Keywords

1 Introduction

In the coming years, exponential growth in connected IoT devices is expected. It will include consumer, industrial and healthcare applications, with a market forecast of between $4 and $11 trillion by 2025, around 1600 billion in Industrial IoT (Source McKinsey). Machines will be increasingly pervasive, intelligent, connected, and equipped with forms of distributed intelligence that will interface with users.

Ongoing technological changes call for the definition of socially sustainable digital innovation pathways make technology work at the service of humans (workers, citizens, students, or else) and for the development of a human-centred society. According to a 2018 study by the McKinsey Global Institute, in urban contexts, digitization has contributed to raising various quality-of-life indicators from 10 to 30%, in areas ranging from transport and healthcare to the reduction of the pollution generated by manufacturing.

In particular, according to a recent Microsoft/IDC study, about 85% of jobs are expected to undergo a transformation in the next 3 years, 33% of workers will have to engage in re-skilling, 26% will take on new roles, 27% of works will be outsourced or automated.Footnote 1 Skills will undergo essential changes accordingly. A ranking of the skills necessary to use artificial intelligence shows that technical, cognitive, process and social skills will need improvement. New future professions include, among others, data scientists, data engineers, data analysts and the like.Footnote 2

The new document of the European Commission on Industry 5.0 focuses on the crucial role of people, in industrial contexts, on data, information and knowledge management. Industry 5.0 presents a business model based on cooperation between machines and humans, which implies rethinking and innovating models and tools for managing information, as the current ones are often inadequate to deal with the complexity of present socio-technological environments. Particularly so in fact, as the evolution of information technology has made new developments possible.

In the new scenarios, the debate on the role of people in factories is spurred by a wealth of new ideas. This makes it increasingly urgent to study new models that can enable people to improve their work and have a leading role in the evolution of production processes and in the introduction of systems for the exploitation of artificial intelligence. All these studies have to consider the diversities of the workers’ cognitive and physical skills. Artificial intelligence and future technologies will increase the workers’ skills and knowledge, providing physical help in dealing with the heaviest and riskiest jobs and decision-making support.

However, knowledge generated during the job (particularly tacit knowledge) must be mapped before it can be re-used in the company in a way that meets the privacy requirements of the factory and the individuals. All this requires an intensive effort from both a technological and an organizational point of view.

The continuous introduction of new technologies and management practices calls for new skills and knowledge that should be developed through training for people wishing to enter the job market and through continuous education programs for those already in employment.

This action line aims to design and develop new solutions to enhance individuals and their skills, thus contributing to their satisfaction and well-being. Innovative factories should be increasingly inclusive and firmly focused on the involvement and participation of people (users, operators, managers). These models should be human-centric. Humans run and control technologies and, in general, all the dimensions through which the new factory is defined.

The most relevant challenges certainly include the creation of safe and comfortable workplaces capable of generating positive emotions in the people involved in the production, and workstations that reduce the workers’ physical and cognitive effort and help them move on to activities with greater value added. In the new factories, the design of spaces, workstations, new facilities architectures, and new models for the safe interaction between humans and machines will become central aspects. Particular attention will be paid to improving human interaction with an environment populated by various technologies/systems such as robots, machinery, interfaces, and automation systems.

The study and experimentation of new technologies for reducing physical effort can be sparked by exploiting the potential of wearable devices and exoskeletons. The cognitive effort could be eased by intelligible and straightforward information, profiled according to the worker’s needs.

Similarly, the role of workers is shifting from traditional repetitive to higher-level tasks thanks to the cooperation with advanced support systems, collaborative robots, and AI-based technologies. On the one hand, the objective will be to provide all the material and cognitive support tools that can improve workers’ skills and knowledge and, on the other, generate new training procedures and tools to help workers be up-to-date and keep step with the changes in manufacturing processes.

Finally, the knowledge generated on the job, particularly tacit knowledge, will be mapped and reused within the company to be compatible with individual privacy. This will bring benefits both in terms of the well-being of people, whether users, operator or managers, and in terms of company policies and procedures.

