerings with some level of customization have become a standard for many industries, with the help of flexible computer-aided and additive manufacturing systems, organizations are able to combine the low cost of mass production with the intricacies of individual customization.
• Decentralised co-creation networks: refers to a new paradigm in which groups of organizations are no longer organizing themselves with the help of a central entity or control tower, but rather they take part in ecosystems in which functions and responsibilities are distributed. As a result, organizations and cyber-physical systems operating in such ecosystems are expected to function and interact autonomously, while still retaining the same level of coordination, which in some cases manifests itself as emergent behaviour. Additionally, I4.0 facilitates organizations to seamlessly integrate with each other in order to co-create highly customized offerings. It is expected that I4.0 may also lead to completely new business models driven by technological innovations.
• Life-cycle solutions: organizations are moving from offering a product to their customers to offering a series of services and solutions to address a specific customer problem/need. This entails that a customer will no longer purchase or own a product, but rather they would pay for the service of using it. For providers of such solutions/services, this means that they need to maintain and update them throughout their whole lifecycle in order to ensure that they remains relevant for their customers.

Similarly to business, we expect that Smart industry will also have a great impact on multiple aspects of society ranging from the way organizations and individuals behave in relation to waste, to the education of a future generation of workers, to considering societal needs when developing new technology, and in general, to new policies and regulations. We consider the following aspects as important societal developments relating to Smart industry:

• Sustainable economy: the production, distribution, trade and consumption of goods in a manner which promotes reduction of waste and efficient use of resources. Organizations can make use of sustainability-driven management systems and big data analytics in order to ensure the achievement of such efficiency objectives and industrial symbiosis, and the development of a circular economy.
• Policy and regulation: one common aspect for all the Smart industry technologies is that they deal with large amounts of data of organizations and individuals which need to be shared and handled in a responsible, and secure way. Therefore, new policies and regulations need to be developed, together with standardization in areas, such as sustainable production and consumption, and interoperability of systems and organizations within the global value chain.
• Education 4.0: with the introduction of new technologies, processes and practices, the workforce of the future needs to be educated within a fundamentally new type of education system adequately tailored to I4.0 needs, and focusing on shaping professionals  that master 21^st century skills.
• Social inclusion and ethics: observes and discusses risks of excluding (large) groups of people from the gains of the socio-technical transformation. At the same time, it explores the ways to steer this technological transformation in an inclusive way, which helps to anticipate the risks, and challenges, already at an early stage of the technological development.  

As it can already be seen from the previous two focal areas (business and society), the role of people as workers and consumers will be heavily impacted by I4.0. Therefore, changes are expected in the way workers are trained, organized, managed and in their interactions with new technology. We consider the following as important I4.0-related workforce developments:

• Continuous learning and employability: refers to the voluntary and ongoing pursuit of knowledge with the purpose of self-development for personal or professional reasons. It raises the question of how workers are trained in order to prepare them for jobs in Smart industry (on-the-job training vs formal education).
• Self-management and crowd-working: an organizational structure which reduces/removes the need for direct supervision due to the formation of teams which are empowered and able to work autonomously. At the same time, independent individuals may feel the need or prefer to offer their services on digital platforms in the gig economy.
• Management of T-shaped talent: the type of human capital needed by organizations in an era of Smart industry is predictably different than before. The nature of work often requires workers to have a different set of skills, acting as T-shaped professionals, who have boundary-crossing competences next to a deeper functional understanding.
• Technology-enhanced work: one of the distinctive aspects of Smart industry is the way workers are expected to interact with machines. Workers are expected to be enhanced by technology to perform tasks (enabled workers), while others interpret and organize the information provided by technology for decision-making and problem-solving purposes (knowledge engineers).