Expected impact: increasing workplace safety and well-being; reducing psycho-physical stress; increasing qualification opportunities and promoting the personal enhancement of workers, preserving them from exclusion and downgrading; improving the transparency of the algorithms that regulate the operation of digital platforms; improving the capacity of companies and workers to exploit the advantages of the data economy to ensure, in compliance with ethical-legal limitations and individual freedom, the achievement of collective goals in terms of the workers’ health and well-being in the factory.

The research and innovation priorities of the strategic action line on Factories for humans are (Fig. 1):

Fig. 1
figure 1

Strategic Action Line 3—Factories for humans

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  • PRI3.1—New Technologies, Methods and Tools to optimize the work environment, human–machine interactions and cognitive load

  • PRI3.2—New Technologies and Methods for the management of Knowledge, Information, Privacy and the Company’s Human Capital

  • PRI3.3—New Technologies and Methods for the training and certification of the skills and professionalism of persons and of the human capital in the Life Long Learning (LLL) era

2 PRI3.1 New Technologies, Methods and Tools for Optimizing the Work Environment, Human–machine Interactions and Cognitive Load

People play a central role in designing intelligent factories and, above all, in generating their flexibility and “resilience”. Factories can thus adapt to new and not entirely predictable contexts and reconfigure promptly and efficient according to new work patterns and production models.

This research and innovation priority proposes to design new technologies and methods for managing human resources as a critical element in the new concept of “resilient factory”. In particular, this priority proposes objectives connected with ergonomics, human–machine interaction, and the person’s cognitive load, promoting new ways of working and communicating (remote work, collaboration with automation systems, and management of production re-purposing).

The proposal is to develop methods, models, and systems to support operators in their work, increase their physical and cognitive abilities, improve work quality and productivity, and reduce margins of error while ensuring the workers’ general well-being. As a result, safety and well-being in the workplace will be increased by acting on numerous factors: an adequate redesign of the type and sequence of activities during work shifts, better organization of the premises, information display mode optimized according to needs and activities, natural and intuitive interactions between humans and machines). This will reduce psycho-physical stress, mental and psychological fatigue, anxiety, and overload.

One of the fundamental themes for the future will be to guarantee a continuous and rapid evolution of production systems to seize new opportunities and to face ongoing changes. Humans are at the centre of a constant redevelopment that requires analytical skills, goal identification, and creation of solutions and planning of pathways to achieve the desired results. This shift has to take place at all levels, from the local improvement of individual workstations, geared to increase efficiency and respond to new needs in terms of products and materials, seizing new opportunities in terms of tools, sensors and plants, to factory networks, supply chains and interaction with the markets. To be effective, this change must be pervasive and see the joint effort of all the people who take part in this evolution at different levels. The ability to manage this evolution, rather than the optimization of individual situations, will in all likelihood be the real competitive lever in a rapidly and radically changing context such as the current one. Humans are the critical resource, as they can use their intelligence and dedication to ensure high performance and competitiveness for their company. In this perspective, it is important to underline that people must be put in a position of dominating changes. The rapid and continuous evolution of technological products and production systems should parallel the ongoing and targeted development of the knowledge and skills of the people who cooperate to manage change at different levels.

In particular, the goals of this research and innovation priority concern:

  • New materials and tools for workstation ergonomics and safety: development of active and passive PPE (Personal Protective Equipment) including materials that absorb high levels of thermal and mechanical energy, safety devices, wearable sensors, smart work clothes, workers’ supports for repetitive tasks, intelligent exoskeletons for workers’ safety, digitization of workers and work environment so that the worker’s operations can be designed in a human-centred perspective and complex tasks can become more accessible.

  • New technologies for customization of work environments: development of intelligent solutions designed to adapt workstations to the specific physical and cognitive skills of individual workers, considering their role, duties, abilities and needs; developments of intelligent solutions designed to recognize operators and to reconfigure the working environment based on working conditions.

  • Methods and technologies for the management of the human–machine interaction: development of natural and intuitive interaction models, new human–machine interaction paradigms, new methods and tools for the analysis of workplace interactions, new human–robot cooperation and collaboration models, natural communication languages, natural and transparent intelligent adaptive interfaces, including environmental, wearable and multi-modal types (immersive interfaces with Virtual and Augmented Reality, Brain Computer Interfaces, etc.).

  • Methodologies and technologies for optimizing the cognitive load: development of work environment design models and advanced interfaces capable of supporting control activities and decision-making; structured approaches for the measurement of mental stress in the industrial environment, intelligent systems (based on Deep Learning, Digital Twin, Artificial Intelligence, GANs networks, etc.) to help optimizing mental load, avoiding both overload and underload.

  • Tools to bolster adaptation to continuous changes in production: development of intelligent systems that can predict the operator’s actions and provide online support on Virtual and Augmented Reality (operations to be carried out, procedures, activity tracking, etc.), Virtual Training systems and immersive, augmented or mixed Virtual Reality tools, that can help to improve staff safety by providing remote training on how to deal with risky situations.

Interaction with Other Action Lines

This research and innovation priority is transversal and will have strong interaction with action lines LI5 and LI6.

  • Human–machine collaboration and integration in innovative manufacturing (LI5) will increase human capabilities rather than replace them, allowing humans to focus on higher value-added, creative and socially relevant activities.

  • Human–machine interaction will be studied in close connection with Strategic action line LI6. The aim will be to provide guidelines for designing collaborative human–robot systems within evolutionary and resilient manufacturing systems geared to enhance people’s work and gain by the workers’ experience.

Time Horizon

Short-Term Goals (2–3 years):

  • Materials and tools to improve workplace ergonomics and safety,

  • Models for the management of the human–machine interaction geared to enhance the work of people in factories,

  • Models for designing working environments and advanced interfaces that can provide support for control and decision-making,

  • Digital models of workers to be deployed in planning their operations and designing the means to facilitate complex tasks.

Medium-Term Goals (4–6 years):

  • Technologies for the customization of work environments,

  • Intelligent solutions capable of adapting machines to operators, detecting the presence of DPIs and checking in real time that the correct procedures are being carried out,

  • Technologies for the management of the human–machine interaction,

  • Technologies for the optimization of cognitive load

Long-term goals (7–10 years):

  • Tools to bolster the adaptation to continuous changes in production processes (intelligent systems capable of predicting the operator’s actions and offering online support, and advanced Virtual Training systems to train operators to deal with changes)

3 PRI3.2 New Approaches to the Management of Corporate Knowledge, Privacy and Human Capital

Knowledge, creativity and the human capability to cope with unforeseen events play a fundamental role in generating innovation and managing production. Competitiveness involves acquiring awareness of these aspects and enhancing the human component and its ongoing development. As machines become increasingly intelligent and connected (Smart Machines), new business models will lead the way from a product economy to a product-service economy (Servitization) also in manufacturing. Prognostics and remote configuration of the new “connected products” will be the basis for this change. Knowledge is increasingly important and remotely available thanks to new digital tools.

Furthermore, emerging production models provide for the pervasive introduction of AI-based tools, which involves the creation of knowledge management and decision-making support tools, as well as smart machines that work together with humans. In this scenario, it is necessary to keep the focus on the central role and skills of people means promoting the trustworthiness and explainability of AI methodologies, with a view to ensuring that AI-based systems comply with the ethical principles of respect for people, prevention of damage, fairness and explicability.

The research and innovation priority must therefore address the following objectives:

  • Methods for managing information, privacy of people and factories: Define rules and methodologies to collect, manage, harmonize and share information related to workers and factories. They should ensure an appropriate acquisition and use of data with a particular focus on sensitive data Privacy and Security. This area includes: CyberSecurity (i.e. IT equipment safety and security), anonymization of data both at data generation and data processing level, certification of data from its generation all along the management and analysis process chains. The issue of the workers’ self-sovereignty with regard to their digital identities (Self Sovreign Identity) is also very important.

  • Technologies and methodologies for the management of corporate knowledge: It is necessary to develop new tools for the collection, integration and harmonization of knowledge generated by people and databases, with a view to ensuring that such knowledge can be reused and optimized. In particular, data should be made accessible, available and easily integrated through new methods in support of data analysis and event prediction. Specific consideration should also be given to systems for the management of information useful in carrying out the manufacturing and decision-making processes assigned to the workers. Structured and shared knowledge will help workers be more productive, sharing experiences that will help them find easier, quicker and cheaper solutions to problems, contributing to their personal development and training in the specific field. In particular, this goal requires actions to be deployed along three lines:

  • Knowledge capture: Defining “Human Computation” methodologies and technologies to transform tacit into explicit knowledge by actively involving people in processing information, putting users back at the centre of the process (human-in-the-loop), to collect, improve and preserve the quality and reliability of knowledge. This will make it possible to create and enhance a company’s living asset capable of generating systems that can be used by workers for their self-improvement, favouring a natural transition from a behavioural to a cognitivist perspective on skills. Knowledge capture systems will also make it possible to valorise intangible assets through appropriate measurement systems, also for valorising them in financial statement.

  • Knowledge Analysis & management: Developing a new generation of methodologies and application tools capable of combining the analysis of captured and structured knowledge with the management of information, whether from human sources or machine interfaces. The aim is to support operations and decision-making processes, by defining conceptual reference models, sharing and managing assets according to FAIR (Findable, Accessible, Interoperable, Reusable) principles. Throughout this process, it is essential that formalized knowledge is communicated in understandable formats to the operators so that new knowledge can constantly be built on existing one and the operator can maintain control of this evolving situation.

  • Knowledge transfer and sharing: Once become a corporate asset, knowledge can be shared, exploited and preserved within the company. Thus it is necessary to develop systems for sharing information and data among people and manufacturing units within the factory with the aim to create value for the workers, for instance through re-skilling and coaching sessions conducted by experienced people for recruitment of new resources and field support. There is a need for Human–Machine Knowledge Transfer mechanisms (Artificial Neural Networks, ANNs) and collaborative robots (Cobots) that operate taking into account human behaviour, using artificial intelligences based on Explainable & Trustworthy AI. As a result, ANNs and cobots will be assigned to small value added activities, and to facilitate workers in their daily actions and support them in their decisions, by providing them with the necessary decision-making elements. It is expected that in these systems, the “human factor” will be at the centre of the decision-making process.

  • Systems for the management of Knowledge IP: it is essential to define methods, infrastructures, guidelines and application tools to ensure that the ownership of knowledge is available and valued, as both a corporate and a personal asset, in a perspective of trust and enhancement of the ideas of those who generated them and of those who provided the tools to generate them.

Interaction with Other Strategic Action Lines

  • Data protection and workers’ privacy issues need to be addressed in close relation with the actions carried out in Strategic action line LI7 regarding cyber-security.

  • With reference to LI6, digitization/digital twin issues will be useful in defining the tools for sharing and managing knowledge within the digital factory.

Time Horizon

Short-term goals (2–3 years):

  • Knowledge: the digitization of the working experience of operators (mapping skills and digitizing them to create value for the company) is a key issue and will lead to the creation of a shared re-skilling knowledge base for the rapid recruitment of new resources. To date, these issues are regarded by companies as interesting and certain initiatives are underway, but no structured solutions are as yet available. For this reason, the goals in this area are defined in the short term of 2 to 3 years, to start raising awareness and promote the creation of solutions to channel these needs. More complex solutions, for instance based on Explainable and Trustworthy AI, should be set in motion promptly to avoid losing ground to competition from other markets. However, their full integration will only be achieved through medium-long term actions (1–5 years)

  • Information & Privacy: data privacy, security and Self Sovreign Identity are certainly extremely engaging issues for both companies and workers, and should be addressed in the short term.

Medium-term goals (4–6 years):

  • Advanced Interactions: The definition and development of IoT, AR and smart wearable technologies, for monitoring and supporting workers, can be addressed through actions in the short-medium term.

Long-Term Goals (7–10 years):

  • Advanced Interactions: Natural multi-modal interfaces, capable of allowing intelligent and self-adaptive interaction with the industrial environment and robots, and convergence of AI, CBI and Exoskeleton systems should instead be addressed in the medium-long term, given the complexity of the context.

4 PRI3.3 New Technologies and Methods to train and certify the skills and competence of individuals and of a company’s human capital in the era of Life Long Learning (LLL)

The constant introduction of new technologies and new management practices in companies is creating a lack of coordination between demand for new professional profiles, the qualification of operators active on the labour market. In Italy, the difference between supply and demand amounts to more than 150,000 jobs available within companies that are not matched on the labour market (2019 data).

In addition, the knowledge acquired by the current workforce during their educational career becomes obsolete quickly and continuous training is required to generate added value for the company. Accordingly, there is a demand for new skills and knowledge in the offer of training programs for people entering the job market, and of updating programs for those already in employment.

From a company’s point of view, it becomes essential to map the workforce’s competence and skills to understand proactively the impact of a new technology, the timeframe to its full exploitation as added value and the most effective training methods.

From an individual’s point of view, the need is for virtual and tangible tools and environments to be constantly available, to ensure a continuous education and the certification of the skills and knowledge acquired, in a Life Long Learning (LLL) perspective. New teaching frames must be used to increase the power of attraction of the manufacturing world and the efficacy of training.

New tools should be developed also to provide user support functions, to manage knowledge and training for classes of both younger and more experienced users, to develop/improve high-profile skills in manufacturing.

Finally, it is essential to develop tools to assess the work carried out and test the skills and abilities acquired, so that the new strategies and technologies introduced can be properly validated from a corporate point of view, the effects of the training received can be assessed, and objective tools can be provided for the certification of the skills and abilities acquired on the job.

The research and innovation priority must therefore address the following objectives:

  1. 1.

    New methods and tools for active training: the availability of new hardware devices such as smartphones, tablets, smart glasses, combined with the prompt availability of protocols with enormous bitrates (5G) and the use of software such as social media and APPs pave the way to the interaction with digital twins of machinery, lines and production environments. The focus is on strengthening MOOCs (Massive Open Online Courses) and experiential tools such as Mixed, Augmented and Virtual Reality, alongside innovative methodologies and new educational models such as “serious games”, to further enable training both in presence and at a distance.

  2. 2.

    Physical environments for the education and training: Development of “protected” physical environments where people can be educated and trained on new technologies, and pilot and demo work environments (Teaching & Learning Factories) which can also be used remotely. An environment of this type should facilitate training in value-added manufacturing, providing workers with interdisciplinary skills, preparing them to integrate new technologies and empowering them with the capability of combining knowledge. The goal is to design and plan “hands on” training courses, in which workers can directly experience technologies. This will guarantee an immediate return from the prompt redeployment of such technologies in the factories.

  3. 3.

    Methods and tools for assessing the acquired skills and abilities: Development of automatic systems for the assessment of the operative skills achieved and required for the performance of the various tasks assigned. Methods and tools to track the impact of education and training in terms of improving and increasing the efficiency of production systems.

  4. 4.

    Methods and tools for planning staff development paths: development of systems that make it possible to plan an evolution of products, processes and manufacturing systems in parallel with the development and acquisition of skills by the staff. This opens the way to the adoption of planning models that focus on improving knowledge and skills through exchanges between operators with different experiences. Such models need to hold into account the knowledge’s evolution timing and connect it to the systems’ evolution timing.

Interaction with Other Strategic Action Lines

  • With reference to LI6, digitization/digital twin’s issues will be useful in defining tools for sharing and managing knowledge within the digital factory.

Time Horizon

Short-term goal (2–3 years):

  • New serious-game methods and tools for active training

  • Physical environments for the education and training of people

  • Methods and tools for planning staff growth paths

Medium-term goal (4–6 years):

  • New methods and tools for active training based on virtual reality and multisensory approaches

  • Methods and tools for assessing the skills and abilities acquired